# The radial acceleration relation in a CDM universe

### Transcript of The radial acceleration relation in a CDM universe

MNRAS 000, 1–20 (0000) Preprint 26 July 2021 Compiled using MNRAS LATEX style file v3.0

The radial acceleration relation in a ΛCDM universe

Aseem Paranjape1? & Ravi K. Sheth2,3†,1 Inter-University Centre for Astronomy & Astrophysics, Ganeshkhind, Post Bag 4, Pune 411007, India2 Center for Particle Cosmology, University of Pennsylvania, 209 S. 33rd St., Philadelphia, PA 19104, USA3 The Abdus Salam International Center for Theoretical Physics, Strada Costiera, 11, Trieste 34151, Italy

26 July 2021

ABSTRACTWe study the radial acceleration relation (RAR) between the total (atot) and baryonic(abary) centripetal acceleration profiles of central galaxies in the cold dark matter(CDM) paradigm. We analytically show that the RAR is intimately connected withthe physics of the quasi-adiabatic relaxation of dark matter in the presence of baryonsin deep potential wells. This cleanly demonstrates how the mean RAR and its scatteremerge in the low-acceleration regime (10−12 m s−2 . abary . 10−10 m s−2) froman interplay between baryonic feedback processes and the distribution of CDM indark halos. Our framework allows us to go further and study both higher and loweraccelerations in detail, using analytical approximations and a realistic mock catalogof ∼ 342, 000 low-redshift central galaxies with Mr ≤ −19. We show that, while theRAR in the baryon-dominated, high-acceleration regime (abary & 10−10 m s−2) is verysensitive to details of the relaxation physics, a simple ‘baryonification’ prescriptionmatching the relaxation results of hydrodynamical CDM simulations is remarkablysuccessful in reproducing the observed RAR without any tuning. And in the (currentlyunobserved) ultra-low-acceleration regime (abary . 10−12 m s−2), the RAR is sensitiveto the abundance of diffuse gas in the halo outskirts, with our default model predictinga distinctive break from a simple power-law-like relation for Hi-deficient, diffuse gas-richcentrals. Our mocks also show that the RAR provides more robust, testable predictionsof the ΛCDM paradigm at galactic scales, with implications for alternative gravitytheories, than the baryonic Tully-Fisher relation.

Key words: galaxies: formation - cosmology: theory, dark matter - methods: analytical,numerical

1 INTRODUCTION

Gravitational interactions at galactic scales offer a fertile test-ing ground for competing theories of gravitation. The highlysuccessful Lambda-cold dark matter (ΛCDM) paradigm at-tributes all gravitational interactions at these scales to theNewtonian limit of general relativity, but postulates the ex-istence of a collisionless (or dark) matter component thatpervades the cosmos (for a recent review, see Salucci 2019).In stark contrast, alternative proposals such as ModifiedNewtonian Dynamics (MOND, Milgrom 1983) attempt toexplain extra-Galactic observations, particularly galactic ro-tation curves, using Standard Model physics alone (i.e., with-out a dark component), but alter the nature of gravity atthese scales. MOND, in particular, postulates a new, fun-damental acceleration scale a0 ∼ 10−10 m s−2 to segregatethe high-acceleration regime of Newtonian dynamics fromthe low-acceleration regime where the nature of gravity ismodified. MOND is just one of a growing number of modifiedgravity models (for a recent review, see Bertone & Tait 2018).

? E-mail: [email protected]† E-mail: [email protected]

Observationally, such competing ideas are potentiallyamenable to testing using empirical correlations betweenthe dynamical, gravitating mass of a system and the lightwe observe from it. Among the several such mass-to-lightscalings that are known to exist for galaxies of different types(Faber & Jackson 1976; Tully & Fisher 1977; McGaugh et al.2000), the ‘radial acceleration relation’ (RAR, McGaugh et al.2016) has recently emerged as an intriguing new potentialtest of gravity.

The RAR is usually expressed as the relation between thecentripetal acceleration profile atot(r) due to all gravitatingcomponents (in ΛCDM, these would be baryonic and darkmatter), and the Newtonian contribution abary(r) to thisprofile from the baryonic components alone. In terms of thegalactic rotation curve vrot(r) and its baryonic contributionvbary(r) (these will be defined below), we have

atot(r) = v2rot(r)/r , (1)

and

abary(r) = v2bary(r)/r . (2)

The RAR and its close cousin, the baryonic Tully-Fisher re-lation (BTFR, McGaugh et al. 2000), have been extensively

© 0000 The Authors

arX

iv:2

102.

1311

6v2

[as

tro-

ph.G

A]

23

Jul 2

021

2 Paranjape & Sheth

discussed in the literature, especially in the context of MONDversus ΛCDM (see, e.g., the review by McGaugh 2015, seealso below) In the ΛCDM framework, unlike MOND, thereis no fundamental acceleration scale. Correlations such asthe RAR and BTFR, to the extent that they are predictedby ΛCDM, are necessarily emergent phenomena that resultfrom a complex combination of many underlying correlations.The fact that the observed BTFR and especially the RARhave low scatter, makes it very interesting to ask how theemergence of these relations in ΛCDM fares against observa-tions (see, e.g., Courteau et al. 2007, for a discussion of theconstraints on physical models of the Tully-Fisher relation).Several studies have followed this line of reasoning and usedhydrodynamical CDM simulations of, both, small samplesof objects as well as cosmological volumes, to quantify theBTFR and RAR expected in ΛCDM (e.g., Sorce & Guo 2016;Sales et al. 2017; Keller & Wadsley 2017; Ludlow et al. 2017;Tenneti et al. 2018; Garaldi et al. 2018).

Focusing on the RAR (we discuss the BTFR separatelylater), a general trend is that most hydrodymanical CDMsimulations that broadly reproduce observed galaxy prop-erties do, in fact, also naturally produce a tight RAR (e.g.,Keller & Wadsley 2017, although see Milgrom 2016). How-ever, the details of the median trend and the scatter aroundit do not always agree with the observed ones (e.g., Ludlowet al. 2017; Tenneti et al. 2018), and it is usually difficult toassess whether the differences are fundamental (e.g., due tospecifics of baryonic feedback physics) or caused by widelydifferent sample definitions and other technical choices inmeasuring rotation curves. For example, the EAGLE simula-tions produce an RAR similar to the observed one but withan inferred acceleration scale a0 higher by about a factor 2(Ludlow et al. 2017), while the RAR in the MassiveBlack-IIsimulation is closer to a power law with no intrinsic accelera-tion scale (Tenneti et al. 2018).

Several authors have attempted to build an analyti-cal understanding of the RAR in a ΛCDM universe (seevan den Bosch & Dalcanton 2000, for early work). Wheeleret al. (2019) have argued that the RAR is a simple algebraicoutcome of the BTFR, although they do not address theemergence of the BTFR itself. Grudic et al. (2020) haveattempted to explain the emergence of a characteristic accel-eration scale from the physics of stellar feedback, expressinga0 using fundamental constants. The emergence of the RARand related scalings in ΛCDM is, in general, easier to appreci-ate using empirical models to connect dark matter to baryons,along with (semi-)analytical modelling for producing rotationcurves. This approach has been adopted by several authorsrecently using the subhalo abundance matching (SHAM)technique (e.g., Desmond & Wechsler 2015; Desmond 2017;Navarro et al. 2017). A common thread in these studies isthat the ΛCDM RAR is a complicated but natural outcomeof a combination of the SHAM association of stellar mass todark halos, the requirement that galaxy disk sizes obey theobservationally constrained scaling with halo properties, andthe magnitude of the ‘backreaction’ of the baryonic materialon the dark matter profile in the inner halo.

In this work, we present new analytical insights into thestructure of the RAR, and the underlying physics that deter-mines this structure, in the ΛCDM paradigm. Specifically,we show that the physics of quasi-adiabatic relaxation of thedark matter profile in the presence of baryons, particularlyin the inner, baryon-dominated regions of the halo, plays akey role in establishing both the median and scatter of the

RAR for any galaxy sample. Although previous work (e.g.,Desmond 2017) has noticed the relevance of this relaxationphysics to the RAR, its full impact on the RAR has not beenappreciated to date (e.g., Navarro et al. 2017, discuss theRAR in the absence of any baryonic effect on the dark mat-ter). We believe this is largely due to the common practiceof expressing the RAR as the functional dependence of atoton abary (e.g., McGaugh et al. 2016; Lelli et al. 2017; Keller& Wadsley 2017; Ludlow et al. 2017; Navarro et al. 2017;Desmond 2017; Tenneti et al. 2018; Di Paolo et al. 2019;Tian et al. 2020), which can easily mask small but significantdifferences between alternative physical models in the pre-dicted approach of atot → abary at large abary. As argued byChae et al. (2019), the baryon-dominated, high-accelerationregime (abary & 10−10 m s−2) of the RAR is better probedby expressing the quantity

∆a ≡ atot/abary − 1 , (3)

as a function of abary. In the language of McGaugh (1999),∆a can be thought of as a ‘residual mass discrepancy’. Weexclusively use this formulation of the RAR in the presentwork.

We augment our analytical calculations with measure-ments of the RAR in a mock galaxy catalog containing acosmologically representative sample of central galaxies withrealistic baryonic properties, including stellar mass and coldas well as hot gas, along with their spatial distributions. Thismock is based on the algorithm recently presented by Paran-jape et al. (2021) and is described below. The use of mockgalaxies with numerically sampled rotation curves allows usto extensively explore the sensitivity of the RAR to changesnot only in the underlying physics and baryon-dark matterscalings, but also to effects of sample selection and othertechnical aspects of rotation curve estimation. Our primarygoal is to emphasize and disentangle conceptual issues, ratherthan perform a detailed comparison with observations. Wetherefore ignore observational errors and focus on the intrin-sic predictions that follow from our analytical argumentsand mock catalogs. As such, we deal only with ‘perfectlymeasured’ rotation curves in this work (see Desmond 2017,for more careful comparisons with observed data sets).

The paper is organized as follows. In section 2, we brieflydescribe the numerical algorithm and N -body simulation boxunderlying the mock galaxy catalog we use in this work. Insection 3, we present analytical calculations that show howany prescription for quasi-adiabatic relaxation and the associ-ated baryon-dark matter scalings (section 3.1) leads directlyto a prediction for the RAR of each individual galaxy, andhence of any population of galaxies (section 3.2). Appendix Abuilds on these analytical results to construct an approxi-mate but fully analytic RAR which allows us to predict theshape and tightness of the RAR in various limits. In sec-tion 4, we explore the RAR of our mock galaxies for variouschoices of relaxation physics prescription, sample selection,baryon-dark matter scaling, and technical details such asrotation curve sampling. This exercise allows us to put allour analytical arguments to the test. In section 5, we discussin detail the predictions of our mocks for the BTFR, high-lighting the pitfalls of over-interpreting BTFR measurementswhich, unlike the RAR, are inherently unstable to variationsin technical details of the analysis. We conclude in section 6.

Throughout, mvir and Rvir refer to the total halo massand virial radius. In keeping with the literature on quasi-adiabatic relaxation, on which we rely heavily, we defineRvir ≡ R200c, the radius at which the enclosed halo-centric

MNRAS 000, 1–20 (0000)

RAR in LCDM 3

density becomes 200 times the critical density ρcrit of theUniverse, so that mvir = (4π/3)R3

vir × 200ρcrit. All our re-sults assume a spatially flat ΛCDM background cosmol-ogy, with parameters Ωm,Ωb, h, ns, σ8 given by 0.276,0.045, 0.7, 0.961, 0.811, compatible with the 7-year resultsof the Wilkinson Microwave Anisotropy Probe experiment(WMAP7, Komatsu et al. 2011). We will denote the base-10(natural) logarithm as log (ln).

2 MOCK CATALOGS

Our results are based on a mock galaxy catalog constructedusing the algorithm described in detail by Paranjape et al.(2021, hereafter, PCS21). Below, we briefly summarise thisalgorithm and the N -body simulation that is populatedwith mock galaxies, followed by a discussion of the baryoniccomponents and associated rotation curve of each mockcentral galaxy.

2.1 Simulation and mock algorithm

We use one realisation of the L300 N1024 simulation configu-rations discussed by PCS21. This is a gravity-only simulationwith 10243 particles in a (300h−1Mpc)3 cubic box, performedusing the code gadget-2 (Springel 2005)1 with halos identi-fied using the code rockstar (Behroozi et al. 2013).2 Furtherdetails of the simulation can be found in Paranjape & Alam(2020).

