Determination of mass hierarchy in ICAL at INO...Mass hierarchy @ INO INO Collaboration...

Mass hierarchy @ INO INO Collaboration

Determination of ν mass hierarchy inICAL at INO

Ali Ajmi1, Abhish Dev2, Mohammad Nizam1,Nitish Nayak2, S. Uma Sankar2

1Homi Bhabha National Institute, Mumbai, India

2Indian Institute of Technology Bombay, Mumbai, India

ANL HEP Division Seminar, 4 Nov. 2015

INO Collaboration, 2015INO Collaboration, 2015

AA, AD, MN, NN, US 1 ANL HEP Division Seminar, 4 Nov. 2015

Mass hierarchy @ INO ICAL@INO INO Collaboration

The INO-ICAL Detector:

� India-based Neutrino Observatory or the INO, an upcomingexperimental facility to house the Iron Calorimeter (ICAL).

� The ICAL aims to study the interactions of atmosphericneutrinos and antineutrinos.

� ICAL is a magnetisedcalorimetric detector, thus able totell apart the µ+s from the µ−s.

� This ensures a promisingdetermination of the ν-masshierarchy.Anushree at al. 10.1007/JHEP04(2013)009



� Laboratory site: cavern with ∼1km rock-covering in all directions.

� Accessible through a ∼1.9 kmlong tunnel.

� Several smaller caverns tofacilitate many experimentalprograms.

� The ICAL is housed in themain cavern.



full unit structure

� ICAL will comprise 3 modules with a total weight of50kton.

� Each module contains 151 horizontal iron layers, 5.6 cm inthickness.

� Interspersed with them: resistive plate chambers (RPCs),total of ∼30,000.

� Each RPC unit spans a surface of 1.84 m × 1.84 m and is2.5 cm in width (height).

� Successive RPC units 9.6 cm apart from each other in thevertical direction.



� Gas Composition used in the RPC:R134A : Isobutane : SF6 = 95.2 : 4.5 : 0.3 %

� RPC µ detection efficiency &98%; time resolution ∼ 1ns.

� The iron layers are magnetised to∼1.3 T.

� This accounts for the curvature in thetracks of the charged particles.

� The reconstruction of these tracks tellsus the momentum and the charge of theparticle, i.e. µ+ or µ−.

� Thus the νµ and ν̄µ events are wellidentified.

Courtesy: Behera et al.

arXiv:1406.3965



Reading the event

� The X strips and Y strips pick up signals independently,during an event.

� Combination of both should define the coordinates of theevent point (hit) on XY plane.

� So, the number of hits = 1, when X and Y pick up onesignal each, in a layer.

� For multiple X and Y strip hits, a straightforwardcombination implies all possible pairs of the (X, Y).

� This leads to overcounting the number of hits, by includingfalse event points in a plane, commonly called “ghost hits”.


Mass hierarchy @ INO Reading the event INO Collaboration

Redefining “hits”:

� The number of strip hits fetched by the X and the Y sideare compared for each layer.

� We consider the maximum of the two numbers as thenumber of hits in that layer.

� This number is summed over all the layers in an ν event,and is finally referred to as the “hits” in the followingstudy.

� Thus the hits excludes ghost hits.� Alternate methods to remove ghost hits are also in process

in the Collaboration.


Mass hierarchy @ INO Events in ICAL INO Collaboration

The ν-interactions in ICAL

� ICAL has a good ability to identify muon tracks� Using reconstruction algorithm, the energy and the

direction of the muons can be determined with goodprecision and charge identification.[Chatterjee et al. 10.1088/1748-0221/9/07/P07001 ( arXiv:1405.7243), Kolahal et al.

10.1016/j.cpc.2014.09.003 (arXiv:1510.02792)]

� The νµCC events incident in thevertical cone, and with significantenergy give such track containingevents.

� Trackless events, i.e. the νeCC,NC, and νµCC(Low energy orHorizontal) are also observable atICAL and form the background.


Mass hierarchy @ INO Talk Today INO Collaboration

Improving the hierarchy sensitivity of ICAL usingneural network

arXiv:1510.02350


Mass hierarchy @ INO Objective INO Collaboration

Objective & Physics Motivation:

� The hierarchy discrimination is most visible for neutrinoenergies above 3 GeV and for upward going neutrinos inthe vertical cone (−1 < cos θ < −0.5).

� To obtain the best hierarchy discrimination, we mustdevelop a procedure to select such events with highefficiency and high purity.

� In the analysis procedure, νµCC events with Eν > 3 or 4GeV and with | cos θ| > 0.4 or 0.5 are designated as signal.All other kinds of events are treated as the background.

� We used adaptive neural network, an application in ROOT,to select the signal events. The variables used tocharacterize an event are chosen based on the expected hitdistribution of the signal events.


Mass hierarchy @ INO Physics Motivation INO Collaboration

Plots of the muon neutrino survival probability Pµµ foratmospheric neutrinos:


Mass hierarchy @ INO Data Specs INO Collaboration

Simulations:

§ 500 years of ICAL data, generated using NUANCE in theenergy range Eν= {0.1,100}GeV.

