Post on 28-Sep-2020
day2- building detectorsPeter Wittich
Cornell University
recap of matter inter.• MIP: ionization important mechanism for energy
loss; Bethe-Bloch• e/γ: losses via radiation: bremsstrahlung/pair
prodution• multiple scattering described by gaussian approx
with large non-gaussian tails• now: hadrons!
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π’s and other hadrons
• in addition to radiative losses π: strong interactions• interactions with nuclei
⇨large energy deposits, hadron shower
⇨π0 → γ γ: em shower too (!)
⇨nuclear excitations (invisible)
⇨described by a nuclear int. length λa
• λa >X0
⇨leads to later hadr. shower development
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!a =A
NA ! "! #inel
hadron shower• air shower from
high energy cosmic ray
4 Weeks, Phys. Rep. 160 (1988) 1
em vs had• typically X0<λn
⇨hadronic showers start later than electromagnetic showers
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MaterialX0
g/cm2λn
g/cm2
H2 63 52
Al 24 106
Fe 14 132
Pb 6 193
build-a-detector• we know how particles interact with matter• we have some ideas how to identify them
⇨exploit differences in showering, interaction with matter
• we want to know
⇨momentum and charge‣ F = q(v x B)
⇨energy: absorb the particles
⇨species: exploit differences
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revisit
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ν-like
charge and momentum• apply magnetic field:
⇨F = q(v x B)• measure a few points on the
track of the particle• reconstruct curvature of
track, p ~ 1/c
⇨dpT/pT ~ pT
⇨s = Bl2/(13.3 pT)• extract “impact parameter”
⇨d.o.c.a. to beamline
⇨long-lived neutrals, B’s• “do no harm” - material
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Sagitta
L
M. Mannelli
pT ~ curvaturepT ~ L2
gaseous detectors
• traditionally preferred technology for large volume detectors⇨example CDF COT, CLEO
⇨CDF: 30 k wires, 180 μm hit resolution
⇨advantage: low thickness (fraction of X0)• sense and field wires, potential difference• choice of gas, drift times, performance at high occupancy
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COSY experiment
drift region avalanche region
CDF COT• Central Outer Tracker
⇨resolution δpt/pt < 0.1%pt• router ~ 140 cm• thickness 1.7%/X0
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solid-state detectors
• high granularity in dense environment• high speed (25 ns bc)• 1-2% resolution @ 100 GeV• 2ndardy vertex (b and τ )
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N Bulk
P+ implants
Al Strips
+HV
OV+ +
++++++++
- - - - - - - - - --
Oxide
M. Mannelli
D0 SVX• compact
configuration• cooling and
services: large material budget
• SA stereo and axial layers
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calorimeters• goal: measure energy• HEP calorimeters are
total absorption• stop particles• little material before
calorimeter• collect all energy
above some cutoff• lateral and longitudinal
shower shape
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Sampling Calorimeters• passive radiator (high Z)• active detector (low X0)
⇨scintillator
⇨wire chamber• σE/E ~ 10%
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detector
Homogenous Calo• one material to
capture both• fast scintillator,
collect light with optical detector
• σ/E smaller
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detector, light guide
CLEO CsI crystal
calorimeter resolution
• a: stochastic term, photon counting.
• N ~ E,
⇨σ ~ √E
• b: constant term
• σshower ~ E
• c: electronic noise
• σnoise ~ const
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!E
E=
a!E" b" c
E
add terms in quadrature to get final response
em vs had calor
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Weeks, Phys. Rep. 160 (1988) 1
shower development
• longitudinal, lateral width; MIP• particle ID with calorimeter
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Aleph, NIM A 294 (1990) 121
incident particles
π μ e
em calorimeters
• ATLAS, CDF: sampling calorimeters; CMS: homogenous
• material choices
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PbWO4 (CMS) 25 X0 3%,0.5%,.2
Scintillator/Pb (CDF) 17 X0 13.5%, ,
Liquid Ar/Pb (ATLAS) 25 X0 10%,0.4%,.3
PbWO4
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hadron calorimeters• both electromagnetic and hadronic showers
⇨π0→γγ • e/h ratio; compensating and non-compensating• “invisible energy” in hadronic shower
⇨E<Ec
• resolutions typically a lot worse than em case
⇨(a ~ 30-50%++)
⇨due to large fluctuations and e/h‣ for large E not as important (LHC)
⇨repercussions for missing ET, e.g.