The PCS21 algorithm, which is based on the halo occu-pation distribution (HOD) models calibrated by Paul et al.(2018) and Paul et al. (2019), populates host halos in thisbox with mock central and satellite galaxies, producing aluminosity-complete sample of galaxies with an r-band ab-solute magnitude threshold Mr ≤ −19. In addition to ther-band magnitude, each mock galaxy is assigned realistic val-ues of g−r and u−r colours and stellar mass m∗. A fractionof these galaxies is also assigned non-zero values of neutralhydrogen (Hi) mass mHi. The HOD models underlying thisalgorithm are constrained by the observed abundances andclustering of optically selected galaxies in the Sloan DigitalSky Survey (SDSS, York et al. 2000),3 and of Hi-selectedgalaxies in the ALFALFA survey (Giovanelli et al. 2005).PCS21 presented extensive tests of the algorithm, along witha detailed discussion of cross-correlation statistics betweenoptical and Hi-selected samples that are predicted by thealgorithm.

In this work, we focus only on central galaxies, whosehost halos are ‘baryonified’ by the PCS21 algorithm as dis-cussed below. The L300 N1024 box described above con-tains approximately 342, 000 central galaxies with Mr ≤−19. The median along with 16th and 84th percentiles oflog[mvir/(h

−1M)] for the host halos of these centrals is11.64+0.54

−0.33. At fixed mass, halo concentrations have a mass-independent Lognormal scatter of σln cvir = 0.16 ln(10). Forthe overall distribution of central galaxy hosts, this gives amedian with 16th and 84th percentiles of cvir = 7.7+3.8

−2.6. Herecvir = Rvir/rs, with rs the scale radius of the halo returnedby rockstar by fitting a Navarro, Frenk & White (1996,NFW) profile.

1 http://www.mpa-garching.mpg.de/gadget/2 https://bitbucket.org/gfcstanford/rockstar3 www.sdss.org

2.2 Baryonification scheme

The PCS21 algorithm uses a modified version of the baryoni-fication prescription of Schneider & Teyssier (2015, hereafter,ST15) to model the spatial distributions of a number ofbaryonic components in each central galaxy and its host halo.These include:

• A spherical distribution of stars in the central galaxy(‘cgal’) with half-light radius Rhl whose relation with thehalo radius Rvir is constrained by observations (Kravtsov2013). The corresponding mass fraction is fcgal = m∗/mvir.In principle, we could also model the stellar distribution as acombination of a disk and a bulge, which we leave for futurework.

• A 2-dimensional axisymmetric Hi disk (‘Hi’) with scalelength hHi, for centrals with mHi > 0, with the hHi-mHi

relation being constrained by observations (Wang et al. 2016,see equation 8 of PCS21). The corresponding mass fractionis fHi = 1.33mHi/mvir, with the prefactor accounting forHelium correction. (The Hi disk was not modelled by ST15.)

• A spherical distribution of bound hot gas (‘bgas’) inhydrostatic equilibrium. The halo mass dependence of the cor-responding mass fraction fbgas is constrained by X-ray clus-ter observations at mvir & 1013h−1M using a 2-parametermodel and extrapolated to lower masses where needed. Wewill discuss the sensitivity of our results to these parametervalues later.

• Expelled gas (‘egas’) or the circum-galactic medium(CGM). As discussed by PCS21, for rotation curve modellingthis is essentially a uniform density distribution inside Rvir,so that the specific value of the free parameter used by ST15to model its distribution does not affect any of the analysisbelow. The corresponding mass fraction fegas is constrainedby baryonic mass conservation by demanding4

fbary ≡ fcgal + fHi + fbgas + fegas = Ωb/Ωm ' 0.163 . (4)

In addition to modelling the Hi disk, the PCS21 version ofbaryonification also departs from ST15 by truncating andnormalising all mass profiles at the halo virial radius ratherthan at infinity. As discussed by Arico et al. (2020), thisconsiderably simplifies the implementation of this schemewhile still maintaining its accuracy in our regime of interest.Further details of the numerical implementation, as well asall the underlying scalings of baryonic mass fractions andgalaxy sizes with halo properties, can be found in section 3.2of PCS21. Baryonification schemes of this type have beenshown to successfully reproduce the small-scale matter powerspectrum and bispectrum of cosmological hydrodynamicalsimulations (e.g., Chisari et al. 2018; Arico et al. 2021).

The rotation curve vrot(r) for each mock galaxy is calcu-lated using equation (11) of PCS21, which can be rewritten

4 This is violated by a small fraction (∼ 1%) of objects with Mr ≤−19 for which the sum fcgal+fHi+fbgas exceeds Ωb/Ωm (which in

turn are dominated by objects having fcgal + fHi > Ωb/Ωm). Forsuch objects, we follow PCS21 and set fegas = 0 without changingany of the other baryonic mass fractions, so that fbary > Ωb/Ωm.

Overall mass conservation then implies that the correspondingdark matter fraction frdm = 1− fbary (see section 3) is smaller

than 1− Ωb/Ωm for these objects.

MNRAS 000, 1–20 (0000)

4 Paranjape & Sheth

as

v2rot(r) = v2Hi(r) +∑α

Gmα(< r)

r+Gmrdm(< r)

r

≡ v2bary(r) +Gmrdm(< r)

r, (5)

where, in the first line, v2Hi(r) is the Hi disk contribu-tion (equation 10 of PCS21), the sum runs over α ∈bgas, cgal, egas, mα(< r) is the mass of component α en-closed in radius r and mrdm(< r) is the corresponding massof the ‘relaxed’ dark matter component which we discuss indetail in the next section, and the second line defines thebaryonic contribution v2bary(r).

Below, we will also use the total (sphericalised) massprofile contained in radius r, which can be split into contribu-tions from baryons and the relaxed dark matter component,

mtot(< r) = mbary(< r) +mrdm(< r)

=∑χ

mχ(< r) +mrdm(< r) , (6)

where the sum in the second line runs over χ ∈bgas, cgal, egas,Hi.

For later use, we also calculate an integrated baryonicmass Mbary (e.g., Lelli et al. 2017; Sales et al. 2017) foreach central as the sum of the masses of stars and cold gascontained inside the radius r = 2Rh,bary, where Rh,bary isthe radius which encloses half the mass of stars and coldgas5:

Mbary = mcgal(< 2Rh,bary) +mHi(< 2Rh,bary) , (7)

and where mHi(< r) includes the Helium correction men-tioned above, so that mHi(< Rvir) = 1.33mHi. Our use ofa 3-dimensional half-mass radius to define Mbary can, inprinciple, lead to systematic effects when comparing withobservations which typically use projected sizes for measur-ing Mbary. For such analyses below, we have checked thatreplacing Mbary with the total m∗ + 1.33mHi for each galaxyleads to identical conclusions, i.e., our results are expectedto be insensitive to the exact definition of Mbary.

As discussed in the Introduction, the radial accelerationrelation is then the dependence of ∆a = atot/abary − 1 onabary, with atot and abary given by equations (1) and (2),respectively. Notice that vbary, and hence abary, containscontributions from both spherical as well as axisymmetriccomponents. This is consistent with observational analysesof the RAR (see, e.g., McGaugh et al. 2016).

3 PHYSICS OF THE RAR: ANALYTICALINSIGHTS

Thus far, we have not commented on the shape of the relaxeddark matter profile mrdm(< r). As we discuss in this section,this is a key component in determining the shape of the meanRAR.

3.1 Quasi-adiabatic relaxation

In the default PCS21 model, mrdm(< r) is calculated assum-ing complete spherical symmetry for all components, and

5 In practice, we determine 2Rh,bary by sampling the rota-tion curve using 200 logarithmically spaced points in the range

(0.001, 1)×Rvir for each central galaxy.

assuming that the dark matter quasi-adiabatically relaxes(approximately conserving angular momentum) in responseto the baryonic components. The details of the procedure canbe found in ST15 or Appendix A of PCS21 and are brieflysummarised below. This relaxation can be described using afunction ξ(r) defined as

ξ ≡ r/rin , (8)

where rin is the initial radius of a spherical dark matterelement which eventually relaxes to a final radius r. Theequation governing the form of ξ can be written in generalas

ξ = 1 + X(mudm(< rin)

mtot(< r)

), (9)

where mudm(< rin) is the unrelaxed dark matter profile. Weapproximate this using the NFW form in this work (althoughsee below). The function X (y) in the ST15 model, which wasadopted by PCS21, is given by

X (y) = qrdm (y − 1) . (10)

Here qrdm is a parameter controlling the level of angularmomentum conservation, with qrdm = 1 for perfect conserva-tion and qrdm = 0 for no baryonic backreaction. The defaultmodel from PCS21 follows the ST15 prescription and setsqrdm = 0.68. Equation (9) is then solved iteratively to obtainξ(r), using which the relaxed dark matter profile satisfies(see Appendix A of PCS21)

mrdm(< r) = frdmmudm(< r/ξ) , (11)

where frdm is the mass fraction of dark matter inside thehost halo’s virial radius; due to equation (4), this is set tofrdm = 1−Ωb/Ωm in this work for all but the small fractionof objects discussed in footnote 4.

Figure 1 shows the numerically computed relaxationratio ξ in the ST15 model (right panel) for three examplesof baryonified halos whose mass profiles are shown in theleft panel. The right panel shows that there is a lower limitto ξ because y ≥ 0 in equation (10) (being the ratio ofmasses, y cannot be negative at any r). Moreover, while theST15 model leads to a contraction of the dark matter profilethroughout the least massive halo, it predicts an expansionin the outskirts of more massive halos. A comparison withthe left panel shows that this happens in regions where thefraction of bound and/or expelled gas is higher than thatof stars and the Hi disk (compare the thin solid lines whichshow all baryons with the dash-dotted lines showing only thestellar and Hi component). The dashed red curve and bandin the right panel respectively show the median and central95% range of ξ for the entire mock catalog used below.

Strictly speaking, the assumption of perfect sphericalsymmetry is not valid due to the presence of the axisymmet-ric baryonic disk, as well as the fact that dark matter halosin gravity-only simulations are triaxial in general. Includingthese non-spherical effects analytically and calculating a tri-axial ~ξ(~r) is quite difficult. Interestingly, though, the resultsof hydrodynamical simulations show that baryonic backre-action actually tends to make the dark matter distributionafter relaxation more spherical (Dubinski 1994; Kazantzidiset al. 2004; Abadi et al. 2010; Cataldi et al. 2021). We there-fore expect that, in practice, our spherical assumption willlead to an accurate average description of quasi-adiabaticrelaxation. We intend to explore the effects of asphericity inthe relaxation process, along with detailed comparisons tohydrodynamical simulations, in future work.

MNRAS 000, 1–20 (0000)

RAR in LCDM 5

Figure 1. Relaxation physics. (Left panel:) Mass profiles for three examples of baryonified halos hosting an NGC99-like galaxy (curveswith different colours), with different combinations of components shown using the linestyles indicated in the legend. Halo concentrations,

stellar masses and Hi disk sizes were set using scaling relations from the literature (see Paranjape et al. 2021, PCS21), while the Himass was fixed to mHi = 109.83h−2M in each case. Other baryonic fractions were set as described in the text. The two lower mass

halos are the same as shown in figure 4 of PCS21. (Right panel:) Relaxation ratio ξ = r/rin for the dark matter profile computed using

equations (9) and (10) with qrdm = 0.68. The thick solid curves show ξ for the three halos from the left panel. The dashed red curve andband respectively show the median and central 95% range of ξ for the entire luminosity-complete mock catalog used in the text. The

lower horizontal line shows the theoretical lower bound of 1− qrdm (see text). The upper horizontal line indicates unity, the solution when

baryons do not affect the dark matter profile. Values of ξ less (greater) than unity correspond to contraction (expansion) of the darkmatter profile due to the presence of baryons.

3.2 Relaxation ratio and the RAR

Equation (9) allows us to appreciate an intimate connectionbetween the level of angular momentum conservation andthe shape of the RAR. For the spherically symmetric caseassumed above, equations (8) and (11) give the identity

mudm(< rin)

mtot(< r)=

1

frdm

atot(r)− abary(r)

atot(r). (12)

Using this, equation (9) can be formally inverted and, aftersome straightforward algebra, brought to the form

∆a =Λ

1− Λ, (13)

where ∆a was defined in equation (3), and

Λ ≡ frdm X−1(ξ − 1) , (14)

with X−1(z) = y being the inverse function of X (y) = z.Equation (13) is remarkable because it shows that, as a

function of the relaxation ratio ξ, the RAR has zero scatterin the spherical baryonification model, regardless of the exactfunctional form of X (y) which sets the mean relation. Forour default choice of frdm = 1 − Ωb/Ωm, it is clear fromequation (14) that the scatter in the RAR as defined inthe literature arises solely from the scatter between ξ andabary. If we think of these functions as ξ = ξ(r|bary,dm) andabary = abary(r|bary), then, at fixed r and for a given bary-onic configuration, this scatter is caused predominantly bythe object-to-object variation in halo mass and concentrationfor different central galaxies with this baryonic configura-tion. There could be some additional scatter at fixed halomass and concentration if, for example, multiple baryonicconfigurations happen to lead to the same value of ξ butdifferent abary, or vice-versa. This is, of course, very differentfrom MOND which predicts that the RAR should have nointrinsic scatter.

