Calorimeter systems at collider experiments

1. Calorimeter systems at collider experiments Erika Garutti (DESY) erika.garutti@desy.de

2. Outline • From single calorimeter detectors to calorimeter in a detector system • Calorimeters for jets • Particle flow algorithms to improve jet energy resolution • Highly granular calorimeters - techniques for analog and digital calorimetry erika.garutti@desy.de

3. From single calorimeters to a HEP detector Calorimeters are in general one component of a complex detector system ATLAS barrel HCAL and coil CMS ECAL Endcap Typical of collider detector is the onion-like Structure of the detector system erika.garutti@desy.de

4. Detectors for collider experiments CMS • Typical onion-like structure for most of modern collider detectors • - The tracking system comes first (minimum material budget) • The calorimeter stops (most of) the particles so has to come second • Muons can escape the calorimeter and require an extra detector erika.garutti@desy.de

5. Particles are not kind! The distinction between electromagnetic and hadronic calorimeter is not rigorous for a hadron ~30-40% of first hard interaction of a hadron happen in the EM-calo The choice of a high Z material for the EM-calominimizes the hadron interactions before the Had-calo: ~30 X0 to stop an EM shower = 1 lintof Tungsten (W) or 3 lintof Iron (Fe) W Fe erika.garutti@desy.de

6. Particles are not kind! About 11-12 lintare needed to contain hadrons with energy ~100 GeV ~1.2 m of W or 2.2 m of Fe [cm] Fe W The choice of a high Z material for the Had-calo minimizes its depth erika.garutti@desy.de

7. ideal calo  ideal calo system Ideal calorimeter • Calorimeter system requirements • g identification (EM/Had segment.) • separation of jets (lateral segment.) • calo contained inside magnetic coil e- 100 GeV = k x100 GeV p- 100 GeV = k x 100 GeV L • Implications: • e/p = 1 • L 30 X0 && L  11 lint erika.garutti@desy.de

8. Particles are not alone! At collider experiments particles come typically in “jets” • Jets are a collimated group of particles that result from the fragmentation of quarks and gluons • They are measured as clusters in the calorimeter • momentum of cluster is correlated to the momentum of the original quark Why not using tracker (has better resolution)? erika.garutti@desy.de

9. Measure charged + neutralparticles Performance of calorimeters improves with energy DE/E  1/+ const. while in a magnetic spectrometer Dp/p  p Obtain information on energy flow: total (missing) transverse energy, incoming direction (with high segmentation) Obtain information fast(<100ns feasible)  recognize and select interesting events in real time (trigger) Why are jets measured in the calorimeter? At high energy calorimetry is a must magn. spectr. particle E or p [GeV] erika.garutti@desy.de

10. Phenomenology of jets • Partons (quark/gluon) are produced from the interaction of beam particles • Partons fragment into hadrons • Jets clustering algorithm: • Typically uses a geometric assumption to group particles from the same parton (cone) • A fraction of the parton energy can be lost (out of the cluster) Jet = sum of many particles (e,g,p,p,n,K,…) technically: (EEM CAL + EHAD CAL )clusters + muon momentum + Emiss erika.garutti@desy.de

11. Jet versus calorimeter energy scale • Jets are complicated processes • EM and Had Calocalibrations are generally not sufficient to get calibrated jet energy • More work needs to be done!! • Jet energy scale is crucial for many important measurements: • Top quark mass (used to constrain Higgs boson) • Higgs searches / branching ratio • Search for beyond physics the standard model • Measurements often performed by comparing real data with simulations • Need to get both physics and detector simulation right erika.garutti@desy.de

12. Absolute jet energy scale • Response to single particles non-linear (in test beam) • However, jets are identified as one single objects by clustering algorithm • For a 50 GeV jet: calibration is not the same whether: • one 50 GeV pion • 10 times 5 GeVpions or whether: • one 50 GeVp0 or p+/- CMS test beam erika.garutti@desy.de

13. Absolute jet energy scale • Response to single particles non-linear (in test beam) • However, jets are identified as one single objects by clustering algorithm • For a 50 GeV jet: calibration is not the same whether: • one 50 GeV pion • 10 times 5 GeVpions or whether: • one 50 GeVp0 or p+/- • Solution: • Get the average energy scale: Simulate an “average” particle configuration inside jet • Use test beam information to get calibration factor for single particles erika.garutti@desy.de

