Fast simulation in ATLAS
|
|
- Mabel Haynes
- 6 years ago
- Views:
Transcription
1 Fast simulation in ATLAS A. Salzburger, CERN for the ATLAS collaboration 1
2 Simulation load & requirements typical MC campaign in ATLAS O( 9) events << recorded data - future precision measurements will need more fast MC is needed to cope with present/future needs - higher statistics raises additional questions: I/O, disk space, reconstruction time - see talk of R. Brun on simulation R&D this afternoon ATLAS simulation, 20 2
3 Simulation hierarchy high CPU CONSUMPTION full library alternative/fast low parametric HIERARCHY ACCURACY 3
4 Simulation hierarchy high full library alternative/fast CPU CONSUMPTION event reconstruction (efficiency/fakes) low parametric HIERARCHY ACCURACY physics object creation 3
5 Simulation in ATLAS 20+ years of simulation full library Geant4 / Fluka,Flugg / Geant3 Frozen Showers alternative/fast parametric AFII (Atlfast2) / AFIIF (Atlfast2F ) Atlfast(1) 4
6 Full Simulation Geant4 full talk of J. Chapman talks at this WS geometry model: library Geant4 (translated from ATLAS GeoModel), very detailed, O( 6 ) nodes physics alternative/fast engine: Geant4 tuning possibilities: via geometry/material physics list parametric step length process cuts 5
7 Geant4 tuning the simulation adapting ID material (+ /20 %) investigate effects on ID tracking - to keep geometry structure done by changing material density of certain elements fitted mean ratio 0 K S Minimum Bias Events ( s=900 GeV) Data / MC (nominal) ATLAS Preliminary fitted mean ratio 0 K S Minimum Bias Events ( s=900 GeV) MC (%) / MC (nominal) MC (20%) / MC (nominal) ATLAS Preliminary < 1.5 Beam Pipe Pixel Layer-0 Pixel Layer-1 Pixel Layer-2 2 SCT Layers 3 Radius [mm] < 1.5 Beam Pipe Pixel Layer-0 Pixel Layer-1 Pixel Layer-2 2 SCT Layers 3 Radius [mm] 6
8 Geant4 tuning the simulation N changing the geometry description see talk of Olivier Arnaez electron shower shape dependence on geometry model, changing blended material into individual layers changing the physics list ATLAS calorimeter response to anti-neutrons (0.1 < pt < 50 GeV) 7
9 The library approach Frozen Showers geometry model: Geant4 (translated from GeoModel) physics engine: Geant4 (library, pre-simulated showers) tuning possibilities: limited to shower shapes in library full library alternative/fast parametric 8
10 Fast simulation AFII & AFIIF AFII - parametric cell response of the ATLAS calorimeter: FastCaloSim - Inner Detector & Muon System: Geant4 AFIIF full - parametric cell response of the ATLAS calorimeter: FastCaloSim - Inner Detector & MuonSystem: fast Monte Carlo track simulation (FATRAS) library alternative/fast parametric 9
11 Fast simulation Ways to speed up simulation approximate geometry optimise transport and navigation π 3 full approximate models library parameterisations alternative/fast ctrl C parametric V take shortcuts use new technologies
12 Fast simulation Ways to speed up simulation approximate geometry optimise transport and navigation π 3 full approximate models library parameterisations alternative/fast ctrl C parametric V take shortcuts use new technologies... danger of re-invent the wheel
13 Fast simulation simplified geometry model ATLAS TrackingGeometry (a) - Inner Detector & Calorimeter: simplification to layers and cylindrical volumes keeping the exact description of sensitive elements sensors and chips (b) cooling tubes navigation through the geometry is only done using the layers and volume boundaries, modules are found by intersection with layer material is mapped onto layers using Geant4 description and geantinos thermal management tile (b) sensors and chips cooling tubes mounting socket (c) (d) t [X0] 0.05 Surface 2 Surfac e lx thermal management tile (c) mounting socket 0.01 (d) t [X0] La ye Lay r1 er TrackingGeometry Geant4 module overlap l x [mm]
14 Fast simulation simplified geometry model - Example Inner Detector: O(0) layers and detector boundaries r [mm] η= η= sensitive modules are identical Geant z [mm] TrackingGeometry z [mm] - Muon System: simplification of chambers & exact transcript into TrackingGeometry classes 12
15 Geant4 / TrackingGeometry material comparison Geant4 material TrackingGeometry representation TrackingGeometry Geant4 example for TrackingGeometry layer 13
16 Fast simulation optimised transport model ATLAS TrackingGeometry is a fully connective geometry with interlinked nodes - built from one common surface class to be used with extrapolation engine - result of propagation to current node guides to next node Volume A Volume B n CB t 2 Surface AB Surface CB Surface AC t 1 n AC Volume C 14
17 Fast simulation optimised transport model ATLAS TrackingGeometry is a fully connective geometry with interlinked nodes - built from one common surface class to be used with extrapolation engine - result of propagation to current node guides to next node Volume A Volume B t2 Surface AB ncb Surface AC Surface CB t1 nac Volume C 5 The ATLAS TrackingGeometr 14
18 Fast simulation optimised transport model ATLAS TrackingGeometry is a fully connective geometry with interlinked nodes - built from one common surface class to be used with extrapolation engine - result of propagation to current node guides to next node Volume A Volume B t2 Surface AB ncb Surface AC Surface CB t1 nac Volume C 5 The ATLAS TrackingGeometr 14