For the specific choice of X in equation (10) adopted inthis work, equation (13) simplifies to

∆a =(ξ − 1) + qrdm

qrdm/frdm − (ξ − 1)− qrdm. (15)

To glean some analytical insights into the implications ofequation (13) or equation (15), it is useful to analyse theresult perturbatively in the case 0 < qrdm 1, i.e., in thelimit of small baryonic backreaction. This is the same limitas studied by Navarro et al. (2017), who ignored baryonicbackreaction and focused on explaining the origin of theRAR in the low-acceleration regime using various baryon-dark matter scalings. At lowest order in qrdm, this leadsto

ξ − 1 ' qrdm(fbarymudm(< r)−mbary(< r)

frdmmudm(< r) +mbary(< r)

). (16)

Plugging this into equation (15) gives, after some simplifica-tion,

∆a 'frdmmudm(< r)

mbary(< r), (17)

which is an eminently sensible result. This also shows thatthe RAR in the limit of no baryonic backreaction can beexpected to have a large scatter as a function of abary, sincethe dark matter profile mudm(< r) in the numerator of equa-tion (17) is decoupled from abary(r) ∼ mbary(< r)/r2, apartfrom the baryon-dark matter scalings that relate halo massand concentration to baryonic mass fractions and sizes. Ap-pendix A shows that, if the initial mass distribution is similarto an NFW profile, then the RAR is amenable to analytictreatment, even when backreaction is large. In particular,one can analytically estimate the RAR of individual galaxiessuch as the ones depicted by the thick solid lines in figure 1.The resulting dependence of the median and scatter of the

MNRAS 000, 1–20 (0000)

6 Paranjape & Sheth

RAR on various halo and galaxy properties then provides ananalytic understanding of the trends we discuss below usingnumerically sampled mock galaxies.

4 RESULTS FROM MOCKS

With these analytical arguments in hand, we now explorethe RAR in the mock catalogs described in section 2 byvarying the underlying baryonification choices of the PCS21algorithm, as well as selecting galaxy samples using variouscriteria.

4.1 Default model

The coloured histogram in the top panel of figure 2 showsthe RAR – the horizontal axis shows log[abary( m s−2)] andthe vertical axis shows log[∆a] – of the full sample of centralgalaxies with Mr ≤ −19 in one mock (∼ 342, 000 objects)for our default baryonification model. We calculated atot(r)and abary(r) on 20 logarithmically spaced values of r in therange (0.001, 1)×Rvir for each central galaxy (we explore theeffects of changing this sampling choice below). Our resultstherefore explore not only the inner, baryon-dominated partsof each halo, but also the halo outskirts corresponding to theultra-low-acceleration regime (abary . 10−12 m s−2) which isas yet observationally unconstrained.

The dashed and solid purple curves showlog[F(abary/a0) − 1] with F(x) being, respectively, theMOND-inspired calibration for atot/abary from equation (4)of McGaugh et al. (2016),

F(x) =1

1− e−√x, (18)

and equation (4) of Chae et al. (2019),

F(x) =

[1

2+

√1

4+

1

xν

]1/ν, (19)

with a0 = 1.2×10−10 m s−2 (McGaugh et al. 2016 denote thisas g†). For x 1 both functions scale as F → 1/

√x, and

both tend to unity when x 1 (which simply reflects the factthat this is the limit in which baryons dominate). However,the approach to this baryon-dominated limit is different:∆a = F − 1→ e−

√x for equation (18) whereas ∆a → x−ν/ν

for equation (19). The solid curve shows ν = 0.8, which Chaeet al. (2019) argue fits the observed RAR well, particularly atabary ≥ a0 where ellipticals dominate. However, ν = 1, whichlies approximately midway between the solid and dashedcurves, may provide a better description of the RAR definedby spirals (Famaey & Binney 2005; Sanders & Noordermeer2007; Chae et al. 2020, see also Appendix A1).

The solid yellow curve shows the median 〈∆a,mock 〉 ofthe distribution in bins of abary, while the dashed yellowcurves show the corresponding 16th and 84th percentiles.6

We see that the median RAR of our default mock is in remark-ably good agreement with the solid purple curve for abary &10−12 m s−2, i.e., throughout the low- and high-accelerationregimes. (Quantitatively, | 〈∆a,mock 〉 /∆a,eqn 18 − 1| . 20%in this range.) Since the solid purple curve was shown by

6 To calculate the yellow curves, we use 17 linearly spaced bins

in log[abary/( m s−2)] in the range (−13.5,−7.5), discarding binscontaining fewer than 10 data points. The location of the curves

on the horizontal axis is taken to be the median abary of each bin.

Figure 2. Default RAR. (Top panel:) The RAR (∆a fromequation 3 as a function of abary) defined by central galaxies in

one mock with our default baryonification model having qrdm =0.68 (see sections 2.2 and 3.1). The coloured histogram countsmeasurements from all centrals, with rotation curves sampled at

20 logarithmically spaced points in the range (0.001, 1)×Rvir foreach object. Yellow solid and dashed lines show the median and

central 68% region of the distribution in bins of abary. Solid and

dashed purple curves show F(abary/a0)− 1 using equations (19)and (18), respectively, with parameters as described in the text.

The measured median relation agrees well with the solid purple

curve at low and high abary. The break in the median relationat ultra-low accelerations (abary . 10−12 m s−2) is discussed in

detail in the text. (Bottom panel:) Residuals relative to the solid

purple curve, computed as log[∆a/(F(abary/a0)− 1)], using F(x)from equation (19), for each point (abary, atot) having abary >

10−12 m s−2, shown as a function of Mbary which is assignedfor each galaxy using equation (7) (so each galaxy contributes avertical streak). Yellow curves now show the median and central

68% of the distribution of the residuals in bins of Mbary.

Chae et al. (2019) to be a good description of the observedRAR, this is a non-trivial success of our default model, withno additional tuning beyond what was already discussedby PCS21 to match other observations. The scatter aroundthe median relation is typically σlog[atot] ∼ 0.075 dex forabary > 10−12 m s−2. (Note that the scatter in log[∆a] seenin the figure is considerably larger; we report the scatterin log[atot] in the text for ease of comparison with the lit-erature.) The RAR in the (as yet unobserved) ultra-lowacceleration regime of abary . 10−12 m s−2 sharply breaksaway from the extrapolation of equations (19) and (18),first dipping below at abary ' 10−12 m s−2 and then risingsteeply at abary . 10−12.4 m s−2. We have found that thecloud with very few points at the top left of the distribu-tion (log[abary] . −12.6, log[∆a] & 1.4) is dominated byobjects having extremely low m∗ (∼ 106h−2M), which arelikely numerical artefacts in the statistical sampling of thecolour-dependent mass-to-light ratio in the PCS21 algorithm.We will therefore ignore the regime abary . 10−12.6 m s−2

in the discussion below. We will, however, later explore thenature of the galaxies which lead to the dip and rise near

MNRAS 000, 1–20 (0000)

RAR in LCDM 7

abary ' 10−12 m s−2. For now, we simply note that our resultsconstitute predictions for the ultra-low-acceleration regime(see also Oman et al. 2020).7

The bottom panel of figure 2 shows the residuals ofthe RAR ratio data in the top panel with the solid purplecurve, as a function of baryonic mass Mbary (equation 7).Specifically, on the vertical axis we plot log[(atot/abary −1)/(F(abary/a0) − 1)] using F(x) from equation (19) witha0 = 1.2 × 10−10 m s−2 and ν = 0.8. This is conceptuallysimilar to figure 5 of Lelli et al. (2017), who define theresiduals using log[(atot/abary)/(F(abary/a0))], i.e., withoutsubtracting unity in the numerator and denominator in-side the logarithm. This difference is important because theresiduals calculated by Lelli et al. (2017) will be artificiallysuppressed in the high-acceleration regime where the numer-ator and denominator both approach unity. By subtractingthis leading behaviour, our definition of the residuals offersa sharper characterisation of the scatter around the medianrelation. We see from the bottom panel of figure 2 that thisscatter is nevertheless small, with a typical value of ∼ 0.2dex, similar to the scatter seen in log[∆a] in the top panel.We have checked that using the Lelli et al. (2017) definitionof residuals instead, the typical scatter in the bottom panelis even smaller, closer to ∼ 0.1 dex and similar to what theyfind.

It is clear from the discussion in the Introduction andsection 3 that the RAR in our ΛCDM mocks is an emergentphenomenon rather than a universal law (Keller & Wadsley2017; Desmond 2017; Navarro et al. 2017; Ludlow et al. 2017;Tenneti et al. 2018). That discussion also shows that galaxiespopulating halos of different masses and concentrations mightbe expected to define different RARs, in general. The RAR isadditionally expected to be sensitive to the physics of quasi-adiabatic relaxation of dark matter in the presence of baryons.In the following subsections, we explore the sensitivity ofthe RAR to differences in the physical content of galaxies,observational selection criteria and, importantly, differencesin the physical modelling of baryonification. Unless otherwisementioned, the plots below are formatted identically to thetop panel of figure 2, with the solid and dashed purple curvesbeing repeated from that figure.

4.2 Sensitivity to relaxation physics

Figure 3 shows the effect of changing the details of the quasi-adiabatic relaxation scheme (see the discussion in section 3.2).The top panel shows the RAR obtained if the baryonic matterhad no backreaction on the dark matter profile (e.g., Navarroet al. 2017), i.e., setting qrdm → 0 in equation (10) whichgives ξ → 1 in equation (15) and leads to equation (17).The ultra-low-acceleration regime is essentially unchangedas compared to the default case in figure 2, which is notsurprising since this arises from the outer, dark matter dom-inated regions of the halo where the dark matter profile isrelatively unaffected by the presence of baryons in any case.In the high-acceleration regime, on the other hand, we see a

7 Recently, Lelli et al. (2017) and Di Paolo et al. (2019) have

presented RAR observations of ultra-faint dwarf spheroidal galax-ies which probe values abary . 10−12 m s−2 (see Garaldi et al.2018, for the corresponding predictions from ΛCDM simulations).

This, however, is different from our predictions which hold for theoutskirts of much more massive systems and are hence relevant

on very different length scales.

Figure 3. RAR and relaxation physics. Same as top panel

of figure 2, but assuming qrdm = 0 (no baryonic backreactionon the dark matter profile; top panel) or qrdm = 1 (perfectangular momentum conservation; bottom panel). The medianand scatter are both different compared to our fiducial choice,

qrdm = 0.68, from figure 2, especially in the high-accelerationregime (abary & 10−10 m s−2). These trends can be understoodanalytically (Appendix A).

dramatic effect: the median RAR is substantially lower, andthe scatter is substantially higher, than in the default case.

The bottom panel shows the RAR in the opposite limitwhere baryonic backreaction perfectly conserves angularmomentum, which we model by setting qrdm = 1 in equa-tions (15) and (9). As expected, the ultra-low-accelerationregime is unaffected. In the high-acceleration regime, theRAR is now substantially higher than in the default case,with a substantially smaller scatter. Appendix A providesanalytic understanding of the strong dependence on qrdm.