14. What is inside a jet? There are wide variations to the average particle energy inside a jet … but also on the energy carried by different type of particles in a jet These fluctuations add uncertainty to the jet energy scale determination Eparticle/Ejet ? erika.garutti@desy.de

15. Jet energy resolution at LHC jet jet Stochastic term for hadrons only: ~93% and 42% respectively erika.garutti@desy.de

16. ideal calo  ideal calo system Ideal calorimeter • Calorimeter system requirements • g identification (EM/Had segment.) • separation of jets (lateral segment.) • calo contained inside magnetic coil e- 100 GeV = k x100 GeV p- 100 GeV = k x 100 GeV Calorimeter system e- 100 GeV Sampling calorimeters can have highest density Different material in EM/Had segments Different layer thickness in the same material Extra material (support/cables) between calos different sampling factors p- 100 GeV erika.garutti@desy.de

17. Sampling Method Weights applied to different calorimeter compartments Enlarged cone size yields increased electronic noise H1 Method Weights applied directly to cell energies Better resolution and residual nonlinearities Energy weighting for jets Back-to-back dijet events |h|=0.3 Can the jet energy resolution be better? ATLAS erika.garutti@desy.de

18. Precision jet physics lepton machine (ILC: e+ e- @ 0.5-1 TeV, CLIC:@ 1-3 TeV ) LEP-like detector At the Tera-scale, we will do physics with W’s and Z’s as Belle and Babar do with D+ and Ds Mj1j2 Jet1 Jet2 Jet3 Jet4 Mj3j4 Brqq~70% ILC design goal • Require jet energy resolution improvement by a factor of 2 • Worse jet energy resolution (60%/E) is equivalent to a loss of ~40% lumi Mj1j2 Note due to Breit-Wigner tails best possible separation is 96 % Perfect build a detector with excellent jet energy resolution sjet ~3% LEP-like • reasonable choice for LC jet • energy resolution: • minimal goal sE/E < 3.5 % W Z0 Mj3j4 erika.garutti@desy.de

19. Calorimeter for Particle Flow • Jet energy resolution is worse than (or at most as good as) hadron resolution • [world best: ZEUS HCAL shad~35%/E] • How to improve on jet energy resolution: •  Resolution in hadronic calorimeter limited by “fluctuations” : number of p0 • produced & amount of invisible energy in one nuclear interaction • Two approaches: • measure the shower components in each event •  access the source of fluctuations (Dual/Triple Readout) • minimize the influence of the calorimeter (in particular hadronic one) •  use combination of all detectors erika.garutti@desy.de

20. The first idea: Energy flow First algorithm developed by ALEPH (LEP) in the early 90ies: • Combineenergy measurement from the calorimeter with the momentum measurement from the tracking Ecalo= 25 GeV p=20 GeV En = 5 GeV • Energy of neutral hadron obtained by subtraction: En = Ecalo – ptrack BUT: shad ~ 60% E  Ehad = 25 ± 3 GeV En = 5 ± 3 GeV Calorimeter resolution important in the subtraction method • To not double count the energy: energy deposited in the calorimeter by the tracks has to be masked  Generally granularity of had. (and em) calorimeter is the limiting factor erika.garutti@desy.de

21. Particle Flow paradigm  reconstruct every particle in the event up to ~100 GeV Tracker is superior to calorimeter  use tracker to reconstruct e±,m±,h±(<65%> of Ejet ) use ECAL for greconstruction (<25%>) (ECAL+) HCAL forh0 reconstruction (<10%>) HCAL E resolution still dominates Ejet resolution But much improved resolution (only 10% of Ejet in HCAL) Typical single particle energy at LC PFLOW calorimetry = Highly granular detectors + Sophisticated reconstruction software erika.garutti@desy.de

22. Particle Flow expectations at LC Goal Jet energy resolution: Benchmark performance using jet energy resolution in Z decays to light quarks: Current Pflow performance (PandoraPFA + ILD) uds-jets (full GEANT 4 simulations) • Equivalent stochastic term shown for comparison • PFA resolution is not stochastic • tails in Gaussian distribution = CONFUSION erika.garutti@desy.de

23. 18 GeV Clustering Topological Association 30 GeV 12 GeV 9 GeV 9 GeV 6 GeV State of the art of Particle Flow algorithm High granularity Particle Flow reconstruction is highly non-trivial Currently best performing algorithm: PandoraPFA many complex steps (not all shown) Iterative Reclustering Fragment ID Photon ID erika.garutti@desy.de For more details: Mark Thomson, NIM 611 (2009) 24-40