19 Fast simulation optimised transport model ATLAS TrackingGeometry is a fully connective geometry with interlinked nodes - built from one common surface class to be used with extrapolation engine - result of propagation to current node guides to next node Volume A Volume B t2 Surface AB ncb Surface AC Surface CB t1 nac Volume C 5 The ATLAS TrackingGeometr 14
20 Fast simulation optimised transport model ATLAS TrackingGeometry is a fully connective geometry with interlinked nodes - built from one common surface class to be used with extrapolation engine - result of propagation to current node guides to next node Volume A Volume B t2 Surface AB ncb Surface AC Surface CB t1 nac Volume C 5 The ATLAS TrackingGeometr 14
21 Fast simulation optimised transport model ATLAS TrackingGeometry is a fully connective geometry with interlinked nodes - built from one common surface class to be used with extrapolation engine - result of propagation to current node guides to next node Volume A Volume B t2 Surface AB ncb Surface AC Surface CB t1 nac Volume C 5 The ATLAS TrackingGeometr 14
22 Fast simulation AFII (FastCaloSim) geometry model: ATLAS calorimeter reconstruction geometry physics engine: ATLAS extrapolation engine (transport) shower shape parameterisation tuning possibilities: shape modification, energy response scaling full library alternative/fast parametric ctrl C V 15
23 Fast simulation AFII high G4 CPU CONSUMPTION 1 ATLFAST2 x 0.1? low HIERARCHY ACCURACY 16
24 Fast simulation AFII - AFII setup: Step 1: Inner Detector with Geant4 Calorimeter, Muon System with Geant4 for µ, all other particles are killed at Calo entry Step 2: Calorimeter cell response from parameterisation applied to particles from ID ID Calorimeter Muon System - Shower parameterisation: parameterisation of shower shape variables, allows tuning to data distributions N arbitrary units extensive validation performed for Geant4/AFII Single Photons: E TOT Fullsim Atlfast-II (E GEN = 20 GeV : 1.00 < η < 1.05) ATLAS Preliminary Simulation E TOT / E GEN 17
25 Fast simulation AFII comparison to Geant4 Entries Entries AFII / G4.9.2! 450 AFII / G Not reviewed, for internal circulation only Not reviewed, for internal circulation only 3 ATLAS Preliminary Simulation G4.9.2 AFII jet p [GeV] T ATLAS Preliminary Simulation G4.9.2 AFII miss E T jet p T SUSY signal events [GeV] E relative resolution miss E T relative resolution extensive validation in the context of SUSY analyses for summer 2011 AFII was found to be accurate enough within systematic of jet energy scale (5%) part of the SUSY signal grid simulation of ~ 60 mio events done with AFII Entries pt distribution of electron fakes AFII / G ATLAS Preliminary Simulation G4.9.2 AFII p [GeV] T p T [GeV] 18
26 Fast simulation AFII comparison to Geant4 ATLFAST2 validation in context of top physics analyses - number of jets, jet properties well reproduced - reproduce mw and mtop within statistical uncertainties Not reviewed, for internal circulation only AFII jets being scaled by
27 Fast simulation in ID/MS FATRAS geometry model: physics engine: tuning possibilities: material scaling full library alternative/fast parametric ATLAS TrackingGeometry ATLAS extrapolation (transport) material effect integration parameterised Geant4 for particle decay (and more?) interaction probabilites smearing factors π 3 ctrl C V 20
28 Fast simulation AFIIF high G4 CPU CONSUMPTION 1 ATLFAST2F x0.01? low HIERARCHY ACCURACY 21
29 Fast simulation in ID/MS FATRAS Parameterisation of material interactions (a) multiple scattering (b) ionisation energy loss Entries 3 2 Entries 3 2 Geant4 FATRAS Entries MPV Sigma MPV Sigma GeV μ - traversing 250mm Si (~0.267 % X 0 ) θ proj (c) brem photon radiation 1 5 GeV π traversing 3mm Al (~3.37 % X 0 ) Δ [MeV] (d) brem photon conversion Entries/bin Geant4 Entries Mean RMS Geant4 (cut) Entries 5068 Mean RMS FATRAS Entries 4620 Mean RMS Entries/bin 3 2 Geant4 FATRAS Entries Mean Entries Mean e e <p FATRAS >/<p G4 > p [GeV] T p e [GeV] E γ T [ 22
30 Fast simulation in ID/MS FATRAS (e) nuclear interactions (parametric model implemented) n particles, energy distributions, parameterised from Geant4 X Entries/bin Entries/bin Geant4 (a) outgoing particle energies from hadr. Interaction E out [GeV] (g) angular distribution of outgoing particles Entries/bin Entries/bin FATRAS (b) outgoing particle energy of highest order from hadr. Interaction st E out [GeV] (h) angular distribution of outgoing particles 3 2 pion Δφ phase space restrictions Δη 23
31 threshold. (b) Comparison of the mean cluster size in the ATLAS pixel detector in 900 GeV collision data (black points) and MC simulated with FATRAS (shaded histogram). Fast simulation in ID/MS FATRAS FATRAS in comparison to data - ID reconstruction, tracks with pt > 500 MeV - using exact same sensitive detector elements: conditions data being fully integrated Arbitrary Units -1-2 by FATRAS. (a) Data, Number Run of pixels hits versus Fatras Arbitrary Units -1-2 (b) Data, Number Run of pixels hits versus Fatras Figure 5: Comparison of the geometric distribution of pixel detector hits in (a) and (b) in 900 GeV collision data (black points) and MC simulated with FATRAS (shaded histogram). The distribution is shaped by the existence of inactive pixel modules that are taken into account -3 the tails of the distributions [mm] z 0 sin [mm] 7. Summary d 0 Since the development of FATRAS was started, it has proven to be a useful tool, not only for debugging the track reconstruction algorithms and the simplified reconstruction geometry 24of