Our default choice of qrdm = 0.68 and, indeed, the choiceof functional form in equation (10) adopted from ST15, issubject to some theoretical uncertainty arising from vari-ous choices in modelling baryonic feedback physics (such aswinds driven by supernovae or active galactic nuclei) madewhile performing hydrodynamical simulations. ST15 do notprovide any error on the value of qrdm and, more generally,the dependence of quasi-adiabatic relaxation on galaxy andhalo properties has also not been systematically studiedin the literature to date (although see Chua et al. 2019;Cataldi et al. 2021, for related studies). Considering thistheoretical uncertainty, as well as the sharp sensitivity ofthe high-acceleration RAR to the physics of quasi-adiabaticrelaxation, the good agreement between the default case andthe observed relation is truly remarkable, especially sincethe original ST15 model made no reference to the RAR.A different point of view would then be to think of RARobservations in the high-acceleration regime as providingconstraints on the value of qrdm (or, more generally, the formof equation 10). In this context, it is worth noting that theRAR in this regime as defined by spiral galaxies is claimedto be better described by setting ν = 1 rather than ν = 0.8in equation (19) (e.g. Chae et al. 2020), which would pullthe median relation lower and might be better described by

MNRAS 000, 1–20 (0000)

8 Paranjape & Sheth

Figure 4. RAR and cold gas content. Same as top panel

of figure 2, but for ‘bulge-dominated’ galaxies having mHi = 0(top panel) or gas-rich ‘spirals’ with mgas/m∗ ≥ 0.72 (bottompanels). Here, mgas = 1.33mHi, and the threshold value cor-responds to the 95th percentile of mgas/m∗ in the mass range

9.9 ≤ log[m∗/(h−2M)] ≤ 10.9. The break in the median relationat ultra-low accelerations (abary . 10−12 m s−2) is restricted tobulge-dominated objects.

decreasing the value of qrdm (see figure A1). We return tothis point below.

4.3 Sensitivity to baryonic content

We next investigate the sensitivity of the RAR to the baryoniccontent of galaxies. We focus here on the presence/absence ofan Hi disk, and on the relative contribution of the expelledgas (‘egas’) component, which is a proxy for the circum-galactic medium. Galaxies with different ‘egas’ fractions maybe expected to behave quite differently in the halo outskirtsand consequently in the ultra-low-acceleration regime of theRAR.

The top panel of figure 4 shows the RAR of mock ‘bulge-dominated’ galaxies without Hi disks, while the bottom panelshows the RAR of gas-rich, disk-dominated ‘spiral’ galaxies.We see that bulge-dominated galaxies span a wider rangeof abary values than the spirals do. In the low-accelerationregime of overlap between the two samples, the median RARfor the two samples is indeed very similar, consistent with theobservations quoted above. The scatter around the medianis somewhat smaller for spirals (σlog[atot] ∼ 0.06 dex) thanfor bulge-dominated galaxies (∼ 0.075 dex). Interestingly,the break from the MOND-inspired relations at ultra-lowaccelerations is restricted to the bulge-dominated systems.We discuss this further below.

We have also checked that splitting our default galaxysample by the value of Mbary (with the split defined at themedian value Mbary ∼ 2 × 1010M) leads to results quali-tatively very similar to figure 4, with the massive samplebehaving like the spirals and the low-mass sample behavinglike the bulge-dominated galaxies. The stark differences be-tween such samples at ultra-low accelerations motivate us to

Figure 5. RAR and diffuse gas content. Same as top panel

of figure 2, but for galaxies with fegas ≥ 0.134, i.e. rich in expelledor diffuse gas (top panel) or fegas ≤ 0.069, i.e., poor in diffuse gas(bottom panel). The threshold values for rich and poor systemsrespectively correspond to the 90th and 10th percentile of fegas in

the mass range 9.9 ≤ log[m∗/(h−2M)] ≤ 10.9. The break in themedian relation at ultra-low accelerations (abary . 10−12 m s−2)is restricted to diffuse gas-rich objects. Section 4.3 argues that

fegas is the primary variable controlling the form of the RAR inthis regime.

study the effect of the one baryonic component that reachesthe halo outskirts, namely the expelled gas (‘egas’) which wediscuss next.

Figure 5 shows the RAR for galaxies with large (toppanel) and small (bottom panel) values of the expelled gasmass fraction fegas, i.e., galaxies rich and poor, respectively,in diffuse gas content. We see that diffuse gas-rich galax-ies tend to populate low and ultra-low accelerations, whilediffuse gas-poor galaxies populate high and low accelera-tions. As compared to the split between bulge-dominatedand spiral galaxies in figure 4, in this case we see distinctdifferences between the two samples already at low acceler-ations 10−12 m s−2 . abary . 10−11 m s−2, with the medianrelation of gas-poor galaxies being lower than that of gas-rich galaxies. This indicates that diffuse gas content is moreimportant than morphology in determining the typical RARat low accelerations.

We also see that the break from the MOND-inspiredrelations at ultra-low accelerations is restricted to diffuse gas-rich galaxies. It is not surprising that galaxies with a largeamount of diffuse gas dominate the RAR arising from the halooutskirts, since the ‘egas’ component of our default model isessentially a uniform density sphere at scales r < Rvir (seefigure 4 of PCS21). In fact, this also explains the results offigure 4, since bulge-dominated galaxies with mHi = 0 arelikely to have higher values of fegas due to the baryonic massconservation constraint.

The specific form of the sharp break in the RAR fromMOND-like predictions at ultra-low accelerations for thehigh-fegas sample is a consequence of the choice of spatialdistribution of the ‘egas’ component, which is the same asmotivated by ST15 in modelling the matter power spec-

MNRAS 000, 1–20 (0000)

RAR in LCDM 9

Figure 6. RAR and halo properties. Same as top panel of figure 2, but for centrals with host halo mass mvir and concentration cvirrestricted to narrow ranges. The left (right) panels show results for mvir values in the 20-25 (90-95) percentile range, i.e. for low (high)halo mass. In each bin of mvir, the top (bottom) panel shows results for the corresponding 10-20 (80-90) percentile range of cvir, i.e. forlow (high) concentrations. The scatter is very low in each bin of (mvir, cvir) but the median relation varies systematically with both mvir

and cvir, consistent with analytical arguments that host halo mass and concentration are the primary variables responsible for the scatter

in the RAR of any galaxy sample (see section 3.2 and Appendix A).

trum of hydrodynamical ΛCDM simulations. This result,together with the difference between the median RARof galaxies rich and poor in diffuse gas at accelerations10−12 m s−2 . abary . 10−11 m s−2, are testable predictionsof the ΛCDM+baryons framework.

4.4 Sensitivity to halo mass and concentration

The results in the preceding two subsections focused onthe dependence of the median RAR at high and ultra-lowaccelerations on variations in the underlying baryon-darkmatter response physics and the baryonic content of galaxies.In this subsection, we aim to understand the scatter aroundthe median relation.

As we saw in section 3.2, RAR as a function of therelaxation ratio ξ has zero scatter in the ΛCDM+baryonsframework. The scatter in the RAR as a function of abaryis therefore entirely due to the scatter between ξ and abary,which in turn is expected to be driven almost entirely by thevariation in halo mass mvir and concentration cvir for galaxieswith similar baryonic content. This means, if we focus ongalaxies in narrow ranges of (mvir, cvir), the resulting RARshould have very little scatter but a mean trend that dependson the values of mvir and cvir, in general.

We test this idea in figure 6. The left (right) panelsshow the RAR for our default model, with galaxies selectedto be in a narrow range of low (high) mvir. Within eachsuch range, the top (bottom) panels further split the galaxiesinto narrow ranges of low (high) values of cvir. It is visuallyobvious that the RAR in each of these (mvir, cvir) bins hasvery low scatter (quantitatively, σlog[atot] ∼ 0.03-0.04), whilethe median trends depend significantly on the values of mvir

and cvir. The median RAR tends to increase in amplitude asmvir increases and, at fixed mvir, as cvir increases. That isto say, galaxies in massive, high-concentration halos have a

median RAR normalisation that is slightly but significantlyhigher than that of galaxies in low-mass, low-concentrationhalos. We have checked that the results of using differentvalues of mcvir and cvir lead to smooth extrapolations ofthese trends (see also figure 8). Appendix A provides analyticunderstanding of these trends.

4.5 Sensitivity to other details

The preceding subsections, together with Appendix A, giveus an essentially complete picture of how the median RARand its scatter emerges from the interplay between haloproperties, their scalings with baryonic content and the directcross-talk between baryons and dark matter through quasi-adiabatic relaxation. In this subsection, we explore a fewmore aspects of the RAR, including its sensitivity to theshape of the ‘un-baryonified’ dark matter profile, some ofthe scaling relations underlying our baryonification schemeand technical choices in sampling the rotation curve data.We also show how the RAR responds to systematic changesin optical sample selection for an SDSS-like galaxy sample.

4.5.1 Dark matter profile

Our default model uses the NFW form to model the ini-tial, ‘un-baryonified’ dark matter profile. We have checkedthat using an appropriately matched Einasto profile instead(Einasto 1965; Cardone et al. 2005; Retana-Montenegro et al.2012; Dutton & Maccio 2014; Klypin et al. 2016) leads toessentially no change in the median RAR or its scatter. Inother words, while the RAR is sensitive to the overall massand concentration of halos (section 4.4), it is relatively insen-sitive to changes in the inner and outer slope of the initialdark matter profile.

MNRAS 000, 1–20 (0000)

10 Paranjape & Sheth

Figure 7. RAR and bound gas fraction. Same as top panel

of figure 2, but with the baryonification parameter β defining thebound gas fraction in equation (20) increased (top panel) anddecreased (bottom panel) by ±0.4 from the default value of 0.6.The ultra-low-acceleration regime (abary . 10−12 m s−2) responds

to low values of fbgas. The text relates this to the relative massfractions and hence spatial distributions of bound and diffuse gasin the halo outskirts (section 4.5.2).

4.5.2 Baryonic scaling relations: bound gas fraction

The mass fraction fbgas in hot, bound gas in our defaultmodel from PCS21 is the same as used by ST15 and is givenby

fbgas = (Ωb/Ωm)×[1 + (Mc/mvir)

β]−1

, (20)

with Mc = 1.2× 1014h−1M and β = 0.6. ST15 showed thatthere is considerable room for variation in the values of Mc

and especially β when considering the effects of baryonifi-cation on the matter power spectrum alone. Moreover, asdiscussed by PCS21, the relation above has been extrapo-lated to halos with mvir ∼ 1011h−1M in our mocks, wellbelow the scale mvir & 1013h−1M at which ST15 calibratedtheir results. It is therefore interesting to ask how the RAR isaffected by variations in these model parameters. We explorethis in figure 7, focusing on β since the pivot scale Mc isreasonably well constrained by the X-ray cluster observationscited by ST15. We see that variations in β primarily affectthe ultra-low-acceleration regime, changing the slope of themedian RAR. This is sensible, because an increase in fbgasat the mass scales of our interest (by decreasing β) will corre-spondingly decrease fegas due to baryonic mass conservationand hence change the relative spatial behaviour of the ‘bgas’and ‘egas’ components in the halo outskirts (see figure 1).Thus, the ultra-low-acceleration regime of the RAR is, inprinciple, sensitive to the physics of both hot and cold gasin the outer halo.

4.5.3 Baryonic scaling relations: stellar size

Our default model treats the stellar profile as a bulge withhalf-light radius Rhl = 0.015Rvir, which is approximately the

result obtained by Kravtsov (2013) using a power-law fit to∼ 180 galaxies. We have not included the scatter of ∼ 0.2 dexaround this relation which was reported by Kravtsov (2013)and which, as discussed by him, could in principle be linkedto the halo spin using the formalism of Mo et al. (1998). Weassess the potential effect of this scatter in figure 8, usinghalo concentration as a proxy for internal halo properties(the analytical model in Appendix A suggests that the RARought to be sensitive to a correlation between Rhl/Rvir andcvir).

Halo concentrations in our mocks have a Lognormaldistribution at fixed mass, with a median 〈 cvir|mvir 〉 anda scatter 0.16 dex taken from Diemer & Kravtsov (2015).The left (right) panels of figure 8 show the RAR after mul-tiplying (dividing) the default Rhl/Rvir for each galaxy by(cvir/ 〈 cvir|mvir 〉)µ, thus leading to a positive (negative) cor-relation between stellar bulge size and initial halo concen-tration. In this toy model, the entire variation in Rhl/Rvir

is explained by halo concentration; more realistic modelswould allow room for other variables (such as halo angularmomentum, or some unspecified source of stochasticity) toalso play a role. By construction, the modified set of Rhl/Rvir

values obey a Lognormal distribution at fixed halo mass, withmedian 0.015 and a scatter of 0.16 × µ dex. We thereforeset µ = 1.3, which gives a scatter of ' 0.2 dex in Rhl/Rvir

for both choices of the correlation, consistent with Kravtsov(2013).