§ All types of interactions (CC and NC) of all 3 neutrinoflavours (νe, ν̄e, νµ, ν̄µ, ντ , ν̄τ ) are considered.

§ 5 different sets: 1 NOOSC, 3 NH with different seeds, 1 IH.§ The NOOSC set is used for the training of the neural

network.§ The 3 NH and the 1 IH dataset are used for analysis by

applying the trained neural network on them.


Mass hierarchy @ INO Simulations contd INO Collaboration

§ Parameters used for ν-oscillation: ∆m212 = 7.5× 10−5 eV2,

|∆meff2| = 2.47× 10−3 eV2 (i.e. ∆m31

2(NH) = 2.51× 10−3

eV2, ∆m312(IH) = −2.43× 10−3 eV2), sin2 θ12 = 0.31,

sin2 2θ13 = 0.09, sin2 θ23 = 0.5 and δCP = 0.§ The generated ν events propagated in ICAL using Geant4

simulation.§ Use the ICAL reconstruction code to first identify the

muon track and then reconstruct Eµ and cos θµ.§ Calculate the mass hierarchy discrimination from these

measured values of Eµ and cos θµ.


Mass hierarchy @ INO Selection Parameters INO Collaboration

Effective Selection Parameters:

� Consider events with hits in more than five layers (L > 5).� This ensures better reconstruction efficiency of µ tracks� It eliminates most of the background due to the non-νµCC

events⇒ About 90% of the events selected now are νµCC events[Ali Ajmi, S. Uma Sankar 10.1088/1748-0221/10/04/P04006 (arXiv:1510.02350)]

� Employ neural network to select signal events with highefficiency and good purity

� Require appropriate input variables for the neural network� Such variables should distinguish between signal and

background among the νµCC events.



Primary Signal Defining Zones

We divide these events into four subsets based on the neutrinoenergy and direction, using the information from the eventgenerator.

� Signal events: Eν : 4-100 GeV and | cos θz| > 0.5,� High energy horizontal events: Eν : 4-100 GeV and| cos θz| ≤ 0.5,

� Low energy vertical events: Eν : 0.1-4 GeV and| cos θz| > 0.5,

� Low energy horizontal events: Eν : 0.1-4 GeV and| cos θz| ≤ 0.5.


Mass hierarchy @ INO Selection Parameters: 1 INO Collaboration

§ Hits:� This gives some measure of the energy.� Low energy neutrino events give less number of hits

compared to the high energy events.



§ Layers:� An initial cut of 5 layers is included to ensure background

suppression.� The high energy vertical neutrino events give hits in more

number of layers than the low energy/horizontal events.� Muons travel longer distances than hadrons or electrons of

the same energy.Effect of 5layer cut Layers distribution



§ Maximum horizontal spread of an event (maxdist):� The vertical or near-vertical νµCC events spread less in the

horizontal plane than the horizontal events.� The horizontal spread between a pair of hits is given by

D =√

((x2 − x1)2 + (y2 − y1)2)

� We calculate D for every pair of hits in an event and defineits maxdist to be the maximum value of D [ref:arXiv:1510.02350].

� The maxdist is quite large in case of high energy horizontalevents and is moderate for the other three types of events.



The “maxdist” distribution:

Combined Effects 1 Combined Effects 2



§ Singlets:� It’s the number of layers in an event containing single hit.� A vertical νµCC event is expected to contain more singlets

than horizontal νµCC events or the muonless events.



§ Triplets:� It’s the no. of single hits in 3 consecutive layers in an event.� A muon track is comprised of such segments.

Distribution of the single-hit layers in the νµCC events (L>5), combined

with maxdist/layers≤10 and # possible triplets>0, for the NOOSC dataset.



Summarizing the effects of Selection Parameters:

� Hits or layers distinguish the low energy from the highenergy range ν events.

� Maxdist distinguishes the horizontal high energy eventsfrom the rest.

� The high energy vertical νµCC events contain significantlylarger number of singlets than the low energy/ horizontalevents.

� The hits-pattern across the layers in case of the high energyvertical νµCC events form more number of triplets than thethe other three categories of νµCC events considered.

Quantitative


Mass hierarchy @ INO Neural Network Analysis INO Collaboration

Neural Network Analysis:

� Various multivariate tools are tested� We choose to use adaptive neural network (ANN).� We define our signal = νµCC events with (say)

Eν={Es,100}GeV and |cosθ| >##.� So, background = All the rest νµCC events + all non-νµCC

events (i.e., all NCs, νeCC and ντCC).� Train the Network to distinguish between the signal and

the background events.� Apply the learned output to assign probabilities ( 0:bkg.−→ 1:signal ) to any given set of events. An example

� Put a cut on the probability to obtain a signal rich samplewith optimum signal selection efficiency and signal purity.

� Bin the selected events into a favorable binning scheme andcalculate the < ∆χ2 >.


Mass hierarchy @ INO Calculating ν Mass Hierarchy INO Collaboration

Deciding the binning scheme:

X For the selected signal-like events, Eµ and cos θµ arereconstructed.

X The events are sorted into bins of the reconstructed Eµ andcos θµ.