32 Fast simulation in ID/MS AFII/AFIIF - ATLAS standalone Muon System and combined (ID/MS) muon reconstruction standalone combined 25
33 Fast simulation ATLFAST Fully parametric simulation - smearing approach to create physics objects (no reconstruction) - has been used in the TDR phase of ATLAS full library alternative/fast parametric x0.001 ctrl C V 26
34 Fast simulation ATLFAST Long experience in ATLAS using parametric simulation - extremely fast - no efficiencies/fakes - no pile-up effects - however, often requested by theorists/externals, generic applications on the market (e.g. Delphes) Is parametric smearing something we (as a community) want to provide? ATLAS prefers to publish unfolded fiducial measurements to a parameteric public simulation. 27
35 Recap full library Geant4 / Fluka (Flugg) / Geant3 Frozen showers alternative/fast parametric AFII / AFIIF Atlfast(1) 28
36 Recap full library Geant4 Frozen showers used for public results alternative/fast parametric AFII / AFIIF Atlfast(1) current MC11 campaign in forward calorimeter 29
37 Recap All simulation setups run in Athena G4 run in G4 setup: G4 run/event handling G4 particle stack Frozen AFII / F ATLFAST run in G4/FATRAS setup: requires 2/1 simulation job runs in ATLFAST setup 30
38 Schematic: AtlfastII-F 31
39 The Integrated Simulation Framework ISF 32
40 The Integrated Simulation Framework ISF 32
41 The nutshell ISF vision DefaultFlavorCalo: fast MC FlavorFilterID: use full MC in cone around electron FlavorFilterID: use full within jet containing b- hadron DefaultFlavorID: use fast MC FlavorFilter: process µ with full MC 33
42 The nutshell ISF design ISimService fast MC ISimService full MC ID fast MC Calo full MC Particle Stack Simulation Kernel MS else fast MC full MC GeoFilter FlavorFilter misc. 34
43 The nutshell ISF design encapsulation of particle stack from flavors - one central stack - one simulation kernel ISimService ID Calo ISimService fast MC full MC fast MC full MC Particle Stack Simulation Kernel MS else fast MC full MC GeoFilter FlavorFilter - division of ATLAS simulation into sub-geometries misc. 35
44 The nutshell ISF design flavor filtering for each sub detector - allows refinement of simulation to optimise speed/accuracy balance ISimService ID Calo ISimService fast MC full MC fast MC full MC Particle Stack Simulation Kernel MS else fast MC full MC GeoFilter FlavorFilter - flexibility to integrate new simulation flavors - prepare for parallel particle processing misc. 36
45 The nutshell ISF design & prototype encapsulation of common services defined by interfaces - event handling, stack handling, truth filling, hit recording, barcode creation aim to feed central hit collection from ISF flavors - to allow for common pile-up digitization FastCaloSim & FATRAS have been imported to the prototype - first simulation tests running - we want to gather experience how to mix simulation flavors Geant4 interfacing on the way - Geant4 can/will still keep an internal particle stack - learn how to feed a future parallel simulation flavor 1st Z -> ee event with FastCaloSim in ISF 37
46 ISF benefits mix and play A common hit service in the ISF - AFII creates already SimHit objects similar to Geant4 hits plan to update FATRAS in Inner Detector - implemented parameterized material effects ISF: use Geant4 - did not deploy ATLAS digitization model ISF: create hits like Geant4 track SimHit Layer 1 Layer 0 SimHit Geant4 interaction ISimHitSvc 38
47 ISF benefits extend easily new parameterisation of hadronic leakage into the Muon System 39
48 ATLAS fast simulaton today & tomorrow ATLAS has extremely profited from Geant4 simulation - close collaboration with Geant4 community is extremely profitable ATLAS has a long-standing experience with fast simulation - starting from ATLFAST in TDR times - first AFII used in public results for summer conferences bulk production of AFII samples in new MC11 campaign Currently fast/full simulation require different setups Huge effort started to develop an integrated simulation framework - steer all simulation flavors from one single framework - vision: mix fast and full MC techniques in one single event - feedback this experience into simulation R&D for the future (long-term) 40
49 Backup section 41
50 Timing Tables Sample Full G4 Sim Fast G4 Sim Atlfast-II Atlfast-IIF Minimum Bias t t Jets Photon and jets W ± e ± ν e W ± µ ± ν µ Table 1: Simulation times per event, in ksi2k seconds, for the full Geant 4 simulation, fast Geant 4 simulation, Atlfast-II, Atlfast-IIF [4]. Atlfast-II uses the full simulation for the inner detector and muon system and FastCaloSim in the calorimetry. Atlfast-IIF uses FastCaloSim for the calorimetry and FA- TRAS for the inner detector and muon system. All times are averaged over 250 events. TABLE I: Average time required for production of simulated hits for single muon samples in ATLAS MuonSpectrometer. Simulated hit creation Geant4 FATRAS (MSonly) [s/event] [s/event] p T =GeV 0.13± ±0.002 p T =0GeV 0.17± ±
51 ATLAS/FATRAS extrapolation engine IN IExtrapolator Extrapolator OUT INavigator Navigator IPropagator StraightLinePropagator HelixPropagator RungeKuttaPropagator STEP_Propagator IMaterialEffectsUpdator MaterialEffectsUpdator McMaterialEffectsUpdator IMultipleScatteringUpdator MultipleScatteringUpdator IMagneticFieldTool MagneticFieldTool MagneticFieldTool_xk IEnergyLossUpdator EnergyLossUpdator McEnergyLossUpdator McConversionCreator 43
