1. From the ATLAS electromagnetic calorimeter to SUSY Freiburg, 15/06/05 Dirk Zerwas LAL Orsay Introduction ATLAS EM-LARG Electrons and Photons SUSY measurements Reconstruction of the fundamental parameters Conclusions

2. Introduction LHC: CERN’s proton-proton collider at 14TeV 2800 bunches of 10 11 protons bunch crossing frequency: 40.08MHz Low Luminosity: 10 33 cm -2 /s meaning 10fb -1 per experiment (3 years) High Luminosity 10 34 cm -2 /s meaning 100fb -1 per experiment (n years) SLHC: most likely 10 35 cm -2 /s meaning 1000fb -1 per experiment (2015+) startup for physics: late 2007 Two multipurpose detectors: ATLAS, CMS The experimental challenges of the LHC environment: bunch crossing every 25ns 22 events par BX (fast readout, 40MHz  200Hz, event-size 1.6MB) High radiation  FE electronics difficult (military and/or space technology) and with that do precision physics!

3. Physics at the LHC Process Events/s Events/year other machines W  eν 15 10 8 10 4 LEP/ 10 7 TeV. Z  ee 1.5 10 7 10 7 LEP tt 0.8 10 7 10 4 TeVatron bb 10 5 10 12 10 8 Belle/Babar QCD jets 10 2 10 9 10 7 p T >200GeV If the machine works well: Factory of Z, W, top and QCD jets. Will be limited quickly by systematics! Measurements: W mass to 20MeV needs control of the linearity/energy scale (0.02% energy scale) Higgs mass measurement (if etc) in γγ SUSY precision measurements with leptons Stringent requirements on the energy scale, uniformity and linearity of the ATLAS-EM Calorimeter response! Startup date getting closer, need to prove that we understand and are prepared Calo! You have heard already much about the physics from Sven Heinemeyer, Tilman Plehn, Christian Weiser,… plus in-house expertise on ATLAS-Tracker, ATLAS- Muons, Higgs physics,…..so try to find things of added value not covered so far: Calo+SUSYreco

4. The ATLAS Electromagnetic Calorimeter Liquid Argon Sampling Calorimeter: lead (+s.s.) absorbers (1.1, 1.5mm Barrel) liquid Argon gap 2.2mm 2kV (barrel) varying gap and HV in the endcap accordion structure  no dead area in φ “easy” to calibrate R 4m φ Z 3.2m Z 0m R 2.8m

5. Granularity (typical Δη X Δφ ): Presampler = 0.025x0.1 (up to η=1.8) Strips = 0.003x0.1 (EC  ) Middle = 0.025x0.025 (main energy dep) Back = 0.05 x0.025 The barrel and endcap EM-calorimeters! Some numbers: 2048 barrel absorbers 2048 barrel electrodes giving 32 barrel modules (4years of production and assembly) 16 endcap modules All assembled and inserted in their cryostats Barrel cryostat in pit waiting for electronics Thickness: 24-30X 0 Barrel Endcaps

6. Calibration of the ATLAS EM calorimeter General Strategy and Sequence for electrons and photons: Calibration of Electronics necessitates a good understanding of the physics and calibration signal Corrections at the cluster level: position corrections correction of local response variations corrections for losses in upstream (Inner detector) material and longitudinal leakage Refinement of corrections depending on the particle type (e/γ) uniformity 0.7% with a local uniformity in ΔηXΔφ=0.2x0.4 better than 0.5% inter-calibrate region with Zee What can be studied where? Calibration of electronics studied in testbeam Corrections at cluster level: testbeam and ATLAS simulations uniformity: testbeam Zee: simulation The best Monte Carlo is the DATA! For ATLAS: Testbeam  TestbeamMC  ATLASMC  ATLAS

7. ATLAS series modules in testbeam 1998-2002: prototype and single module tests at CERN: 4 ATLAS barrel modules 3 ATLAS endcap modules Single electron beams 20-245GeV Studies of: energy resolution linearity uniformity particle ID 2004: combined testbeam endcap and barrel including tracking and muons FE electronics Sitting directly on the feedthrough as in ATLAS