The top (bottom) panels of figure 8 use the same rangesof mvir shown in the left and right panels, respectively, offigure 6. Similarly to that figure, we further split these fixed-mvir samples into narrow ranges of cvir. For this figure alone,so as to highlight differences between the subsamples, weonly show the median and central 68% of each RAR using thedifferently coloured lines. As expected from the discussionin sections 3.2 and 4.4, galaxies at fixed mvir and cvir tracethe same RAR regardless of the sign of the bulge size-haloconcentration correlation. We do see a very interesting traceof this signature however, in that the range of values of abaryexplored by any sample responds sensitively to whether thecorrelation is positive or negative. In the former case, galaxiesin high-concentration halos explore lower values of abarythan low-concentration ones, and vice-versa for a negativecorrelation. This trend can be understood as follows. Considera specific galaxy with stellar mass m∗ in an (mvir, cvir) host.For a positive correlation, a large cvir implies a larger Rhl forthis galaxy than in the absence of the correlation. Since the(now flatter) stellar density profile must enclose the same m∗inside the same Rvir, its inner parts are forced to be lower,thus contributing less to mcgal(< r) and hence abary in theinner region, than in the absence of the positive correlation.A negative correlation between Rhl and cvir has exactly theopposite effect.

While this shows that there is clearly no new physicsexplored by such a correlation beyond the dependence of theRAR on mvir and cvir through the relaxation ratio ξ, it doeslead to a curious degeneracy. It is clear from the left handpanels of figure 8 that the RAR obtained from averaging overall cvir values will tend to curve downwards at large abary inthe case of a positive Rhl-cvir correlation. Further integrationover mvir will not change this curvature, so the resultingRAR will be qualitatively similar to that in which there is noRhl-cvir correlation but qrdm is smaller (compare top panelof figure 3). Conversely, the right hand panels of figure 8show that an Rhl-cvir anti-correlation will result in an RAR

MNRAS 000, 1–20 (0000)

RAR in LCDM 11

Figure 8. RAR and stellar profile. Similar to figure 6, showing results when the baryonification parameter Rhl/Rvir (whose defaultvalue is 0.015) is multiplied (left panels) or divided (right panels) by (cvir/ 〈 cvir|mvir 〉)1.3, with 〈 cvir|mvir 〉 being the median concentration

at fixed halo mass, which leads to a scatter of ∼ 0.2dex in Rhl/Rvir around a median value of 0.015 in each case. For this figure, we show

only the median and central 68% scatter of the RAR for centrals selected by mvir and cvir: top (bottom) panels correspond to halos in the20-25 (90-95) percentile ranges of mvir, and the coloured lines in each panel further split the samples into the indicated percentiles of cvir(c.f. figure 6). While galaxies at fixed (mvir, cvir) trace out the same RAR in each case, the range of abary explored depends sensitively on

whether the correlation between Rhl and cvir is positive or negative. See section 4.5.3 for a discussion.

that would imply a larger qrdm if one assumed there wasno Rhl-cvir correlation (e.g. bottom panel of figure 3). Thatcurvature in the RAR may arise from the Rhl-cvir relationrather than qrdm must be kept in mind during any analysiswhich aims to probe the physics of quasi-adiabatic relaxationusing the RAR.

We noted earlier that setting ν = 1 rather than ν = 0.8in equation (19) might provide a better description of theRAR of spiral galaxies than that of ellipticals. The abovediscussion shows that our model is capable of explaining sucha difference either by decreasing the value of the relaxationparameter qrdm, or making Rhl correlate positively with cvir,or a combination of the two. This is subject to the caveat thatthe relaxation model in equation (10) (adopted from ST15) isitself approximate, and we also have not yet self-consistentlymodelled stellar disks. Using a more physically motivated‘size-mass’ correlation, such as the one between galaxy sizeand halo angular momentum alluded to above, can potentiallyadd another dimension to such degeneracies (see also thediscussion in Desmond 2017). It will be interesting to studysuch effects in hydrodynamical simulations of cosmologicalvolumes, which we leave to future work.8

Finally, our discussion of scalings with optical size hasan interesting connection to other recent work. In their studyof the RAR in dwarf disk spirals and low surface brightnessgalaxies, Di Paolo et al. (2019) found that the RAR dependson a third parameter, x ≡ r/Ropt, where Ropt is the scalewhich contains 83% of the stellar light. While this trend ismost obvious at accelerations which are smaller than whereour mocks are complete, it is plausible that our mocks exhibitsomething similar, since Ropt scales with Rhl, which scales

8 We have checked that the same exercise performed using theHi disk size hHi rather than the stellar bulge size Rhl leads to no

significant effect on the RAR. This is likely because observationsallow a scatter of only ' 0.06 dex in hHi at fixed mHi, which leaves

room for only a weak correlation, at best, between hHi and cvir.

Figure 9. RAR and optical profile. Dependence of the RAR

on x ≡ r/Ropt, for massive Hi galaxies (for which our sample isvolume complete), shown in the format used by Di Paolo et al.

(2019) in their analysis of dwarf disk spirals. There is a hint of adependence on x at low accelerations, in qualitative agreement

with the observations. The upturn at low abary in the low xsystems deserves further study.

with halo mass, and we do expect weak trends with halo mass.Figure 9 indeed shows a qualitatively similar trend at leastat x & 0.4, even though we have made no effort to identifydwarf disk spirals in the mocks. Performing a more carefulcomparison would be interesting – especially for what itmay teach us about the interplay between size-mass-angularmomentum correlations and qrdm at low accelerations – butis beyond the scope of this work.

MNRAS 000, 1–20 (0000)

12 Paranjape & Sheth

Figure 10. RAR and luminosity. Same as top panel of figure 2, for centrals selected by r-band absolute magnitude as indicated, with

the samples increasing in luminosity from top left → bottom left → top right → bottom right. There is a clear luminosity dependence,with the RAR shifting vertically upwards for brighter samples. This is a natural consequence of the halo mass dependence seen in figure 6(see section 4.5.4).

Figure 11. RAR and optical colour. Same as top panels of figure 10, with the top (bottom) panels now focusing on red (blue) galaxies.The separation between red and blue uses the luminosity-dependent threshold on g − r colour given by equation (21). There is a sharp

difference between red and blue centrals in the fainter bin at ultra-low accelerations (abary . 10−12 m s−2, c.f. figure 5), with relativelylittle difference between the two samples in the brighter bin. These trends can be traced back to differences, or lack thereof, in the massfraction fegas of diffuse gas in these samples (see section 4.5.4).

4.5.4 Optical selection

Observational analyses of the RAR are typically limited bythe quality of rotation curve (e.g., Lelli et al. 2016a) or veloc-ity dispersion (e.g., Chae et al. 2019) measurements, whichcan introduce inhomogeneities in the statistical properties ofthe associated galaxy sample. Since our mock catalogs have‘perfect’ rotation curve measurements, we can use them to

ask how the RAR responds to systematic variations in, say,optical sample selection for SDSS-like galaxies.

Figure 10 shows the RAR using our default model formock centrals chosen to lie in bins of luminosity (one bin ineach panel), represented by r-band absolute magnitudes Mr

(see PCS21 for a detailed definition). The samples increase inluminosity going from top left → bottom left → top right →bottom right, with the faintest bin corresponding to sub-L∗centrals (median mvir ' 1011.6h−1M) and the brightest

MNRAS 000, 1–20 (0000)

RAR in LCDM 13

to BCGs of massive clusters (median mvir ' 1013.8h−1M).We see a clear indication that the normalisation of the me-dian RAR increases with increasing luminosity, which issensible given the results of figure 6 and the fact that cen-tral luminosity correlates positively with halo mass in ourmocks. Interestingly, recent results suggest that the RARof observed galaxy clusters also has an elevated normalisa-tion relative to that of galaxy samples (Tian et al. 2020;see also Pradyumna et al. 2021), in qualitative agreementwith our results. We also find that the typical scatter ofthe RAR varies non-monotonically with luminosity, beingσlog[atot] ∼ 0.067, 0.069, 0.077, 0.066 in successively brighterbins.

It is also interesting to split the galaxy sample at fixedluminosity by colour. We use a luminosity-dependent thresh-old on the g − r colour index of each of our mock centralgalaxies, given by (Zehavi et al. 2011)

(g − r)cut(Mr) ≡ 0.21− 0.03Mr . (21)

We classify galaxies having g − r ≥ (g − r)cut(Mr) as ‘red’and the rest as ‘blue’. Figure 11 shows the resulting RAR fortwo of the luminosity bins shown in figure 10. In the absenceof ‘beyond halo mass’ effects such as galactic conformity,galaxy colours in our default mocks correlate only with galaxyluminosity, not with halo mass or concentration. Naively,therefore, we should not expect any difference in the RAR ofred and blue galaxies at fixed luminosity. This is indeed thecase for the brighter luminosity bin shown in the right panelsof figure 11. There is, however, a secondary correlation onemust account for in a luminosity-complete sample. This isthe fact that, due to a colour-dependent mass-to-light ratio,blue centrals of a given luminosity will have lower stellarmasses m∗ than red centrals with similar luminosity (see,e.g., figure 3 of PCS21 and figure 4 of Paranjape et al. 2015).The decrease in m∗ from red to blue objects is accompaniedby an increase in fegas due to baryonic mass conservation.We saw already, in figure 5, that samples with higher fegastend to break away from the smooth, MOND-inspired RARfunctional forms at ultra-low accelerations. Figure 6 alsoshowed that this break is prominent only for galaxies withlow-mass hosts, whose median RAR can reach the ultra-low-acceleration regime. Not surprisingly, then, we see in the leftpanels of figure 11 that faint blue centrals trace out exactlythe same break, which is correspondingly absent for faintred objects. At higher luminosity, the corresponding halomasses are higher, so that the median RAR does not reachthe ultra-low acceleration regime, leading to identical RARsfor red and blue galaxies as discussed above.

4.5.5 Sampling

All our results above have been based on an arbitrarilychosen sampling of the rotation curve of each galaxy, using 20logarithmically spaced values of r in the range (0.001, 1)×Rvir

(section 4.1). Since observed rotation curves are typicallyinhomogeneous in the available sampling (e.g., Lelli et al.2017), it is important to check what role sampling plays inestablishing the median RAR and its scatter. We test thisin figure 12 by comparing our default results with thoseobtained using a different sampling choice, now using 40linearly spaced points in the same range (0.001, 1) × Rvir

for each galaxy. Visually, the resulting histogram is verydifferent from the default case, being over-sampled at ultra-low accelerations and under-sampled at high accelerations (as

Figure 12. RAR and rotation curve sampling. (Top panel:)

Identical to top panel of figure 2, i.e. using rotation curves sampledwith 20 logarithmically spaced points between (0.001, 1)×Rvir foreach galaxy. (Bottom panel:) Same as top panel, but sampling eachrotation curve using 40 linearly spaced points between (0.001, 1)×Rvir. The density of points in different parts of the RAR dependsstrongly on the sampling of the rotation curves, but the medianrelation and its scatter are relatively insensitive to this choice.

expected from the fact that the linear sampling decreases thenumber of available points in the inner halo). Encouragingly,though, the median RAR as well as the scatter are relativelyunaffected across the entire range of abary (nearly 6 orders ofmagnitude) probed in the plot. We conclude that samplingchoices are not expected to be a major source of systematicuncertainty in the median and scatter of the RAR.

5 BARYONIC TULLY-FISHER RELATION

The RAR is closely linked with the so-called baryonic Tully-Fisher relation (BTFR, McGaugh et al. 2000) Mbary ∝ V αc ,which generalises the classical Tully-Fisher relation m∗ ∝ V αc(Tully & Fisher 1977) to include the mass in cold gas inaddition to stellar mass. The BTFR with a slope α = 4 anda small scatter ∼ 0.1 dex has been shown to be valid overa wide dynamic range of baryonic mass for gas-rich spiralgalaxies (e.g., Lelli et al. 2016b, although see below).

The quantity Vc in the BTFR is an estimate of thecircular velocity in the outer parts of the galaxy and is meantto be a proxy for the total matter content of each system.The precise definition of Vc has been the subject of somediscussion, and the inferred slope α of the BTFR is rathersensitive to the assumed definition of Vc (Bradford et al.2016). Defining Vc at some fixed multiple of the disk scalelength typically leads to α ' 3, while defining Vc in the‘flat part’ of the rotation curve (see below), typically yieldssteeper slopes α ' 4 (Brook et al. 2016; Lelli et al. 2019).It has been argued (Lelli et al. 2019) that the latter mustbe more fundamental, since the associated scatter in theBTFR is reduced as compared to that when using Vc tied tothe disk scale length. It has been further argued (Wheeleret al. 2019), that the RAR in the low-acceleration regime

MNRAS 000, 1–20 (0000)

14 Paranjape & Sheth

Figure 13. Baryonic Tully-Fisher relation (BTFR) using the default baryonification model. Coloured histograms show thedistribution of Mbary, the ‘baryonic’ (i.e., stellar + cold gas) mass from equation (7) against circular velocity Vc defined in different ways.