52 ATLAS/FATRAS extrapolation engine IN IExtrapolator Extrapolator OUT INavigator Navigator IPropagator StraightLinePropagator HelixPropagator RungeKuttaPropagator STEP_Propagator IMagneticFieldTool MagneticFieldTool MagneticFieldTool_xk IMaterialEffectsUpdator MaterialEffectsUpdator McMaterialEffectsUpdator IMultipleScatteringUpdator MultipleScatteringUpdator G4MaterialEffectsUpdator IEnergyLossUpdator EnergyLossUpdator McEnergyLossUpdator McConversionCreator 43
ATLAS Tracking Detector Upgrade studies using the Fast Simulation Engine
Journal of Physics: Conference Series PAPER OPEN ACCESS ATLAS Tracking Detector Upgrade studies using the Fast Simulation Engine To cite this article: Noemi Calace et al 2015 J. Phys.: Conf. Ser. 664 072005
More informationReducing CPU Consumption of Geant4 Simulation in ATLAS
Reducing CPU Consumption of Geant4 Simulation in ATLAS John Chapman (University of Cambridge) on behalf of the ATLAS Simulation Group Joint WLCG & HSF Workshop 2018 Napoli, Italy - 28th March 2018 Current
More informationATLAS Simulation Computing Performance and Pile-Up Simulation in ATLAS
ATLAS Simulation Computing Performance and Pile-Up Simulation in ATLAS John Chapman On behalf of the ATLAS Collaboration LPCC Detector Simulation Workshop 6th-7th October 2011, CERN Techniques For Improving
More informationElectron and Photon Reconstruction and Identification with the ATLAS Detector
Electron and Photon Reconstruction and Identification with the ATLAS Detector IPRD10 S12 Calorimetry 7th-10th June 2010 Siena, Italy Marine Kuna (CPPM/IN2P3 Univ. de la Méditerranée) on behalf of the ATLAS
More informationCMS Simulation Software
CMS Simulation Software Dmitry Onoprienko Kansas State University on behalf of the CMS collaboration 10th Topical Seminar on Innovative Particle and Radiation Detectors 1-5 October 2006. Siena, Italy Simulation
More informationThe full detector simulation for the ATLAS experiment: status and outlook
The full detector simulation for the ATLAS experiment: status and outlook A. Rimoldi University of Pavia & INFN, Italy A.Dell Acqua CERN, Geneva, CH The simulation of the ATLAS detector is a major challenge,
More informationUpdated impact parameter resolutions of the ATLAS Inner Detector
Updated impact parameter resolutions of the ATLAS Inner Detector ATLAS Internal Note Inner Detector 27.09.2000 ATL-INDET-2000-020 06/10/2000 Szymon Gadomski, CERN 1 Abstract The layout of the ATLAS pixel
More informationTracking POG Update. Tracking POG Meeting March 17, 2009
Tracking POG Update Tracking POG Meeting March 17, 2009 Outline Recent accomplishments in Tracking POG - Reconstruction improvements for collisions - Analysis of CRAFT Data Upcoming Tasks Announcements
More informationPhysics and Detector Simulations. Norman Graf (SLAC) 2nd ECFA/DESY Workshop September 24, 2000
Physics and Detector Simulations Norman Graf (SLAC) 2nd ECFA/DESY Workshop September 24, 2000 Simulation studies for a future Linear Collider We believe that the physics case for the LC has been made.
More informationATLAS PILE-UP AND OVERLAY SIMULATION
ATLAS PILE-UP AND OVERLAY SIMULATION LPCC Detector Simulation Workshop, June 26-27, 2017 ATL-SOFT-SLIDE-2017-375 22/06/2017 Tadej Novak on behalf of the ATLAS Collaboration INTRODUCTION In addition to
More informationModules and Front-End Electronics Developments for the ATLAS ITk Strips Upgrade
Modules and Front-End Electronics Developments for the ATLAS ITk Strips Upgrade Carlos García Argos, on behalf of the ATLAS ITk Collaboration University of Freiburg International Conference on Technology
More informationAnalogue, Digital and Semi-Digital Energy Reconstruction in the CALICE AHCAL
Analogue, Digital and Semi-Digital Energy Reconstruction in the AHCAL Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany E-mail: coralie.neubueser@desy.de Within the collaboration different calorimeter
More informationATLAS ITk Layout Design and Optimisation
ATLAS ITk Layout Design and Optimisation Noemi Calace noemi.calace@cern.ch On behalf of the ATLAS Collaboration 3rd ECFA High Luminosity LHC Experiments Workshop 3-6 October 2016 Aix-Les-Bains Overview
More informationDesign of the new ATLAS Inner Tracker (ITk) for the High Luminosity LHC
Design of the new ATLAS Inner Tracker (ITk) for the High Luminosity LHC Jike Wang (DESY) for the ATLAS Collaboration May/2017, TIPP 2017 LHC Machine Schedule In year 2015, ATLAS and CMS went into Run2
More informationMIP Reconstruction Techniques and Minimum Spanning Tree Clustering
SLAC-PUB-11359 July 25 MIP Reconstruction Techniques and Minimum Spanning Tree Clustering Wolfgang F. Mader The University of Iowa, 23 Van Allen Hall, 52242 Iowa City, IA The development of a tracking
More information05/09/07 CHEP2007 Stefano Spataro. Simulation and Event Reconstruction inside the PandaRoot Framework. Stefano Spataro. for the collaboration
for the collaboration Overview Introduction on Panda Structure of the framework Event generation Detector implementation Reconstruction The Panda experiment AntiProton Annihilations at Darmstadt Multi
More informationThe Compact Muon Solenoid Experiment. Conference Report. Mailing address: CMS CERN, CH-1211 GENEVA 23, Switzerland
Available on CMS information server CMS CR -2008/100 The Compact Muon Solenoid Experiment Conference Report Mailing address: CMS CERN, CH-1211 GENEVA 23, Switzerland 02 December 2008 (v2, 03 December 2008)
More informationTHE ATLAS INNER DETECTOR OPERATION, DATA QUALITY AND TRACKING PERFORMANCE.