8. Electronics: bipolar signal time to peak 50ns (variable) 40MHz sampling of 5 samples (125ns) three gain system 1/9.5/10 (automatic choice) From 5 samples in time to one “energy”: Optimal Filtering coefficients: exponential versus linear different entry points inductance effect: parallel versus serial electronic gains The Signal/Electronics Calibration Calibration signal : ~0.2% Physics signal  01.4 L non-uniform: 2-3 % effect on E along  Preamp + shaper (3gains) + SCA 60 30 10 L (nH)

9. Hamac SCA: Atlas Calorimeter Electronics. Sampling of 3x4 signals at 40MHz, 13.5 bits of dynamic range with simultaneous write and read in rad-hard technology (DMILL). Same type of chip used in digital oscilloscope: keep the high dynamic range and increase the sampling rate and bandwidth while using the cheapest technology on the market: 0.8µ pure CMOS (patent filed in April 2001). Instruments are based on the MATACQ chip which is a sampling matrix able to sample data at 2GS/s over 2560 points and 12 bits of dynamic range with a very low power consumption compared to standard systems. This structure has first been used in the design of the new digital oscilloscope family of Metrix (0X7000). This product is the first autonomous 12-bit scope on the market. Award for technology transfer to industry of the SFP (  DPG) Also used in a 4-channel VME and GPIB board. The latter offers the 2GS/s – 12bits facility with low power at low cost. It’s perfectly suited for high dynamic range precise measurements in harsh environments (CAEN). Digression:From physics to industry. Dominique Breton LAL-Orsay, Eric Delagnes CEA-Saclay

10. Cluster Corrections Clustering with fixed size Correct position S-shape in eta Correct phi offset S shape eta in strips local energy variations phi (accordion) local energy variations eta Testbeam: phi modulation Endcap: ATLAS simulation: S-shape Variation of correction as function of η under control (smooth behaviour)

11. Cluster Corrections: longitudinal weighting Non-negligeable amount of material before the calorimeter Reconstruction needs to optimise simultaneously energy resolution and linearity. Method based on Monte Carlo and tested with data in one point η= 0.68: Correct for energy loss upstream of Presampler (cryostat+beam line material) Energy lost between PS and calo (Cable/board) Small dependence of calo sampling fraction+ lateral leakage with energy Longitudinal leakage depth function of depth only f brem extracted from simulation and beam transport of H8 beam line, not present in ATLAS 1.5 X 0, 3.6 %@10 GeV 0.9 X 0, 4.1 %@10 GeV > 30 X 0, 0.3 %@10 GeV E PS = energy in presampler E i =energy in calorimeter compartments

12. Linearity Achieved better than 0.1 % over 20-180 GeV but : - done only at one  position in a setup with less material than in ATLAS and no B field -No Presampler in Endcap (ATLAS) for  >1.8 Systematics at low energy ~0.1 % Dedicated setup was used in 2002 to have a very precise beam energy measurement : - Degaussing cycle for the magnet to ensure B field reproducibility at each energy (same hysteresis) - Use a precise Direct Current-Current Transformer with a precision of 0.01 % - Hall probe from ATLAS-Muon in magnet to cross-check magnet calibration  lots of help from EA-team (I. Efthymiopoulos)  Limitation of calorimeter linearity measurement is 0.03 % from beam energy knowledge - Absolute energy scale is not known in beam test to better than ~1 % - Relative variation is important

13. Energy resolution Resolution at  =0.68 Local energy resolution well understood since Module 0 beam tests and well reproduced by simulation :  Uniformity is at 1% level quasi online but achieving ATLAS goal (0.7 %) difficult Good agreement for longitudinal shower development between data and testbeam MC

14. Cluster Energy Corrections In ATLAS: use a simplified formula: E(corr) = Scale(eta)*(Offset(eta)+W0(eta)*EPS+E1+E2+W3(eta)*E3) 3x7 0.1%-0.2% spread from 10GeV to 1TeV over all eta remember testbeam was 1point: proof that the method works! 10GeV 50GeV 100GeV

