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

3

!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

5

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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