Proceedings of the PIC 2012, Štrbské Pleso, Slovakia THE ATLAS INNER DETECTOR OPERATION, DATA QUALITY AND TRACKING PERFORMANCE. E.STANECKA, ON BEHALF OF THE ATLAS COLLABORATION Institute of Nuclear Physics
More informationDirect photon measurements in ALICE. Alexis Mas for the ALICE collaboration
Direct photon measurements in ALICE Alexis Mas for the ALICE collaboration 1 Outline I - Physics motivations for direct photon measurements II Direct photon measurements in ALICE i - Conversion method
More informationFAMOS: A Dynamically Configurable System for Fast Simulation and Reconstruction for CMS
FAMOS: A Dynamically Configurable System for Fast Simulation and Reconstruction for CMS St. Wynhoff Princeton University, Princeton, NJ 08544, USA Detailed detector simulation and reconstruction of physics
More informationMonte Carlo programs
Monte Carlo programs Alexander Khanov PHYS6260: Experimental Methods is HEP Oklahoma State University November 15, 2017 Simulation steps: event generator Input = data cards (program options) this is the
More informationFastSim tutorial for beginners
FastSim tutorial for beginners Matteo Rama Laboratori Nazionali di Frascati 1st SuperB Collaboration meeting London, September 2011 Part I FastSim overview M. Rama - 1st SuperB Collaboration Meeting QMUL
More informationAlignment of the ATLAS Inner Detector
Alignment of the ATLAS Inner Detector Heather M. Gray [1,2] on behalf of the ATLAS ID Alignment Group [1] California Institute of Technology [2] Columbia University The ATLAS Experiment tile calorimeter
More information8.882 LHC Physics. Track Reconstruction and Fitting. [Lecture 8, March 2, 2009] Experimental Methods and Measurements
8.882 LHC Physics Experimental Methods and Measurements Track Reconstruction and Fitting [Lecture 8, March 2, 2009] Organizational Issues Due days for the documented analyses project 1 is due March 12
More informationTau ID systematics in the Z lh cross section measurement
Tau ID systematics in the Z lh cross section measurement Frank Seifert1 supervised by Arno Straessner1, Wolfgang Mader1 In collaboration with Michel Trottier McDonald2 Technische Universität Dresden 1
More informationEUDET Telescope Geometry and Resolution Studies
EUDET EUDET Telescope Geometry and Resolution Studies A.F.Żarnecki, P.Nieżurawski February 2, 2007 Abstract Construction of EUDET pixel telescope will significantly improve the test beam infrastructure
More informationEvent reconstruction in STAR
Chapter 4 Event reconstruction in STAR 4.1 Data aquisition and trigger The STAR data aquisition system (DAQ) [54] receives the input from multiple detectors at different readout rates. The typical recorded
More informationPoS(High-pT physics09)036
Triggering on Jets and D 0 in HLT at ALICE 1 University of Bergen Allegaten 55, 5007 Bergen, Norway E-mail: st05886@alf.uib.no The High Level Trigger (HLT) of the ALICE experiment is designed to perform
More informationEicRoot software framework
EicRoot software framework Alexander Kiselev EIC Software Meeting Jefferson Lab September,24 2015 Contents of the talk FairRoot software project EicRoot framework structure Typical EicRoot applications
More informationA Topologic Approach to Particle Flow PandoraPFA
A Topologic Approach to Particle Flow PandoraPFA Mark Thomson University of Cambridge This Talk: Philosophy The Algorithm Some New Results Confusion Conclusions Outlook Cambridge 5/4/06 Mark Thomson 1
More informationHLT Hadronic L0 Confirmation Matching VeLo tracks to L0 HCAL objects
LHCb Note 26-4, TRIG LPHE Note 26-14 July 5, 26 HLT Hadronic L Confirmation Matching VeLo tracks to L HCAL objects N. Zwahlen 1 LPHE, EPFL Abstract This note describes the HltHadAlleyMatchCalo tool that
More informationPerformance of the ATLAS Inner Detector at the LHC
Performance of the ALAS Inner Detector at the LHC hijs Cornelissen for the ALAS Collaboration Bergische Universität Wuppertal, Gaußstraße 2, 4297 Wuppertal, Germany E-mail: thijs.cornelissen@cern.ch Abstract.
More informationThe performance of the ATLAS Inner Detector Trigger Algorithms in pp collisions at the LHC
X11 opical Seminar IPRD, Siena - 7- th June 20 he performance of the ALAS Inner Detector rigger Algorithms in pp collisions at the LHC Mark Sutton University of Sheffield on behalf of the ALAS Collaboration
More informationIntegrated CMOS sensor technologies for the CLIC tracker
Integrated CMOS sensor technologies for the CLIC tracker Magdalena Munker (CERN, University of Bonn) On behalf of the collaboration International Conference on Technology and Instrumentation in Particle
More informationA New Segment Building Algorithm for the Cathode Strip Chambers in the CMS Experiment
EPJ Web of Conferences 108, 02023 (2016) DOI: 10.1051/ epjconf/ 201610802023 C Owned by the authors, published by EDP Sciences, 2016 A New Segment Building Algorithm for the Cathode Strip Chambers in the
More informationFast pattern recognition with the ATLAS L1Track trigger for the HL-LHC
Fast pattern recognition with the ATLAS L1Track trigger for the HL-LHC On behalf of the ATLAS Collaboration Uppsala Universitet E-mail: mikael.martensson@cern.ch ATL-DAQ-PROC-2016-034 09/01/2017 A fast
More informationPXD Simulation and Optimisation Studies
PXD Simulation and Optimisation Studies Z. Drásal, A. Moll, K. Prothmann with special thanks to: C. Kiesling, A. Raspereza, Prague people Charles University Prague MPI Munich ILC Software Framework Summary
More informationarxiv:hep-ph/ v1 11 Mar 2002
High Level Tracker Triggers for CMS Danek Kotliński a Andrey Starodumov b,1 a Paul Scherrer Institut, CH-5232 Villigen, Switzerland arxiv:hep-ph/0203101v1 11 Mar 2002 b INFN Sezione di Pisa, Via Livornese
More informationGEANT4 is used for simulating: RICH testbeam data, HCAL testbeam data. GAUSS Project: LHCb Simulation using GEANT4 with GAUDI.
Status of GEANT4 in LHCb S. Easo, RAL, 30-9-2002 The LHCbexperiment. GEANT4 is used for simulating: RICH testbeam data, HCAL testbeam data. GAUSS Project: LHCb Simulation using GEANT4 with GAUDI. Summary.
More informationThe LHCb upgrade. Outline: Present LHCb detector and trigger LHCb upgrade main drivers Overview of the sub-detector modifications Conclusions
The LHCb upgrade Burkhard Schmidt for the LHCb Collaboration Outline: Present LHCb detector and trigger LHCb upgrade main drivers Overview of the sub-detector modifications Conclusions OT IT coverage 1.9
More informationA flexible approach to clusterfinding in generic calorimeters of the FLC detector
A flexible approach to clusterfinding in generic calorimeters of the FLC detector University of Cambridge, U.K. : simulation/reconstruction session Outline Tracker-like clustering algorithm: the basis.