15. Energy resolution in ATLAS Simulation 100GeV resolution X0 in front of strips Energy resolution in ATLAS wrt testbeam 20% worse Typically 2-4 X0 in front of calorimeter Good correlation with resolution Current method at the limit of its sensitivity For historians: wrt TDR 25% degradation, but in TDR simulation Inner Detector Material description incomplete

16. Barrel uniformity @ 245 GeV in testbeam In beam setup, one feedthrough had quality problem ( open symbols) due to large resistive cross talk (non-ATLAS FT).  > 7 is ATLAS like and can be used as reference : uniformity better than 0.5 % Energy scale differs by 0.13 %  quality of module construction is excellent rms 0.62% 0.45% 4.5‰0.49% Module P13 Module P13  > 7 Module P15  > 7 Module P13 Energy resolution Similar results for endcap modules

17. Position/Direction measurements in TB 245 GeV Electrons ~550 μm at  =0 ~250 μm at  =0  mid  strip  H  γγ vertex reconstructed with 2-3 cm accuracy in ATLAS in z Precision of theta measurement 50mrad/sqrt(E) Good agreement of data and simulation  Z ~5mm  Z ~20mm

18. Z  ee uniformity 0.2x0.4 ok in testbeam description of testbeam data by Monte Carlo satisfactory make use of Z  ee Monte Carlo and Data in ATLAS for intercalibration of regions 448 regions in ATLAS (denoted by i) mass of Z know precisely E i reco = E i true (1+α i ) M ij reco =M ij true (1+(α i +α j )/2) fit to reference distribution (Monte Carlo!!!) beware of correlations, biases etc… At low (but nominal) luminosity, 0.3% of intercalibration can be achieved in a week (plus E/P later on)! Global constant term of 0.7% achievable!

19. Mass resolution of Higgs bosons H  γγ 120.96GeV σ= 1.5GeV H  γγ Note that the generated Higgs mass is 120GeV: Effect: calibration with electrons, so the photon calibration is off by 1-2% Getting from Electron to photon in ATLAS will require MC! H  ZZ  4e: Mass scale correct within 0.1GeV σ=2.2GeV

20. Particle Identification/jet rejection Dijet cross section ~1mb Z  ee 1.5 10 -6 mb W  eν 1.5 10 -5 mb Need a rejection factor of 10 5 for electrons Use the shower shape in the calorimeter Use the tracker Use the combination of the calo+tracker Cut based analysis gives for electrons an efficiency of about 75-80% with a rejection factor of 10 5 Multivariate techniques are being studied for possible improvements (likelihood, neural net, boost decision tree)

21. Soft electrons Two possibilities for seeded electron reconstruction calo tracker Reconstruction of electrons close to jets difficult, and interesting (b- tagging) especially for soft electrons. Dedicated algorithm: builds clusters around extrapolated impact point of the tracks calculates properties of the clusters PDF and neural net for ID useful per se as well as for b- tagging H  bb pions e id efficiency = 80% Pion rejection in: J/Psi : 1050±50 WH(bb) : 245±17 ttH : 166 ±6 J/Psi WH ttH What can we do now with all that?

22. Supersymmetry 3 neutral Higgs bosons: h, A, H 1 charged Higgs boson: H ± and supersymmetric particles: spin-0spin-1/2spin-1 Squarks: q R, q L q Gluino: gg Sleptons: ℓ R, ℓ L ℓ h,H,ANeutralino χ i=1-4 Z, γ H±H± Charginos: χ ± i=1-2 W±W± ~~ ~~ The parameters of the Higgs sector: m A : mass of the pseudoscalar Higgs boson tanβ: ratio of vacuum expectation values mass of the top quark stop (t R, t L ) sector: masses and mixing ~ ~~ Theoretical limit: m h  140GeV/c 2 Many different models: MSSM (minimal supersymmetric extension SM) mSUGRA (minimal supergravity) GMSB AMB NMSSM Conservation of R-parity production of SUSY particules in pairs (cascade) decays to the lightest sparticle LSP stable and neutral: neutralino (χ 1 ) signature: missing E T See talks by Sven and Tilman: Here only a reminder for completeness sake