24. Confusion in Particle Flow If these hits are clustered together with these, lose energy deposit from this neutral hadron (now part of track particle) and ruin energy measurement for this jet. Level of mistakes, “confusion”, determines jet energy resolution not the intrinsic calorimetric performance of ECAL/HCAL Three types of confusion: i) Photons ii) Neutral Hadrons iii) Fragments Failure to resolve neutral hadron Reconstruct fragment as separate neutral hadron Failure to resolve photon erika.garutti@desy.de

25. Technical aspects of Particle Flow • Use calorimeter measurement to • “guide” the clustering: • re-cluster if Eclusterdiffers too much from track momentum • Back to an “Energy Flow” method • but much higher sophistication • Hadronic calorimeter resolution • effects the clustering performance (second order effect) erika.garutti@desy.de

26. HCAL ECAL Detector design at ILC “no” material in front – calorimeter inside the solenoid large radius and length – to better separate the particles large magnetic field – to sweep out charged tracks small Moliere radius – to minimize shower overlap small granularity – to separate overlapping showers PandoraPFA currently used to optimize the ILD detector design ILD: International Large Detector ECAL: • SiW sampling calorimeter • longitudinal segmentation: 30 layers • transversesegmentation: 5x5 mm2pixels HCAL: • Steel-Scintillator tile sampling calorimeter • longitudinal segmentation: 48 layers(6 lI) • transversesegmentation: 3x3 cm2tiles erika.garutti@desy.de

27. Optimization of HCAL • Maximum containment inside the solenoid  small lI • HCAL will be large: absorber cost/structural properties important ? • small granularity – to separate overlapping showers • 3cm x 3cm tiles looks reasonable (5M ch.vs 50M for 1x1cm and 500k ch for 10x10cm) • for low-energetic jets the confusion term of PFA is less sensitive to tile size erika.garutti@desy.de

28. Understand Particle Flow performance erika.garutti@desy.de

29. Time structure of the hadronicshower Previous studies performed assuming a r/o electronics gate of 200ns • Timing for 250 GeV jet • (corrected for time of flight) • 95 % of energy in 10 ns • 99 % in 50 ns Steel HCAL • In steel suggests optimal timing window in range >10 ns • How is the situation in W? erika.garutti@desy.de

30. Time structure of the hadronicshower • both #n and #p far from closed shells • naively would expect more nuclear interactions with W • Problem: expect longer time profile (decays, secondary interactions) • Furthermore: not clear how well modeled in Geant 4 single KLs (QGSP_BERT) 0.3 MiPcut Tungsten HCAL Steel HCAL • Tungsten is much “slower” than Steel • only 80 % of energy in 25 ns • only 90 % in 100 ns • how much due to thermal n ? erika.garutti@desy.de

31. Particle Flow performance vs time cut Tungsten HCAL Steel HCAL • For no time cut (1000 ns) peformance of CLIC_ILD very good • - somewhat better than ILD (thicker HCAL, larger B) • For high(ish) energy jets – strong dependence on time cut • - suggests time window of > 10 ns • - need something like 50 ns to get into “flat region” erika.garutti@desy.de

32. Summary on Particle Flow Algorithm • Interplay of highly granular detectors and sophisticated pattern recognition (clustering) algorithms • Basic detector parameters thoroughly optimized using PandoraPFA • Time structure of hadronic shower is an important parameter in the feasibility study & in the design of the readout electronics  needs validation A PFLOW detector is not cheap: do we believe in simulations ? erika.garutti@desy.de

33. The zoo of PFLOW calorimeters erika.garutti@desy.de

34. Analogue .vs. Digital readout Energy deposited by a charged particle in the active material of a sampling calorimeter follows a Landau distribution  Long-tail Therefore large fluctuations in energy deposition for a single particle Typical calorimeters have multiple particles crossing each cell • analogue readout – including Landau fluctuations A sufficiently high granularity calorimeter may only have a single particle crossing each cell • possibility of digital readout, i.e. count charged particles – insensitive to Landau fluctuations erika.garutti@desy.de

35. Slope = 23 hits/GeV Analogue .vs. Digital readout photon analysis ECAL: Analog readout required hadron analysis HCAL: either Analog or Digital readout S.Magill (ANL) Non-linear behavior for dense showers Calorimeter cell size 1x1cm2 erika.garutti@desy.de

36. The zoo of PFLOW calorimeters erika.garutti@desy.de * Credit: the following slides are based on work done by the CALICE collaboration