More informationPrecision Timing in High Pile-Up and Time-Based Vertex Reconstruction
Precision Timing in High Pile-Up and Time-Based Vertex Reconstruction Cedric Flamant (CERN Summer Student) - Supervisor: Adi Bornheim Division of High Energy Physics, California Institute of Technology,
More informationAdding timing to the VELO
Summer student project report: Adding timing to the VELO supervisor: Mark Williams Biljana Mitreska Cern Summer Student Internship from June 12 to August 4, 2017 Acknowledgements I would like to thank
More informationATLAS NOTE ATLAS-CONF July 20, Commissioning of the ATLAS high-performance b-tagging algorithms in the 7 TeV collision data
ALAS NOE ALAS-CONF-2-2 July 2, 2 Commissioning of the ALAS high-performance b-tagging algorithms in the ev collision data he ALAS collaboration ALAS-CONF-2-2 2 July 2 Abstract he ability to identify jets
More informationATLAS, CMS and LHCb Trigger systems for flavour physics
ATLAS, CMS and LHCb Trigger systems for flavour physics Università degli Studi di Bologna and INFN E-mail: guiducci@bo.infn.it The trigger systems of the LHC detectors play a crucial role in determining
More informationPerformance of FPCCD vertex detector. T. Nagamine Tohoku University Feb 6, 2007 ACFA 9, IHEP,Beijin
Performance of FPCCD vertex detector T. Nagamine Tohoku University Feb 6, 27 ACFA 9, IHEP,Beijin Outline FPCCD and Vertex Detector Structure Impact Parameter Resolution Pair Background in Vertex Detector
More informationComputing in High Energy and Nuclear Physics, March 2003, La Jolla, California
Simulation in ALICE Computing in High Energy and Nuclear Physics, 24-28 March 2003, La Jolla, California F. Carminati, A. Morsch on behalf of the ALICE Offline Project CERN, 1211 Geneva 23, Switzerland
More informationLCDG4 Status. Dhiman Chakraborty, Guilherme Lima, Jeremy McCormick, Vishnu Zutshi. LC Simulations Workshop Argonne, June 02 05, 2004
LCDG4 Status Dhiman Chakraborty, Guilherme Lima, Jeremy McCormick, Vishnu Zutshi LC Simulations Workshop Argonne, June 02 05, 2004 Outline Overview LCDG4 features XML geometry representation SIO contents
More informationEicRoot for tracking R&D studies
EicRoot for tracking R&D studies Alexander Kiselev EIC Software Meeting Jefferson Lab September,24 2015 Contents of the talk Tracking code implementation in EicRoot Few particular applications: Basic forward
More informationEarly experience with the Run 2 ATLAS analysis model
Early experience with the Run 2 ATLAS analysis model Argonne National Laboratory E-mail: cranshaw@anl.gov During the long shutdown of the LHC, the ATLAS collaboration redesigned its analysis model based
More informationTopics for the TKR Software Review Tracy Usher, Leon Rochester
Topics for the TKR Software Review Tracy Usher, Leon Rochester Progress in reconstruction Reconstruction short-term plans Simulation Calibration issues Balloon-specific support Personnel and Schedule TKR
More informationSimulation and Physics Studies for SiD. Norman Graf (for the Simulation & Reconstruction Team)
Simulation and Physics Studies for SiD Norman Graf (for the Simulation & Reconstruction Team) SLAC DOE Program Review June 13, 2007 Linear Collider Detector Environment Detectors designed to exploit the
More informationEvent Displays and LArg
Event Displays and LArg Columbia U. / Nevis Labs Slide 1 Introduction Displays are needed in various ways at different stages of the experiment: Software development: understanding offline & trigger algorithms
More informationTrack reconstruction for the Mu3e experiment based on a novel Multiple Scattering fit Alexandr Kozlinskiy (Mainz, KPH) for the Mu3e collaboration
Track reconstruction for the Mu3e experiment based on a novel Multiple Scattering fit Alexandr Kozlinskiy (Mainz, KPH) for the Mu3e collaboration CTD/WIT 2017 @ LAL-Orsay Mu3e Experiment Mu3e Experiment:
More informationEndcap Modules for the ATLAS SemiConductor Tracker
Endcap Modules for the ATLAS SemiConductor Tracker RD3, Firenze, September 29 th, 23 Richard Nisius (MPI Munich) nisius@mppmu.mpg.de (For the ATLAS-SCT Collaboration) The plan of this presentation Introduction
More informationCut per region. Marc Verderi GEANT4 collaboration meeting 01/10/2002
Cut per region Marc Verderi GEANT4 collaboration meeting 01/10/2002 Introduction Cut here = «production threshold»; Not tracking cut; GEANT4 originally designed to allow a unique cut in range; Unique cut
More informationStudy of the Higgs boson coupling to the top quark and of the b jet identification with the ATLAS experiment at the Large Hadron Collider.