23. At the LHC Large cross section for squarks and gluinos of several pb, i.e. several kEvents sum jet-PT and ET  effective mass Squarks and gluinos up to 2.5TeV “straight forward” Largest background for SUSY is SUSY (but…) Large masses means long decay chains Selection: multijet with large PT (typically 150,100,50 GeV) and OS-SF leptons Invariant masses jet-lepton, lepton- lepton, lepton-lepton-jet related to masses SM SUSY

24. SUSY at the LHC (and ILC) Moderately heavy gluinos and squarks light sleptons Heavy and light gauginos Higgs at the limit of LEP reach τ 1 lighter than lightest χ ± : χ ± BR 100% τν χ 2 BR 90% ττ cascade: q L  χ 2 q  ℓ R ℓ q  ℓ ℓ qχ 1 visible m 0 = 100GeV m 1/2 = 250GeV A 0 = -100GeV tanβ =10 sign(μ)=+ favourable for LHC and ILC (Complementarity) ~ ~ ~ ~ ~

25. Examples of measurements at LHC Gjelsten et al: ATLAS-PHYS-2004-007/29 From edges to masses: System overconstrained plus other mass differences and edges…

26. Using the kinematical formula (no use of model) and a toy MC for the correlated energy scale error: energy scale leptons 0.1% energy scale jets 1% Mass determination for 300fb -1 (thus 2014): Coherent set of “measurements” for LHC (and ILC) “Physics Interplay of the LHC and ILC” Editor G. Weiglein hep-ph/0410364 Polesello et al: use of χ 1 from ILC (high precision) in LHC analyses improves the mass determination

27. From Mass measurements to Parameters SFITTER (R. Lafaye, T. Plehn, D. Z.): tool to determine supersymmetric parameters from measurements Models: MSUGRA, MSSM, GMSB, AMB The workhorses: Mass spectrum generated by SUSPECT (new version interfaced) or SOFTSUSY Branching ratios by MSMLIB NLO cross sections by Prospino2.0 MINUIT The Technique: GRID (multidimensional to find a non-biased seed, configurable) subsequent FIT Other approaches: Fittino (P. Bechtle, K. Desch, P. Wienemann) Interpolation (Polesello) Analytical calculations (Kneur et al, Kalinowski et al) Hybrid (Porod) Beenakker et al

28. SPS1aΔLHCΔILCΔLHC+ILC m0m0 1003.90.090.08 m 1/2 2501.70.130.11 tanβ101.10.12 A0-100334.84.3 Results for MSUGRA StartSPS1a LHCILCLHC+ILC m0m0 1001TeV m 1/2 2501TeV tanβ1050 A0-1000GeV Convergence to central point errors from LHC % errors from ILC 0.1% LHC+ILC: slight improvement low mass scalars dominate m 0 Two separate questions: do we find the right point? need and unbiased starting point what are the errors? Once a certain number of measurements are available, start with the most constrained model Sign(μ) fixed

29. Masses versus Edges need correlations to obtain the ultimate precision from masses…. SPS1aΔLHC masses ΔLHC edges m0m0 1003.91.2 m 1/2 2501.71.0 tanβ101.10.9 A0-1003320 Δm0Δm0 Effect on mℓ R Effect on mℓℓ 1GeV0.7/5=0.140.4/0.08=5 use of masses improves parameter determination! edges to masses is not a simple “coordinate” transformation: Similar effect for m 1/2 Sign(μ) fixed

30. Total Error and down/up effect HiggssleptonsSquarks,gluinosNeutralinos, charginos 3GeV1%3%1% Theoretical errors (mixture of c2c and educated guess): Running down/up spectrum generated by SUSPECT fit with SOFTSUSY (B. Allanach) central values shifted (natural) m 0 not compatible SPS1aΔLHC+ ILCexp ΔLH+ ILCth m0m0 1000.081.2 m 1/2 2500.110.7 tanβ100.120.7 A0-1004.317 Including theory errors reduces sensitivity by an order of magnitude SPS1aSoftSUSYupΔLHC+LC m0m0 10095.21.1 m 1/2 250249.80.5 tanβ109.820.5 A0-100-9710 Higgs error: Sven Heinemeyer et al.