X Matter effect is present only in the up going events.X So, we consider only the up going events, i.e. events with

cos θµ ∈ [0, 1].X For the binning in Eµ, different schemes have been tested.X We restricted the range of muon energies to be (0,17) GeV.X A differential binning scheme in energy is chosen.


Mass hierarchy @ INO Calculating... Deciding the binning scheme: INO Collaboration

X Events most populated in Eµ ∼ 2-3 GeV.X Downgoing events naturally more than the upgoing ones.

Distribution of muon energy and direction (given by NUANCE) of the events

surviving the cut on the neural network probability = 0.7.



Calculation of Mass Hierarchy < ∆χ2 >:

� The oscillation parameters used are as follows:∆m21

2 = 7.5× 10−5 eV2, |∆meff2|=2.47e-3 eV 2, sin2 θ12 =

0.31, sin2 2θ13 = 0.09, sin2 θ23 = 0.5 and δCP = 0.� Determine the value of mass hierarchy < ∆χ2 >:

� We compute 3 values of χ2true as χ2(NH1−NH2),

χ2(NH1−NH3) & χ2(NH2−NH3), and take theiraverage to obtain < χ2

true >. This is expected to betwice the number of bins.

� We compute 3 values of χ2false as χ2(NH1− IH),

χ2(NH2− IH) & χ2(NH3− IH), and take theiraverage to obtain < χ2

false >.� From these we obtain< ∆χ2 > = < χ2

false > − < χ2true >.


Mass hierarchy @ INO Optimising signal definition INO Collaboration

Choice of signal:

� We initially defined the signal events to be νµCC eventswith Eν > 4 GeV and | cos θz| > 0.5.

� But some hierarchy discrimination is present in events withlower energy and smaller | cos θz|.

� So, we systematically lower the minimum values of Eν andcos θz.

� The best hierarchy discrimination sensitivity is obtainedwith Eν > 2 GeV and | cos θz| > 0.2.


Mass hierarchy @ INO Optimising signal definition INO Collaboration

Hierarchy discrimination sensitivity for different signaldefinitions:



Checking the authenticity of the obtained values of < ∆χ2 >:b We estimate the result with an ideal detector (blue), i.e.

with exact reconstruction of momentum and direction.b This reflects the margin to maximisation.b Using the generator level information, we restrict to

including only those events, which have their Eµreconstructed within 30% momentum resolution (magenta).

b We compare these two synthesized plots with the real oneobtained from the ICAL reconstruction code (red).



Comparing < ∆χ2 > plots:



Importance of the neural network analysis:

� The < ∆χ2 > for the differential scheme is always largerthan the uniform scheme.

� For uniform scheme, use of ANN improves < ∆χ2 > from3.5 to 8.

� The differential scheme inherently extracts the differencesbetween the event spectra of NH and IH. ANN leads to asmall improvement in < ∆χ2 > from 7 to 9.



Comparing the effect of neural network and differential binningscheme:


Mass hierarchy @ INO CONCLUSION INO Collaboration

Conclusion:

z We obtained a maximum < ∆χ2 >= 9, i.e. a 3σ hierarchydiscrimination.

z Supposing an ideal detector, with absolute energyreconstruction, very fine direction resolution and perfectcharge identification, one can obtain a < ∆χ2 >∼ 15.

z Therefore improvement in the muon energy reconstructionand charge identification will certainly improve thehierarchy sensitivity of ICAL.



Acknowledgement: We extend our sincere thanks and regards toGobinda Majumder, Indumathi and Amol Dighe for theirinvaluable suggestions. We also thank Tarak Thakore for veryuseful discussions, and all the Collaboration members for theirencouragement and unfailing cooperation.We are certainly grateful to Maury Goodman for inviting me,and all the ANL HEP members for their valuable time.



Thank you !Thank you !



Back ups !



Summarizing the effects of Selection Parameters:

#νµCC eventsSelection Criteria Total #νµCC events | cos θ| >0.5, {4-100}GeVL>0 23.7 × 105 7.8 %L>5 8.96 × 105 18.7 %demand min. 1triplet

5.49 × 105 24.2 %

min. 1 triplet +maxdist/layers≤10

4.48 × 105 27.3 %

min. 1 triplet +maxdist/layers≤10+ #single-hitlayers≥10

1.92 × 105 51.1 %

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Effect of Neural Network on Signal set #2:

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The structure of an RPC unit:

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Effect of initial layer cut:

Selection Criteria νe CC others (NC+ντCC) νµ CC#events: L≥5 84115 73849 683635

∼10% ∼9% ∼81%#events: L≥6 35678 37031 579760

∼5% ∼6% ∼89%

Table 1: Events counts after applying the selection cuts on the Geantoutput for the 500 years of NH data with Eν={0.8,20}GeV.

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Mass hierarchy @ INO Layers Distributions for νµCC events with L>5 INO Collaboration

Layers Distributions for νµCC events with L>5

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Distribution of Maxdist/#hits for νµCC events with L>5

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Distribution of Maxdist/#layers for νµCC events with L>5

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Determination of mass hierarchy in ICAL at INO...Mass hierarchy @ INO INO Collaboration...

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