Left panels use Vc measured at r = 2Rh,bary (where Rh,bary is the baryonic half-mass radius), while right panels use Vc calculated as themean circular velocity in the flat part of the rotation curve using the algorithm of Lelli et al. (2016b). Note the difference in the range ofthe colour bars in the left and right panels. Top panels show ‘bulge-dominated’ galaxies with mHi = 0, while bottom panels show gas-rich

‘spiral’ galaxies with mgas/m∗ ≥ 0.72 (here, mgas = 1.33mHi). The threshold value is chosen as described in the caption of figure 4. Solidyellow curve in each panel shows the median Vc in bins of baryonic mass. Dashed yellow curves show the corresponding 16th and 84th

percentiles (i.e., the horizontal scatter). For comparison, the purple lines (repeated in each panel) show the observed relations using

Vc measured in the flat part of the rotation curve from McGaugh et al. (2000, dotted: Mbary ∝ V 3.98c ) and Lelli et al. (2019, dashed:

Mbary ∝ V 3.85c ) and using Vc measured at twice the observed half-light radius from Lelli et al. (2019, solid: Mbary ∝ V 3.14

c ). We see thatspirals in the mock catalog have steeper BTFR slopes as well as higher normalisations than bulge-dominated galaxies, with the effect

being more pronounced when Vc is measured in the flat part of the rotation curve.

10−12 m s−2 . abary . 10−10 m s−2 is a simple algebraicconsequence of a BTFR with slope α = 4, so that modelswhich satisfy the BTFR with this slope are guaranteed tofollow the observed RAR at low accelerations.

In this section, we use our mock galaxies to place theabove results in the context of the analytical and numericalarguments concerning the RAR from the preceding sections.Figure 13 shows the BTFR for our mock centrals using thedefault baryonification scheme and the same definition ofMbary (equation 7) used in figure 4.

The top panels of the figure focus on pure bulge-likegalaxies while the bottom panels show results for gas-rich,disk-dominated systems, with the split being identical to theone used in figure 4. The left panels show results when Vc

is defined as Vc = vrot(r = 2Rh,bary), where Rh,bary is thebaryonic half-mass radius, i.e. at the same location as usedto calculate Mbary. In the right panels, we follow Lelli et al.(2016b) and discard galaxies for which the ‘outermost’ partof the rotation curve is either rising or falling too steeply.We pick r = 0.3Rvir ' 20Rhl as the outermost measuredradius and define the threshold steepness by requiring thatsuccessive residuals between vrot(r) at smaller radii andthe mean vrot in the outermost region be smaller than 2%.In other words, we implement the iterative algorithm ofLelli et al. (2016b) with a threshold of 2% instead of the5% those authors used. We only use galaxies with at least3 usable values of r, which was also done by Lelli et al.(2016b). Another detail is that we perform this exercise on alinearly sampled grid of r values containing 6 points between

(0.01, 0.3)×Rvir. The resulting mean value of vrot is then anestimate of Vc(flat), the circular velocity in the ‘flat part’ ofthe rotation curve. The sample of galaxies selected by thisanalysis is an order of magnitude smaller than the one usedin the left panels.

We will shortly discuss the dependence of our resultson the (admittedly arbitrary) technical choices in measur-ing Vc(flat). We first note, however, that each of our mocksamples defines a reasonably tight BTFR in figure 13.9 Acloser comparison with results from the literature (purplelines) shows that (a) the BTFR of pure bulges has a slopeclose to α ' 3, decidedly shallower than that of gas-richspirals which are closer to α ' 4. Focusing on the latter(i.e., the lower panels), we also see some hint at the highestmasses that using Vc(flat) leads to a slightly steeper slopethan Vc(2Rh,bary). The horizontal scatter around the medianrelation is ∼ 0.055 dex.

These trends are easily understood. For all objects, both

9 Due to their luminosity-complete nature, our mocks are complete

in stellar and Hi mass only for thresholds m∗ & 109.85h−2Mand mHi & 109.7h−2M (see PCS21 for details), which leads to

a somewhat complicated completeness threshold as a function

of Mbary. In order to avoid the resulting Malmquist bias effectsin characterising the BTFR, throughout this section we reportresults in bins of Mbary rather than Vc. See Bradford et al. (2016);

Lelli et al. (2019) for a discussion of the complications in fittingBTFR slopes to observed data which, in addition to selection

effects, also have errors on both variables.

MNRAS 000, 1–20 (0000)

RAR in LCDM 15

Figure 14. BTFR and rotation curve sampling. Same asright panels of figure 13, except that the rotation curves were

sampled with twice the number of points. We see that the BTFR,especially of massive spirals, is shallower than in figure 13, andmany more galaxies are included in the relation. The BTFR inthe flat part of the rotation curve is hence sensitive to sampling

choices.

the stellar mass and Vc(flat) are tightly correlated with halomass. For bulges, mbary on the relevant scales is dominatedby the stellar component, so the curvature in the top panel,which results in a shallower effective slope, is a consequence ofthe curvature in the m∗-mhalo relation. Spirals in our mockshave the same m∗-mhalo relation, but because our mockshave Hi gas fractions decreasing with mass, spirals do notprobe the higher halo masses where the curvature matters.This is why their m∗ − Vc relation appears to be steeper.Adding the Hi mass to m∗, so as to obtain mbary, lifts therelation for spirals above that for the bulges, bringing themcloser to the observed BTFR.

Thus far, our default BTFR results are in reasonableagreement with observations. This is already interesting, be-cause a comparison with figure 4 shows that, although gas-rich spirals with α ' 4 do fall on the observed RAR in thelow-acceleration regime, so do pure bulges with α ' 3. Inother words, while being on the BTFR may guarantee beingon the RAR (Wheeler et al. 2019), the RAR is obeyed bya much wider class of galaxies. This implies, firstly, thatstatements such as ‘the RAR is a natural consequence of theBTFR’, which suggest that the BTFR is more fundamen-tal than the RAR, must be treated with caution. Secondly,the converse is also not true in our mocks: galaxies whichcontribute to the low-acceleration RAR need not obey theBTFR with α = 4 (compare the upper panels of figures 4and 13), in contrast with some claims in the literature (see,e.g., the discussion in section 7.1 of Lelli et al. 2017).

Things become even more interesting when one startsto question the various technical choices used in defining‘good’ rotation curves. Figure 14 shows the results of an

exercise identical to the one described above in estimatingVc(flat), with the only difference being that we now used alinearly spaced array of r values with 12 instead of 6 pointsin the range (0.01, 0.3)×Rvir, without changing the flatnessthreshold of 2%. It is obvious, upon some thought, thatthis change will relax the flatness restriction and allow moregalaxies to be used in the sample containing valid Vc(flat)values. We see that the resulting BTFR for both bulges aswell as spirals are now very different from those in the rightpanels of figure 13. In particular, the BTFR of spirals is nowconsistent with α ' 3. We have checked that similar resultsare obtained upon relaxing the flatness threshold to 5% forour default sampling, as well as when modifying the definitionof the ‘flat part’ to use analytical slopes d ln vrot/d ln r inconstraining the degree of flatness. Considering the lack ofhomogeneity of rotation curve sampling in (otherwise veryhigh quality) data-bases such as SPARC (Lelli et al. 2016a)which has been used in many recent BTFR analyses, ourmock results call for a great deal of caution in interpretinga BTFR analysis in the context of competing gravitationaltheories.

In contrast, the differences seen in the median RAR andits scatter in figure 12 due to (rather dramatic) changes insampling the rotation curves are relatively minor in com-parison. The RAR is therefore a much more observationallyrobust probe of the nature of gravity at galactic scales thanis the BTFR.

6 CONCLUSIONS

We have presented new analytical insights into the structureand origin of the radial acceleration relation (RAR) betweenthe total (atot) and baryonic (abary) centripetal accelerationprofiles of galaxies in the ΛCDM framework.

Our key result follows from the realisation (section 3)that the residual mass discrepancy ∆a (equation 3) is com-pletely determined, with essentially no scatter, by the ratioξ (equation 9) governing the quasi-adiabatic relaxation ofdark matter in the presence of baryons in any galactic halopotential, through equation (13). Since the physics of thisrelaxation can be approximated using simple fitting func-tions from the literature (equation 10), our framework allowsus to analytically estimate both the median and scatter ofthe RAR (∆a as a function of abary) in quantitative detailover a wide dynamic range in galaxy and halo properties(Appendix A).

We augmented our analytical calculations with measure-ments of the RAR in a realistic mock catalog of ∼ 342, 000low-redshift central galaxies with ‘baryonified’ host halosproduced using the algorithm of Paranjape et al. (2021,PCS21) (sections 2 and 4). We studied three regimes ofabary: (i) high-acceleration (abary & 10−10 m s−2), (ii) low-acceleration (10−12 m s−2 . abary . 10−10 m s−2) and (iii)ultra-low-acceleration (abary . 10−12 m s−2). Our main re-sults can be summarized as follows.

• The median RAR resulting from applying the relaxationprescription of Schneider & Teyssier (2015, ST15) – i.e.,setting the relaxation parameter qrdm = 0.68 in equation (10)– to our mock galaxies is within ∼ 20% of the observedrelation at low and high accelerations abary & 10−12 m s−2

(figure 2). Since the ST15 prescription and value of qrdmwere only tuned to reproduce the relaxation seen in halosin hydrodynamical CDM simulations, with no reference to

MNRAS 000, 1–20 (0000)

16 Paranjape & Sheth

the RAR, this quantitative agreement over more than fourorders of magnitude in abary represents a non-trivial successof the galaxy-dark matter association in ΛCDM.

• This agreement is particularly remarkable in the high-acceleration regime, where we showed that the median andscatter of the RAR are both very sensitive to the valueof qrdm, and there is no a priori reason why the valueqrdm = 0.68 should have worked (figure 3 and Appendix A1,see also below). In this context, we also noted that there ispresently some ambiguity in characterising the observed high-acceleration median RAR derived from rotation-supportedand dispersion-supported galaxies (e.g. Janz et al. 2016), withthe former being possibly lower than the latter (Chae et al.2019). Adjusting qrdm can track such differences (figure A1),suggesting that high-acceleration RAR observations mightplace useful constraints on the physics of quasi-adiabaticrelaxation, and hence on baryonic feedback prescriptions em-ployed in cosmological hydrodynamical simulations (althoughsee below).

• The median RAR in the ultra-low-acceleration regime isvery sensitive to the expelled (or diffuse) gas fraction fegas,and our default model predicts a distinctive break fromsmooth, MOND-inspired relations at abary . 10−12 m s−2 fordiffuse gas-rich systems (figure 5). This regime, correspondingto the outskirts of halos hosting sub-L∗ galaxies, is currentlyunobserved, although future observations of the CGM couldbe promising in this regard (e.g., Cantalupo et al. 2014;Werk et al. 2014; Zahedy et al. 2019). Our results at ultra-low-accelerations constitute robust and testable predictions ofthe ΛCDM framework.

• While the median RAR is set by a combination of baryon-dark matter scalings and relaxation physics (sections 4.2and 4.3, figures 4 and 5), we identified the primary sourceof scatter in the RAR to be host halo mass and concentra-tion, with a magnitude that depends on the value of therelaxation parameter qrdm (section 4.4 and Appendix A1, fig-ures 3 and 6). Specifically, the scatter in the high-acceleration,baryon-dominated regime is small when qrdm → 1 (perfect an-gular momentum conservation) and increases when qrdm → 0(no baryonic backreaction). So, in ΛCDM, the real puzzleposed by the observed tightness of the RAR is: Why is qrdmcloser to 1 than to 0, with small scatter, over the relevantmass range?

• We used our mock galaxies to explore the sensitivityof the RAR to a number of details such as sample selec-tion, rotation curve measurement technicalities, as well asvariations in baryon-dark matter scalings and halo profileshape (section 4.5). For example, we showed that the effectof a potential correlation between the stellar bulge size andhalo concentration on the median RAR at high abary can bedegenerate with that of changing qrdm (section 4.5.3), whichmust be kept in mind if the RAR is to constrain feedbackphysics as mentioned above. Our framework also provides anatural explanation for the observed offset (Tian et al. 2020)between the RAR of cluster BCGs and fainter centrals (fig-ure 10 and section 4.5.4), while predicting that the RAR isrelatively stable against variations in the chosen form of the‘un-baryonified’ dark matter profile (NFW versus Einasto;section 4.5.1) or technicalities of rotation curve sampling(section 4.5.5).

• In contrast, we argued in section 5 that the baryonicTully-Fisher relation (BTFR) is substantially more suscep-tible to such technical details. As such, the RAR is a much

more robust probe of galactic-scale gravitational physics thanis the BTFR.