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muon detectors• muon signature:
⇨extraordinarily penetrating
• shielding, charged particle• outermost layer• backgrounds:
⇨punch-through, sail-through
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0
5
10
15
20
0 1 2 3 4 5Pseudorapidity
Absorp
tion length
sBarrel tile Hadronic endcap Forward calorimeter
EM barrel EM endcap
Material in front of muon System
End of activehadronic
Cryostat walls
Extended
barrel
tile
ATLAS muon TDR
absorption lengths before μ system
hadron collider trigger
• Eight orders of magnitude between total cross section and Higgs boson xsec
• you can’t keep it all• This is the job of the
trigger (1st half of rejection)
Where you start
Physics you want to reach
at LHC• event collide 40 MHz• you can only save 1 kHz
⇨you reject but one in 40,000 events within a second of them occuring
⇨you don’t get two chances of doing this
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importance of trigger at hadron colliders
• Best trigger is no trigger (take everything)
⇨impossible at hadron colliders
⇨you can’t afford it ($$$ or time-wise)• At hadron colliders, trigger can make or break you
⇨10% increase in trigger efficiency is akin to increasing your luminosity by 10%
⇨as if you ran your accelerator 10% longer
⇨You just saved a lot of money
⇨You just improved your analysis by a lot• w/o a trigger you can’t do the physics
⇨if you didn’t trigger on it, it’s gone forever• Trigger takes up a lot of mental energy
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two examples
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CDF: three tiers
• L1: 1.6 MHz → 30 KHz• synchronous pipeline, • Dedicated HW processors
• L2: 30 KHz → 1 KHz• Asynchronous• Mixture of HW processors
and c++ in CPU• L3: 1 KHz → 100 Hz
• CPU Farm w/full SW• Full event information• Full reconstruction
CMS: two tiers
• L1: 40 MHz→ 100 KHz• Dedicated HW• regional → global
• HLT: 100 KHz→1 kHz• CPU farm• Full event info• special reconstruction• Regional reco
progressive filtering in both cases
Bandwidth, Trigger Rate and Luminosity
• DAQ system’s fundamental currency is bandwidth (MB/s to tape)
• Trigger tries to saturate the bandwidth with the good stuff
• Trigger rate is R(t) = σtrig L(t)• Naively,
• i.e., trigger rate depends linearly on inst. lumi• Real world isn’t so nice
!"trig
!L = 0
Trigger cross sections• Cross sections exhibit “growth terms”
• Example: a CDF μ trigger
⇨One of worst• This is due to junk
⇨“real” physics is flat• Accidentals
⇨More at higher lumi
trig = phys + f (L) + g(L2) + ...
Prescales, or, how to maximize throughput
• inst. luminosity drops during store• bandwidth capacity is constant
⇨Adjust trigger to keep throughput optimized⇨Usual method: change your prescales
• prescale: accept n of m events that pass your trigger criteria. Usually, n = 1.
Luminosity vs time for Tev Store
Results of prescales
• See effect of all these prescale changes during nine hours of this CDF run
UDPS kicksin
Start of store
Rate of L1A’s vs time
Adjust ps
PS=1 for alldynamic ps
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data analysis
• trigger’s goal: collect data. 15 Petabytes/year.• how to analyze?
⇨tiered computing model
⇨worldwide access to computing resources
⇨“the grid” http://lcg.web.cern.ch/LCG/ “Arguably largest computing grid on the planet.”
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central data processing• central first-pass processing
⇨derive calibration and alignment constants
⇨reconstruct e, μ, τ, b, jet candidates‣ ecal + track = electron, e.g.‣ not four-vectors: quality of candidates
⇨split data into streams
⇨distribute to computing centers around the globe for individual physicist access
• repeat periodically
⇨better calibration, alignment, reconstruction
⇨most data reprocessed many times, esp at start.
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all of this is before you start
doing your analysis
particle reconstruction
• take elements from the detector and combine them into a candidate particle
⇨e or μ⇨τ
⇨jet ✔ b jet
⇨global event quantities
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electrons and muons
• find energy deposit in calorimeter
• associate with track (electron) or no track (photon)
• find track in the muon system
• associate with track in the central area
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|!|0 0.5 1 1.5 2 2.5
Effic
ienc
y
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
=100 GeVTp
AllStand-aloneCombined
ATLASe/γ μ
typically: resolution degrades in forward region (here η>2.3)
τ lepton
• reconstruct hadronic τ decays• narrow jets in calorimeter• cones around tracks
⇨τ cone
⇨isolation cone• demand tracks, neutrals in cone• τ ID traditionally more
challenging but very sophisticated at Tevatron and LHC
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τ-→l- νν ~2x17%
τ-→h-ν 49%
τ-→h-h+h-ν 15%
jets• our experimental representation
of partons• conceptually simple
⇨gang together all the particles that result from hadronization
⇨goal: back to the partons• in real world very hard
⇨how to decide which particles come from which hadronization process?
• many attempts
⇨jetclu, iterative cone, kT, … 37
M. Donofrio
example jet algo• Order all charged particles according to their PT.• Start with the highest PT particle and include in
the "jet" all particles within the "radius" ΔR = 0.7 (considering each particle in the order of decreasing PT and recalculating the centroid of the jet after each new particle is added to the jet).
• Go to the next highest PT particle (not already included in a jet) and include in the "jet" all particles (not already included in a jet) within the radius ΔR = 0.7.
• Continue until all particles are in a "jet".
38 (R. Field)
but it gets complicated• Requirements: • colinear and infrared safety to reduce dependence
on details of showering models
⇨allow comparison btw theory and experiment• computationally robust and efficient
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M. Vazquez Acosta
D’Onofrio
heavy flavor jets• heavy flavor = b or c• b-tagging procedures take
advantage of the long life-time of B hadrons
⇨cτ~450 μm
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• select tracks associated w/jet• find those with d0>0 (need 2+)• reconstruct 2ndary vtx• Lxy = distance btw prim and
2ndary vtx, > 0 and sign.
Other technique: decays of B hadrons with e or μ.
missing ET (MET)
• basic definition as above (sum over calorimeter elements, includes clustered and unclustered E)
• correct for muons (MIP’s not absorbed in CAL)• correct for hadronic energy scale• correct for known leakage effects (cracks etc)• Alternative: missing HT (not unique - beware)
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!ET " #!
i
EiTni
MET trigger hard
• anything going wrong produces MET• need careful work to understand samples• but very powerful
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data triggered on MET
summarize• “the machine”
⇨colliders, luminosity• interaction of particles with matter
⇨ionization, bremsstrahlung, nucl. interactions• detector elements
⇨tracking, calorimetry
⇨particle identification• trigger and data collection
⇨the grid
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