Study of the Higgs boson coupling to the top quark and of the b jet identification with the ATLAS experiment at the Large Hadron Collider. Calvet Thomas CPPM, ATLAS group PhD day 25 novembre 2015 2 The
More informationOptimisation Studies for the CLIC Vertex-Detector Geometry
CLICdp-Note04-002 4 July 204 Optimisation Studies for the CLIC Vertex-Detector Geometry Niloufar Alipour Tehrani, Philipp Roloff CERN, Switzerland, ETH Zürich, Switzerland Abstract An improved CLIC detector
More informationA Geometrical Modeller for HEP
A Geometrical Modeller for HEP R. Brun, A. Gheata CERN, CH 1211, Geneva 23, Switzerland M. Gheata ISS, RO 76900, Bucharest MG23, Romania For ALICE off-line collaboration Geometrical modelling generally
More informationPhysics CMS Muon High Level Trigger: Level 3 reconstruction algorithm development and optimization
Scientifica Acta 2, No. 2, 74 79 (28) Physics CMS Muon High Level Trigger: Level 3 reconstruction algorithm development and optimization Alessandro Grelli Dipartimento di Fisica Nucleare e Teorica, Università
More informationfor the DESY/ ECFA study detector
The TPC Tracker for the DESY/ ECFA study detector Ties Behnke DESY 1-May-1999 the TPC tracker requirements from physics a TPC at TESLA: can this work? results from simulation technical issues conclusion
More informationTracking and flavour tagging selection in the ATLAS High Level Trigger
Tracking and flavour tagging selection in the ATLAS High Level Trigger University of Pisa and INFN E-mail: milene.calvetti@cern.ch In high-energy physics experiments, track based selection in the online
More informationSimulation study for the EUDET pixel beam telescope
EUDET Simulation study for the EUDET pixel beam telescope using ILC software T. Klimkovich January, 7 Abstract A pixel beam telescope which is currently under development within the EUDET collaboration
More informationCMS Conference Report
Available on CMS information server CMS CR 2005/021 CMS Conference Report 29 Septemebr 2005 Track and Vertex Reconstruction with the CMS Detector at LHC S. Cucciarelli CERN, Geneva, Switzerland Abstract
More information1. INTRODUCTION 2. MUON RECONSTRUCTION IN ATLAS. A. Formica DAPNIA/SEDI, CEA/Saclay, Gif-sur-Yvette CEDEX, France
&+(3/D-ROOD&DOLIRUQLD0DUFK 1 Design, implementation and deployment of the Saclay muon reconstruction algorithms (Muonbox/y) in the Athena software framework of the ATLAS experiment A. Formica DAPNIA/SEDI,
More informationCharged Particle Tracking at Cornell: Gas Detectors and Event Reconstruction
Charged Particle Tracking at Cornell: Gas Detectors and Event Reconstruction Dan Peterson, Cornell University The Cornell group has constructed, operated and maintained the charged particle tracking detectors
More informationSiD Simulation & Reconstruction. Norman Graf (for the sim/reco team) LCWS 2010, Beijing March 28, 2010
SiD Simulation & Reconstruction Norman Graf (for the sim/reco team) LCWS 2010, Beijing March 28, 2010 The LOI Physics Benchmarks Process The Letter of Intent (LOI) process required a number of physics
More informationSoftware and computing evolution: the HL-LHC challenge. Simone Campana, CERN
Software and computing evolution: the HL-LHC challenge Simone Campana, CERN Higgs discovery in Run-1 The Large Hadron Collider at CERN We are here: Run-2 (Fernando s talk) High Luminosity: the HL-LHC challenge
More informationPerformance of the GlueX Detector Systems
Performance of the GlueX Detector Systems GlueX-doc-2775 Gluex Collaboration August 215 Abstract This document summarizes the status of calibration and performance of the GlueX detector as of summer 215.
More informationLAr Event Reconstruction with the PANDORA Software Development Kit
LAr Event Reconstruction with the PANDORA Software Development Kit Andy Blake, John Marshall, Mark Thomson (Cambridge University) UK Liquid Argon Meeting, Manchester, November 28 th 2012. From ILC/CLIC
More informationStefania Beolè (Università di Torino e INFN) for the ALICE Collaboration. TIPP Chicago, June 9-14
ALICE SDD ITS performance with pp and Pb-Pb beams Stefania Beolè (Università di Torino e INFN) for the ALICE Collaboration - Chicago, June 9-14 Inner Tracking System (I) Six layers of silicon detectors
More informationOverview of the American Detector Models
Overview of the American Detector Models Univ. of Oregon The American study groups have investigated two specific models Choosing any particular detector design is a compromise between competing constraints
More informationThe LHCb Upgrade. LHCC open session 17 February Large Hadron Collider Physics (LHCP) Conference New York, 2-7 June 2014
The LHCb Upgrade LHCC open session 17 February 2010 Large Hadron Collider Physics (LHCP) Conference New York, 2-7 June 2014 Andreas Schopper on behalf of Motivation LHCb is a high precision experiment
More informationRobustness Studies of the CMS Tracker for the LHC Upgrade Phase I
Robustness Studies of the CMS Tracker for the LHC Upgrade Phase I Juan Carlos Cuevas Advisor: Héctor Méndez, Ph.D University of Puerto Rico Mayagϋez May 2, 2013 1 OUTLINE Objectives Motivation CMS pixel
More informationSoLID GEM Detectors in US
SoLID GEM Detectors in US Kondo Gnanvo University of Virginia SoLID Collaboration Meeting @ JLab, 08/26/2016 Outline Design Optimization U-V strips readout design Large GEMs for PRad in Hall B Requirements
More informationJet algoritms Pavel Weber , KIP Jet Algorithms 1/23
Jet algoritms Pavel Weber 29.04.2009, KIP Jet Algorithms 1/23 Evolution of the partonic system parton shower Definition of the jet Different, depends on the algorithm Observation level Calorimeter jet
More informationFirst results from the LHCb Vertex Locator
First results from the LHCb Vertex Locator Act 1: LHCb Intro. Act 2: Velo Design Dec. 2009 Act 3: Initial Performance Chris Parkes for LHCb VELO group Vienna Conference 2010 2 Introducing LHCb LHCb is
More information3D-Triplet Tracking for LHC and Future High Rate Experiments
3D-Triplet Tracking for LHC and Future High Rate Experiments André Schöning Physikalisches Institut, Universität Heidelberg Workshop on Intelligent Trackers WIT 2014 University of Pennsylvania May 14-16,