31. Between MSUGRA and the MSSM Start with MSUGRA, then loosen the unification criteria, less restricted model defined at the GUT scale: tanβ, A0, m 1/2, m 0 sleptons, m 0 squarks, m H 2, μ experimental errors only SPS1aLHCΔLHC m 0 sleptons 100 4.6 m 0 squarks 100 50 mH2mH2 10000993242000 m 1/2 250 3.5 tanβ109.824.3 A0-100 181 Higgs sector undetermined only h (m Z ) seen slepton sector the same as MSUGRA light scalars dominate determination of m 0 smaller degradation in other parameters, but still % precision The highest mass states do not contain the maximum information in the scalar sector, but they do in the Higgs sector! Sfitter-team and Sabine Kraml

32. MSSM MSSM fit: bottom-up approach 24 parameters at the EW scale LHC or ILC alone: certains parameters must be fixed LHC+ILC: all parameters fitted several parameters improved Caveat: LHC errors ~ theory errors ILC errors << theory errors  SPA project: improvement of theory predictions and standardisation LHC ILCLHC+ILC With more measurements available: fit the low energy parameters

33. Impact of TeVatron Data? With Volker Buescher (Uni Freiburg): 2008 too early for Higgs to γγ with 10fb-1 at LHC only central cascade SUSY measurements are available: χ 1, χ 2, q L, ℓ R Higgs is sitting on the edge of LEP exclusion WH+ZH 6 events per fb -1 and experiment at TeVatron end of Run: Δm h = ± 2GeV adding background: Δm h = ± 4-5GeV A hint of Higgs from the TeVatron would help the LHC at least the first year! mtop from TeVatron with 2GeV precision makes impact on fit negligible Higgs mass from γγ ~ ~ No Higgs, edges from the LHC: m 0 = 100 ± 14 GeV m 1/2 = 250 ± 10 GeV tanβ = 10 ± 144 A 0 = -100.37 ± 2400 GeV Higgs hint plus edges from the LHC: m 0 = 100 ± 9 GeV m 1/2 = 250 ± 9 GeV tanβ = 10 ± 31 A 0 = -100 ± 685 GeV

34. And the Egret point? Wim de Boer: astro-ph/0408272 EGRET: on Compton gamma ray observatory, measured high energy gamma ray flux. Compatible with Standard Model, but also SUSY: m 0 =1400 GeV m 1/2 = 180 GeV A 0 =700 GeV tanβ = 51 μ > 0 0 WMAP EGRET Stau coannihilation m A resonance Bulk Incomp. with EGRET data Stau LSP No EWSB Dominant Processes at the LHC: m 0 =1400 ± (50 – 530)GeV m 1/2 = 180 ± (2-12) GeV A 0 =700 ± (181-350) GeV tanβ= 51 ± (0.33-2) Measurements: Higgs masses h,H,A mass difference χ 2 -χ 1 mass difference g- χ 2 Sufficient for MSUGRA ~ Uncertainties: b quark mass t quark mass Higgs mass prediction Les Houches 2005: P. Gris, L. Serin, L.Tompkins, D.Z. Tri-lepton signal promissing

35. Conclusions Construction of ATLAS-EM calorimeter modules finished Testbeam studies have driven the improvement of the understanding of the combined optimisation of linearity and resolution of the calorimeter EM calibration under control electron (and photon identification) are at the required level with multivariate approaches under study SFitter (and Fittino) will be essential to determine SUSY’s fundamental parameters mass differences, edges and thresholds are more sensitive than masses the LHC will be able to measure the parameters at the level % LC will improve by a factor 10 LHC+LC reduces the model dependence EGRET: in MSUGRA, LHC has enough potential measurements to confront the hypothesis Many thanks to Laurent Serin for his help in the preparation of the talk!