The intrinsic scatter of the RAR as inferred from ob-servations, after accounting for all sources of measurementerror, is a matter of considerable interest and discussion. Ifthis scatter is indeed negligible, as reported by Lelli et al.(2017), it would pose a major challenge to the galaxy-darkmatter association assumed in the ΛCDM paradigm. Therobustness of this claim of a zero scatter RAR, however,remains debated (Rodrigues et al. 2018; Stone & Courteau2019; Marra et al. 2020). E.g., Stone & Courteau (2019)estimate an intrinsic scatter in the atot-abary relation (theyfocus on the stellar contribution to abary) of 0.11 ± 0.02dex, fully consistent with our results above as well as thosefrom the earlier ΛCDM literature (e.g., Keller & Wadsley2017). Uncertainties in observed rotation curves might alsobe sensitive to technical details of extracting velocity profiles(e.g., Sellwood et al. 2021). Finally, observational RAR (andBTFR) analyses often focus on ‘good’ samples of inhomo-geneously selected rotation curves (e.g., Lelli et al. 2016b,2017), making a direct comparison between predicted andobserved scatter difficult.

Our results above therefore suggest that the medianRAR (i.e., ∆a as a function of abary), especially in theregimes of high and ultra-low accelerations, is likely to bethe most powerful discriminator between alternative gravi-tational models, as well as serving to constrain the physicsof baryon-dark matter interactions in ΛCDM. For this tobe successful, it will be important to perform observationalanalyses with well-defined, representative galaxy samples.

ACKNOWLEDGEMENTS

AP thanks R. Srianand and Sowgat Muzahid for useful dis-cussions. We thank Kyu Chae for comments on an earlierdraft, and our anonymous referee for an insightful report.The research of AP is supported by the Associateship Schemeof ICTP, Trieste and the Ramanujan Fellowship awardedby the Department of Science and Technology, Governmentof India. This work made extensive use of the open sourcecomputing packages NumPy (Van Der Walt et al. 2011),10

SciPy (Virtanen et al. 2020),11 Matplotlib (Hunter 2007)12

and Jupyter Notebook.13

DATA AVAILABILITY

The mock catalogs underlying this work will be made avail-able upon reasonable request to the authors.

REFERENCES

Abadi M. G., Navarro J. F., Fardal M., Babul A., Steinmetz M.,2010, MNRAS, 407, 435

Arico G., Angulo R. E., Hernandez-Monteagudo C., Contreras

S., Zennaro M., Pellejero-Ibanez M., Rosas-Guevara Y., 2020,MNRAS, 495, 4800

10 http://www.numpy.org11 http://www.scipy.org12 https://matplotlib.org/13 https://jupyter.org

MNRAS 000, 1–20 (0000)

RAR in LCDM 17

Arico G., Angulo R. E., Hernandez-Monteagudo C., Contreras S.,Zennaro M., 2021, MNRAS, 503, 3596

Behroozi P. S., Wechsler R. H., Wu H.-Y., 2013, ApJ, 762, 109

Bertone G., Tait T. M. P., 2018, Nature, 562, 51

Bradford J. D., Geha M. C., van den Bosch F. C., 2016, ApJ, 832,11

Brook C. B., Santos-Santos I., Stinson G., 2016, MNRAS, 459,638

Cantalupo S., Arrigoni-Battaia F., Prochaska J. X., Hennawi J. F.,

Madau P., 2014, Nature, 506, 63

Cardone V. F., Piedipalumbo E., Tortora C., 2005, MNRAS, 358,

1325

Cataldi P., Pedrosa S. E., Tissera P. B., Artale M. C., 2021,MNRAS, 501, 5679

Chae K.-H., Bernardi M., Sheth R. K., Gong I.-T., 2019, ApJ,

877, 18

Chae K.-H., Lelli F., Desmond H., McGaugh S. S., Li P., Schombert

J. M., 2020, ApJ, 904, 51

Chisari N. E., et al., 2018, MNRAS, 480, 3962

Chua K. T. E., Pillepich A., Vogelsberger M., Hernquist L., 2019,

MNRAS, 484, 476

Courteau S., Dutton A. A., van den Bosch F. C., MacArthur L. A.,Dekel A., McIntosh D. H., Dale D. A., 2007, ApJ, 671, 203

Desmond H., 2017, MNRAS, 464, 4160

Desmond H., Wechsler R. H., 2015, MNRAS, 454, 322

Di Paolo C., Salucci P., Fontaine J. P., 2019, ApJ, 873, 106

Diemer B., Kravtsov A. V., 2015, ApJ, 799, 108

Dubinski J., 1994, ApJ, 431, 617

Dutton A. A., Maccio A. V., 2014, MNRAS, 441, 3359

Einasto J., 1965, Trudy Astrofizicheskogo Instituta Alma-Ata, 5,

87

Faber S. M., Jackson R. E., 1976, ApJ, 204, 668

Famaey B., Binney J., 2005, MNRAS, 363, 603

Garaldi E., Romano-Dıaz E., Porciani C., Pawlowski M. S., 2018,

Phys. Rev. Lett., 120, 261301

Giovanelli R., et al., 2005, AJ, 130, 2598

Grudic M. Y., Boylan-Kolchin M., Faucher-Giguere C.-A., HopkinsP. F., 2020, MNRAS, 496, L127

Hunter J. D., 2007, Computing In Science & Engineering, 9, 90

Janz J., Cappellari M., Romanowsky A. J., Ciotti L., Alabi A.,Forbes D. A., 2016, MNRAS, 461, 2367

Kazantzidis S., Kravtsov A. V., Zentner A. R., Allgood B., NagaiD., Moore B., 2004, ApJ, 611, L73

Keeton C. R., 2001, ApJ, 561, 46

Keller B. W., Wadsley J. W., 2017, ApJ, 835, L17

Klypin A., Yepes G., Gottlober S., Prada F., Heß S., 2016, MNRAS,457, 4340

Komatsu E., et al., 2011, ApJS, 192, 18

Kravtsov A. V., 2013, ApJ, 764, L31

Lelli F., McGaugh S. S., Schombert J. M., 2016a, AJ, 152, 157

Lelli F., McGaugh S. S., Schombert J. M., 2016b, ApJ, 816, L14

Lelli F., McGaugh S. S., Schombert J. M., Pawlowski M. S., 2017,ApJ, 836, 152

Lelli F., McGaugh S. S., Schombert J. M., Desmond H., Katz H.,

2019, MNRAS, 484, 3267

Ludlow A. D., et al., 2017, Phys. Rev. Lett., 118, 161103

Marra V., Rodrigues D. C., de Almeida A. O. F., 2020, MNRAS,

494, 2875

McGaugh S., 1999, in Merritt D. R., Valluri M., Sellwood J. A.,

eds, Astronomical Society of the Pacific Conference SeriesVol. 182, Galaxy Dynamics - A Rutgers Symposium. p. 528

(arXiv:astro-ph/9812327)

McGaugh S. S., 2015, Canadian Journal of Physics, 93, 250

McGaugh S. S., Schombert J. M., Bothun G. D., de Blok W. J. G.,

2000, ApJ, 533, L99

McGaugh S. S., Lelli F., Schombert J. M., 2016, Phys. Rev. Lett.,

117, 201101

Milgrom M., 1983, ApJ, 270, 365

Milgrom M., 2016, arXiv e-prints, p. arXiv:1610.07538

Mo H. J., Mao S., White S. D. M., 1998, MNRAS, 295, 319

Navarro J. F., Frenk C. S., White S. D. M., 1996, ApJ, 462, 563

Navarro J. F., Benıtez-Llambay A., Fattahi A., Frenk C. S., LudlowA. D., Oman K. A., Schaller M., Theuns T., 2017, MNRAS,

471, 1841

Oman K. A., Brouwer M. M., Ludlow A. D., Navarro J. F., 2020,

arXiv e-prints, p. arXiv:2006.06700

Paranjape A., Alam S., 2020, MNRAS, 495, 3233

Paranjape A., Kovac K., Hartley W. G., Pahwa I., 2015, MNRAS,454, 3030

Paranjape A., Choudhury T. R., Sheth R. K., 2021, MNRAS, 503,4147

Paul N., Choudhury T. R., Paranjape A., 2018, MNRAS, 479,1627

Paul N., Pahwa I., Paranjape A., 2019, MNRAS, 488, 1220

Pradyumna S., Gupta S., Seeram S., Desai S., 2021, Physics of

the Dark Universe, 31, 100765

Retana-Montenegro E., van Hese E., Gentile G., Baes M., Frutos-

Alfaro F., 2012, A&A, 540, A70

Rodrigues D. C., Marra V., del Popolo A., Davari Z., 2018, Nature

Astronomy, 2, 668

Sales L. V., et al., 2017, MNRAS, 464, 2419

Salucci P., 2019, A&A Rev., 27, 2

Sanders R. H., Noordermeer E., 2007, MNRAS, 379, 702

Schneider A., Teyssier R., 2015, J. Cosmology Astropart. Phys.,

2015, 049

Sellwood J. A., Spekkens K., Eckel C. S., 2021, arXiv e-prints, p.arXiv:2101.02525

Sheth R. K., Hui L., Diaferio A., Scoccimarro R., 2001, MNRAS,

325, 1288

Sorce J. G., Guo Q., 2016, MNRAS, 458, 2667

Springel V., 2005, MNRAS, 364, 1105

Stone C., Courteau S., 2019, ApJ, 882, 6

Tenneti A., Mao Y.-Y., Croft R. A. C., Di Matteo T., Kosowsky

A., Zago F., Zentner A. R., 2018, MNRAS, 474, 3125

Teyssier R., Moore B., Martizzi D., Dubois Y., Mayer L., 2011,

MNRAS, 414, 195

Tian Y., Umetsu K., Ko C.-M., Donahue M., Chiu I. N., 2020,

ApJ, 896, 70

Tully R. B., Fisher J. R., 1977, A&A, 500, 105

Van Der Walt S., Colbert S. C., Varoquaux G., 2011, preprint,

(arXiv:1102.1523)

Virtanen P., et al., 2020, Nature Methods, 17, 261

Wang J., Koribalski B. S., Serra P., van der Hulst T., Roychowd-hury S., Kamphuis P., Chengalur J. N., 2016, MNRAS, 460,

2143

Werk J. K., et al., 2014, ApJ, 792, 8

Wheeler C., Hopkins P. F., Dore O., 2019, ApJ, 882, 46

York D. G., et al., 2000, AJ, 120, 1579

Zahedy F. S., Chen H.-W., Johnson S. D., Pierce R. M., Rauch

M., Huang Y.-H., Weiner B. J., Gauthier J.-R., 2019, MNRAS,484, 2257

Zehavi I., et al., 2011, ApJ, 736, 59

van den Bosch F. C., Dalcanton J. J., 2000, ApJ, 534, 146

APPENDIX A: ANALYTIC RELAXATION

Solving the relaxation problem boils down to describinghow the final radius r is related to the initial radius rin.Equation (10) of the main text considers a model in which

r

rin− 1 = qrdm

(mudm(< rin)

mtot(< r)− 1

)(A1)

where mudm(< rin) and mtot(< r) are the initial and fi-nal enclosed mass profiles. Equation (6) says mtot(< r) =mbary(< r) + frdmmudm(< rin) where frdm is the relaxeddark matter fraction (typically one sets frdm = 1− Ωb/Ωm).In the main text, this problem was treated numerically. Themain purpose of this Appendix is to show that, for judicious(but realistic) choices of the profile shapes mudm(< rin) and

MNRAS 000, 1–20 (0000)

18 Paranjape & Sheth

mbary(< r), much of the analysis can be done analytically.We assume spherical symmetry in what follows.

Previous analytic work (e.g. Keeton 2001) exploits thefact that, for simple choices of mbary(< r), the relaxationequation can be solved analytically for any mudm(< rin).This is attractive since, in practice, mudm(< rin) is unknown,so this is the quantity which one hopes to determine from de-tailed observations of the baryons. In effect, such approachessolve for r as a function of mudm(< rin), and hence of rin.However, as we show below, for appreciating what sets theshape of the RAR, it is more illuminating to determinethe inverse of this relation: rin as a function of mbary(< r)and hence of r. Below, we exploit the fact that, for simplechoices of mudm(< rin) the post-relaxation profile can bewritten analytically for any mbary(< r). If, in addition, theabary(r) ≡ Gmbary(< r)/r2 vs r relation can be invertedanalytically, the result will be a fully analytic expression forthe RAR from baryonic relaxation.