More informationThe GEANT4 toolkit. Alessandro De Angelis. L Aquila, September University of Udine and INFN Trieste
The GEANT4 toolkit Alessandro De Angelis University of Udine and INFN Trieste L Aquila, September 2001 Layout Monte Carlo simulation of experiments and detectors GEANT4: philosophy, history, future The
More informationCalorimeter Object Status. A&S week, Feb M. Chefdeville, LAPP, Annecy
Calorimeter Object Status St A&S week, Feb. 1 2017 M. Chefdeville, LAPP, Annecy Outline Status of Pi0 calibration Calibration survey with electrons (E/p) Calorimeter performance with single photons from
More informationATLAS NOTE. December 4, ATLAS offline reconstruction timing improvements for run-2. The ATLAS Collaboration. Abstract
ATLAS NOTE December 4, 2014 ATLAS offline reconstruction timing improvements for run-2 The ATLAS Collaboration Abstract ATL-SOFT-PUB-2014-004 04/12/2014 From 2013 to 2014 the LHC underwent an upgrade to
More informationPoS(IHEP-LHC-2011)002
and b-tagging performance in ATLAS Università degli Studi di Milano and INFN Milano E-mail: andrea.favareto@mi.infn.it The ATLAS Inner Detector is designed to provide precision tracking information at
More informationPrimEx Trigger Simultation Study D. Lawrence Mar. 2002
PRIMEX NOTE 6 PrimEx Trigger Simultation Study D. Lawrence Mar. 2002 Introduction This documents describes a Monte Carlo simulation study for the PrimEx o experiment. The study focused on determining trigger
More informationGamma-ray Large Area Space Telescope. Work Breakdown Structure
Gamma-ray Large Area Space Telescope Work Breakdown Structure 4.1.D Science Analysis Software The Science Analysis Software comprises several components: (1) Prompt processing of instrument data through
More informationTracking and Vertexing performance in CMS
Vertex 2012, 16-21 September, Jeju, Korea Tracking and Vertexing performance in CMS Antonio Tropiano (Università and INFN, Firenze) on behalf of the CMS collaboration Outline Tracker description Track
More informationLHC Detector Upgrades
Su Dong SLAC Summer Institute Aug/2/2012 1 LHC is exceeding expectations in many ways Design lumi 1x10 34 Design pileup ~24 Rapid increase in luminosity Even more dramatic pileup challenge Z->µµ event
More informationA Topologic Approach to Particle Flow PandoraPFA
A Topologic Approach to Particle Flow PandoraPFA Mark Thomson University of Cambridge This Talk: Philosophy The Algorithm Some First Results Conclusions/Outlook LCWS06 Bangalore 13/3/06 Mark Thomson 1
More informationOffline Tutorial I. Małgorzata Janik Łukasz Graczykowski. Warsaw University of Technology
Offline Tutorial I Małgorzata Janik Łukasz Graczykowski Warsaw University of Technology Offline Tutorial, 5.07.2011 1 Contents ALICE experiment AliROOT ROOT GRID & AliEn Event generators - Monte Carlo
More informationSpring 2010 Research Report Judson Benton Locke. High-Statistics Geant4 Simulations
Florida Institute of Technology High Energy Physics Research Group Advisors: Marcus Hohlmann, Ph.D. Kondo Gnanvo, Ph.D. Note: During September 2010, it was found that the simulation data presented here
More informationDijet A LL Dijet cross section 2006 Dijet A LL Preparation for Tai Sakuma MIT
Dijet A LL 2005 Dijet cross section 2006 Dijet A LL Preparation for 2008 Tai Sakuma MIT Motivation for dijet A LL is to constrain ΔG G with initial event kinematics The initial state variables can be written
More informationA Novel Strip Energy Splitting Algorithm for the Fine Granular Readout of a Scintillator Strip Electromagnetic Calorimeter
1 3 A Novel Strip Energy Splitting Algorithm for the Fine Granular Readout of a Scintillator Strip Electromagnetic Calorimeter 4 Katsushige Kotera a, Daniel Jeans b, Akiya Miyamoto c, and Tohru Takeshita
More informationSiD VXD Conceptual Design Su Dong SLAC
SiD VXD Conceptual Design Su Dong SLAC Aug/23/05 Su Dong Snowmass 05 VTX WG: SiD VXD conceptual Design 1 Common Design Features Like other detector concepts, SiD VXD design is open to all sensor technology
More informationAtlantis: Visualization Tool in Particle Physics
Atlantis: Visualization Tool in Particle Physics F.J.G.H. Crijns 2, H. Drevermann 1, J.G. Drohan 3, E. Jansen 2, P.F. Klok 2, N. Konstantinidis 3, Z. Maxa 3, D. Petrusca 1, G. Taylor 4, C. Timmermans 2
More informationExpected feedback from 3D for SLHC Introduction. LHC 14 TeV pp collider at CERN start summer 2008
Introduction LHC 14 TeV pp collider at CERN start summer 2008 Gradual increase of luminosity up to L = 10 34 cm -2 s -1 in 2008-2011 SLHC - major increase of luminosity up to L = 10 35 cm -2 s -1 in 2016-2017
More informationLawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory
Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory Title A new inner vertex detector for STAR Permalink https://escholarship.org/uc/item/1863684z Authors Wieman, H. Beiser, F.
More informationThe Phase-2 ATLAS ITk Pixel Upgrade
The Phase-2 ATLAS ITk Pixel Upgrade T. Flick (University of Wuppertal) - on behalf of the ATLAS collaboration 14th Topical Seminar on Innovative Particle and Radiation Detectors () 03.-06. October 2016
More informationPreliminary results in an ongoing study.
Comparison of Ground Cosmic Ray Photon Data in the LAT with GLEAM MC. Preliminary results in an ongoing study. CR Photon Working Group E. Bloom, A. Borgland, A. Bouvier, E. Charles, Y. Edmonds, S. Funk,
More informationAtlantis event display tutorial
Atlantis event display tutorial basic features Zdenek Maxa (University College London) Atlantis team Introduction features Atlantis event display is a stand-alone Java application Uses variety of 2D projections,
More informationModeling the ORTEC EX-100 Detector using MCNP
Modeling the ORTEC EX-100 Detector using MCNP MCNP is a general-purpose Monte Carlo radiation transport code for modeling the interaction of radiation with materials based on composition and density. MCNP
More informationStandard output and plotting. FLUKA Beginner s Course
Standard output and plotting FLUKA Beginner s Course The FLUKA Standard Output FLUKA provides a standard output file that contains plenty of useful information: (fortran unit 11, inp###.out from rfluka)
More information