1 Wolf G. Holzmann 23 rd Winter Workshop In Nuclear Dynamics Big Sky, Montana, February 11-17, 2007

2 ★ ★ ★ ★ ★ ★ ★ ★ ★ M. Baker, R. Debbe, A. Moraes, R. Nouicer, P. Steinberg, H. Takai, F. Videbaek, S. White Brookhaven National Laboratory, USA J. Dolejsi, M. Spousta Charles University, Prague A. Angerami, B. Cole, N. Grau, W. Holzmann, M. Lelchouk Columbia Unversity, Nevis Laboratories, USA A. Olszewski, B. Toczek, A. Trzupek, B. Wosiek, K. Wozniak IFJ PAN, Krakow, Poland L. Rosselet University of Geneva, Switzerland J. Hill, A. Lebedev, M. Rosati Iowa State University, USA G. Atoian, V. Issakov, H. Kasper, A. Poblaguev, M. Zeller Yale University, USA A. Denisov IHEP, Russia P. Chung, J. Jia, R. Lacey, N N.. Ajitanand Chemistry Department, Stony Brook University, USA V. Pozdnyakov JINR, Dubna, Russia S. Timoshenko MePHI, Moscow, Russia ATLAS HI Working Group

3 Heavy Ion Physics at the LHC Phase Diagram for Nuclear Matter Pb+Pb collisions at the LHC will produce partonic matter at unprecedented T and  Will allow for detailed study and characterization of this high energy density partonic matter. Study evolution from RHIC -> LHC energies. ATLAS will target a comprehensive set of key observables (see Nathan Grau’s ATLAS overview talk) Here, I will exclusively focus on jet tomography.

4 Jets as a tomographic probe of the medium Fragmentation : Jets in h+h collisions Fragmentation : Jets in HI collisions Gyulassy et al., nucl-th/ Jet modification sensitive to gluon densities, path length, … Jet modification sensitive to gluon densities, path length, …. Jets as Tomographic Probes of the Medium!

5 Jet tomography at RHIC STAR, PRL 93 (2004) Jets studied statistically via singles yields and correlations… Qualitatively successful, but quantitative interpretation difficult… interm. pT correlations high pT correlations R AA  -h correlations

6 Jet tomography at RHIC Plus no real fragment. function measurements, etc… Correlation studies complicated by trigger bias effects?  -h correlations suffer from statistics RAA not really constraining E-loss models? T. Renk, hep-ph/

7 Jet tomography at LHC Can (and will) do RHIC type studies with better statistics Can (and will) do high p T jet reconstruction (event-by-event jet tomography, frag. functions, jet structure…) How can jet studies at the LHC improve on the situation? Truly high p T jets will be produced copiously in Pb+Pb collisions at the LHC Why would you want to do this with ATLAS?

8 ATLAS Calorimetery Hadronic Barrel Hadronic EndCap EM EndCap EM Barrel Forward Finely segmented calorimeter coverage over full  range and large  range The ATLAS Calorimeter

9 (Di)jets from PYTHIA in Calorimter Towers embedded in HIJING event Measuring Jets in The ATLAS Calorimeter Energetic jets clearly visible over the heavy ion background Large  coverage is important

10 Jet Background Jet Back ground All too wide for single photons  x  = x 0.1 –Segmentation of first EM sampling layer so fine that heavy ion background is ~ negligible (unique at LHC) –Fine  -> rejection of neutral hadron decays –Clean 1st sampling-> prompt  isolation Taking a closer look

11 Two Approaches to Jet Reconstruction in ATLAS A) Seeded Cone Algorithm Original cells Cloned cells Original towers Subtracted cells New towers Reconstructed jets Layer-by-layer subtraction (exclude seeds) Currently also looking at methods to improve algorithm: seed selection, background subtraction, … First approach: use standard p+p cone algorithm with background subtraction

12 Jet Energy Resolution with Seeded Cone Algorithm Study of different event samples embedded into central Pb+Pb HIJING (b=0-2 fm) Results obtained from standard p+p cone algorithm w/ backgr.- subtraction Some recalibration still needed.

13 Can we control the flowing background? Presampler Layer 1Layer 2Layer 3 Yes! Can measure dN/d ϕ in different layers (and sections) of calorimeters e.g. EM Barrel η ϕϕϕϕ ϕϕϕϕ ηηη

14 B) K T Algorithm clusters particles close in phase-space: d ij = min(k 2 ti,k 2 tj )R 2, where R 2 =(  i -  j ) 2 +(  i -  j ) 2 Kt algorithm purposefully mimics a walk backwards along the fragmentation chain for all possible combinations: O(N 3 ) Cacciari et al: “Fast” Kt optimization to O(NlogN) Two Approaches to Jet Reconstruction in ATLAS d iB = k 2 ti

15 How fast is fast? “Fast” Kt algorithm outperforms cone algorithm, Becomes feasible in heavy ion environment! M. Cacciari et al, hep-ph/

16 Real Jets appear as narrow towers “Fake” Jets appear flat and broad Use jet topology to discriminate between jets and background! “Fast” Kt Finder: Discriminating Jets and Background

Initial look seems promising. Other variables can also be constructed. E T,max = maximum E T in calo cell = average E T in calo cell Discriminating Jets and Background: A First Look

18   PYTHIA  + jet (75 GeV) superimposed on b=4 fm HIJING Pb+Pb event, full GEANT Jet   +Jet in ATLAS

19 PYTHIA  + jet (75 GeV) superimposed on b=4 fm HIJING Pb+Pb event, full GEANT   Background subtracted Jet   +Jet in ATLAS

20   EM Layer 1 E T (GeV) Isolated photon gives clean signal in EM first sampling layer Even in central Pb+Pb ! One (of 64) rows in barrel EM calorimeter 1st sampling layer Δη×Δ ϕ = 0.003x0.1  +Jet in ATLAS

21  +Jet in ATLAS Direct  triggered angular correlations energy calibrated: - jet studies - mach cone studies Photon bremsstrahlung in jet cone? Many interesting possibilities: let your imagination run wild :-)

22 Summary and Outlook Jet modification studies at the LHC hold much potential for quantitative tomography of the partonic medium ATLAS is uniquely positioned to perform key jet measurements wellLots of ground work on jet reconstruction in heavy ion environment (seeded cone algorithm, fast Kt algorithm, different background subtraction schemes, etc…) being done in ATLAS Studies shown only an “amuse gueule” expect much more, soonNew collaborators are welcome!

23 Backup Slides

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26 Jet Position Resolution with Seeded Cone Algorithm Results obtained from standard p+p cone algorithm w/ backgr.- subtraction Some recalibration still needed. Resolutions in  and  for ~50 GeV

27 Jet Background Jet Back ground All too wide for single photons  x  = x 0.1 –Segmentation of first EM sampling layer so fine that heavy ion background is ~ negligible –Fine  -> rejection of neutral hadron decays –Clean 1st sampling-> prompt  isolation The ATLAS Calorimeter

28 The ATLAS Calorimeter Δη×Δ ϕ in LAr Barrel: Layer 1: 0.003x0.1 Layer 2: 0.025x0.025 Layer 3: 0.05x0.025 Finely segmented calorimeter coverage over full  range and large  range

29 Infrared and collinear safeExceptionally suited to study jet sub-structure: - modification of jet topology in Pb+Pb - hard radiation within the jet New ways to distinguish jets and backgroundSystematic cross-check to cone algorithm Advantages of “Fast” Kt Algorithm