A1 The RAR for an initially Hernquist profile

The main text used the NFW functional form to describe theinitial profile, but also showed that an appropriately scaledEinasto profile gave very similar results. Since the preciseparametrization does not matter, it is natural to ask if thereis a parametrization which simplifies the analysis. For scalesrin < Rvir, the NFW model is very well approximated by aHernquist profile, for which

mudm(rin) ≡ mudm(< rin)

mvir=

(rinRvir

Rvir + r1rin + r1

)2

, (A2)

provided that one sets

c1 ≡ Rvir/r1 = c0.75NFW/√

2 (A3)

(Sheth et al. 2001). (Below, we will use the tilde to indicatemass profiles normalised by mvir.)

With equation (A2) for min(rin), the relaxation equationreads

qrdmξ3

=

[1− 1− qrdm

ξ

] [mbary(r)

(1 + 1/c1)2

(1

ξ+Rvir

rc1

)2

+frdmξ2

],

(A4)where ξ ≡ r/rin as in the main text and we definedmbary(r) ≡ mbary(< r)/mvir. This is a cubic equation for ξwhich can be solved analytically. Since all the coefficients arereal, there is at least one real root. This root is given by

1

ξ= S − Q

S− a2, (A5)

where

S =[P +

√P 2 +Q3

]1/3,

P =3a1a2 − a0

2− a32 and Q = a1 − a22,

with

a2 =2µ (1− qrdm)/(r/r1)− (µ+ frdm)

3a3,

a1 =µ

r/r1

(1− qrdm)/(r/r1)− 2

3a3,

a0 = − µ

(r/r1)2 a3and a3 = qrdm + (1− qrdm)(µ+ frdm),

and µ ≡ mbary(r)/(1 + 1/c1)2. This follows from rearrangingthe cubic equation to a0 + 3a1x + 3a2x

2 + x3 = 0 and

Figure A1. Dependence of the RAR relations, determined fromsolving the cubic equation (A4) with mbary(r) from PCS21, on therelaxation parameter qrdm = 0, 1/3, 2/3, 1 (solid coloured lines),

for three choices of halo mass and associated halo concentration(top, middle and bottom panels; the corresponding mass profileswere shown in the left panel of figure 1). The thicker and thinner

segments of these curves show scales smaller and larger, respec-tively, than that of the scale radius hHi of the Hi gas. Thick dashedblack curve in each panel shows the exact numerical solution usingqrdm = 0.68. Thick solid purple curve (repeated in each panel)

shows equation (19) with ν = 0.8. Dotted purple curves in the

bottom panel show the associated scalings at large and small abary.Thick dot-dashed purple curve in the middle panel shows equa-

tion (19) with ν = 1. Thin dashed black curves show the predicted

scalings for qrdm = 1 and qrdm = 0 (respectively, equation A6and A8) in the baryon-dominated regime.

assumes a3 6= 0 which is guaranteed if q ≤ 1. Inserting this ξin equation (15) yields the RAR for any input mbary(r).

Figure A1 shows the result if we use the mbary(r) pro-files (i.e. the sum of the stellar, Hi, bound and expelled gasprofiles) returned by the baryonification procedure of PCS21for the three representative halo masses shown in figure 1.Note how the RAR changes as the quasi-adiabatic relaxationparameter varies (solid coloured curves) from qrdm = 0 (noconservation) to qrdm = 1 (exact conservation). For com-parison, the thick dashed black curve in each panel showsthe numerical solution for each (NFW-based) galaxy usingqrdm = 0.68; for the intermediate-mass halo, this is ratherwell-described by equation (19) with ν = 0.8. Interestingly,

MNRAS 000, 1–20 (0000)

RAR in LCDM 19

the qrdm = 1/3 curve for the same object is equally well-described by equation (19) with ν = 1, which we discussin the main text. For the two larger halo masses, the RARturns over in the outer regions (small abary). This is because∆a = mrdm/mbary and figure 1 shows that mbary increasesmore steeply than mrdm in the outer regions of massive halos.Note also that, at the smallest halo mass, the RAR flat-tens out, so that it lies below the scaling associated withMbary ∝ V 4

c for which atot ∝ a1/2bary. These results are in goodquantitative agreement with those shown in the main text.

Before moving on, it is worth noting that the structureof the cubic (equation A4) makes it easy to understand howthe RAR depends on qrdm, frdm, c1 and mvir in the baryon-dominated limit. For the PCS21 models with qrdm > 0, thisis where r → 0 and r rin. In this ξ 1 limit, the cubicbecomes ξ−3 ≈ −a0. (Since atot/abary ≥ 1 requires ξ ≥1− qrdm, we treat the qrdm → 0 limit more carefully below.)This is particularly instructive when qrdm = 1, since thena3 = 1 and ξ = (1 + c1)2/3[avir/abary(r)]1/3, where avir =Gmvir/R

2vir. Moreover, we expect frdm ≈ 1, so equation (13)

says that the RAR becomes

∆a ≈frdm (1 + c1)2/3

[abary(r)/avir]1/3when qrdm = 1 , (A6)

where ∆a was defined in equation (3). This scaling is shownas the dashed line in figure A1; clearly, it describes theapproach to baryon domination well.

We now consider qrdm → 0 in the baryon-dominatedregime, which is achieved by sending r, rin → 0 with ξ → 1.For this, it is useful to rewrite equation (A4) as

qrdm = (ξ − 1 + qrdm)

[mbary(r)

(1 + c1)2

(c1 + ξ

Rvir

r

)2

+ frdm

],

(A7)making

∆a = frdm

[mbary(r)

(1 + c1)2

(c1 + ξ

Rvir

r

)2]−1

= frdmmudm(rin)

mbary(r)

(A8)

→ frdm

[abary(r) ξ2

avir (1 + c1)2

]−1

→ frdm (1 + c1)2

abary(r)/avirwhen q → 0.

The second equality on the first line connects with equa-tion (17) of the main text, and the final expression is fromthe rin → 0 and ξ → 1 limits. This has a different dependenceon abary than when qrdm = 1, but the dashed curve in fig-ure A1 (which is almost indistinguishable from the magentapart of the qrdm = 0 curve) shows that it describes this limitwell.

Notice that the quantity avir (1 + c1)2 plays a key role.Since all halos have density 200ρcrit,

avirnm s−2

= 0.0032Rvir

100h−1kpc= 0.0053

(mvir

1012h−1M

)1/3

.

(A9)For mvir = 1012h−1M and cvir = 10, typical of halos host-ing galaxies, avir (1 + c1)2 ≈ 0.13 nm s−2. Since frdm ≈ 1,this explains the a0 = 0.12 nm s−2 scale that is usuallyassociated with the RAR from galactic dynamics. Of course,equation (A9) shows that this scale will be larger for clusters.Thus, our analysis shows that the RAR scale depends onhalo mass, concentration and dark matter fraction, but theshape of the RAR in the high-acceleration regime dependson the adiabatic parameter qrdm.

The analysis above is also useful for understanding why

the RAR has small scatter. At fixed frdm (and qrdm) the

scatter comes from m1/3vir (1 + c1)2. However, in ΛCDM, more

massive halos are less concentrated, so averaging over afactor of ∼ 10 in mvir does not lead to large scatter in theRAR. (Of course, variations of order 103 in mvir will bemore significant, which is why the RAR of clusters is offsetfrom that of galaxies – but by much less than a factor of103.) Moreover, equations (A6) and (A8) depend on differentpowers of this combination of mass and concentration; thisexplains why the scatter around the qrdm = 0 relation is largerthan around qrdm = 1 (c.f. figure 3). This leaves variationsin frdm and qrdm as possible additional sources of scatter inthe RAR. However, frdm is expected to have small scatter –and in our mocks it has no scatter (by assumption). So, inΛCDM, the real puzzle posed by the tightness of the RARis: Why is qrdm closer to 1 than to 0, with small scatter, overthe mass range relevant to the RAR of galaxies?

A2 Fully analytic relaxation and the RAR

Although we used mbary(< r) from PCS21 to make figure A1,the analysis of the previous subsection applies to any mbary.This means that, for judicious parameterizations of mbary,it may be possible to provide fully analytic expressions forthe RAR. We now show that this is indeed possible over asubstantial fraction of the halo.

Start with profiles of the form

ρβ(r) ∝ (r/rβ)−β

(1 + r/rβ)4−β, (A10)

which scale as r−β on scales smaller than rβ , and as r−4 onlarger scales. Simulations have shown that it is reasonableto approximate the stellar distribution with β = 2, the Higas with β = 1 and the bound and expelled gas profiles withβ = 0 (but different scale radii rβ). Let

Mβ ≡ 4π

∫ ∞0

dxx2 ρβ(x) (A11)

denote the total mass associated with this profile. Then,provided β < 3, the mass within r is given by

Mβ(< r) = Mβ

(r/rβ

1 + r/rβ

)3−β

. (A12)

If we ignore the two β = 0 components – in practice weassign their mass to the β = 1 component and modify r1 tomatch the profile of their sum – then

mbary(r) ≡ Mbary(r)

mvir= F2

r

r + r2+ F1

r2

(r + r1)2, (A13)

where Fβ ≡ Mβ/Mtot. It is conventional to work not withFβ but with the mass fractions within the virial radius:

fβ ≡Mβ(< Rvir)

mvir= Fβ

(Rvir/rβ

1 +Rvir/rβ

)3−β

. (A14)

Then

Fβ = fβ (1 + rβ/Rvir)3−β and fd ≡ 1−

∑β

fβ . (A15)

Equation (A13) is substantially more realistic, and not muchmore complicated, than the model discussed in AppendixA of Teyssier et al. (2011) in which all the baryons areclubbed into a single component with ρ ∝ r−1. Figure A2illustrates. Thick and thin solid curves compare the actualand approximated mbary profiles for the two halos shown in

MNRAS 000, 1–20 (0000)

20 Paranjape & Sheth

Figure A2. Comparison of the actual profiles (thick curves) withthe analytic approximations (thin curves) described in the text

for the three halos considered in figure A1, with the solid (dashed)curves showing baryonic (initial dark matter) profiles. The colour-

coding is identical to that in figure 1. For all halos, equation (A2)

describes the initial profiles well. Equation (A13) describes mbary

of the least massive halo (blue solid curves) well out to a substantial

fraction of the virial radius, but the gas in the more massive halos

is less centrally concentrated, so the agreement is only good outto the scale radius of the gas (c.f. figure A1).

figure A1. To produce the curves, we set r1 = hHi (the Hidisk scale length) for the lower mass halo and r1 = rs (theNFW scale radius) for the higher mass halo. For the leastmassive halo, the agreement is good over almost the entirehalo, whereas, for the more massive halos, the agreementis good only in the inner regions which are dominated bystars, and out to about the scale radius of the gas. Thickand thin dashed curves show that equation (A2) provides agood description of the initial NFW profile for mudm.

If the baryonic profile is well described by equation (A13)then

abary(r)

avir= F2

R2vir

r(r + r2)+ F1

R2vir

(r + r1)2. (A16)

Equation (A16) shows that, to invert the abary-r relation onemust solve a quartic equation. Therefore, r can be writtenas a complicated but analytic function of abary, which, wheninserted for r in equation (A5) yields ξ as a function ofabary. This ξ(abary), when inserted in equation (15), yields afully analytic expression for the RAR. The accuracy of thisexpression depends on how well equation (A13) approximatesthe true mbary(r). Figure A2 shows that we expect this towork well out to approximately the scale where the gasdominates the baryonic component. As this fully analyticRAR is essentially indistinguishable from that shown by themagenta parts of the curves in figure A1, we have not shownit again.

The procedure just described yields an analytic RARby first finding rin as a function of r, and then writing r as afunction of abary. For mbary(r) given by equation (A13), it isalso possible to do the opposite. I.e., equations (9) and (10)yield a quartic equation for r, which can be solved analyticallyto yield r/rin for any mudm(< rin). This solution for r can

be inserted in equation (A16) to yield abary and then

∆a ≡atotabary

− 1 =frdmmudm(rin)

m2(r) +m1(r)(A17)

=frdmmudm(rin) (1 + r2/r)(1 + r1/r)

2

F2 (1 + r1/r)2 + F1 (1 + r2/r)

yields the corresponding fully analytic (but messy!) RAR.Over the range where equation (A13) provides a good de-scription of mbary(r), the rin(r) and r(rin) approaches arealmost indistinguishable. However, the rin(r) approach, inwhich mudm is given by equation (A2) and mbary is arbitrary,is more efficient (solve a cubic rather than quartic).

We conclude that we have analytic understanding of allthe RAR scalings presented in the main text.

MNRAS 000, 1–20 (0000)