Transcript Maintenance

Physics Analysis With AliRoot
Peter Hristov
ALICE Collaboration
NEC2005, September 16, Varna
16-Sep-05
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Outline
The framework: AliRoot
Analysis examples
Distributed analysis and parallel analysis
facility
Summary of the analysis tools in AliRoot
Conclusions
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The framework:AliRoot
Up-to-date description of
Alice detectors (geometry,
physics processes, detector
response, etc.)
Rich set of event generators,
easily extensible
Possibility to use different
transport packages
Robust IO scheme
User friendly steering classes
for simulation and
reconstruction
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Efficient tracking in the
barrel detectors and in the
MUON arm
Elaborated reconstruction of
the neutral particles
Reconstruction of V0’s
Combined PID based on the
Bayesian approach
ESD classes suitable for
further analysis
Detailed analysis examples
Possibility to explore wide
spectrum of heavy-ion and
pp physics
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AliRoot Layout
G3
Virtual MC
PDF
HIJING
EGEE+AliEn
GRID
CRT
START
EVGEN
DPMJET
RAW
Monit
STEER
AliSimulation
ISAJET
AliReconstruction
Analysis HBTAN JETAN
ESD classes
FMD
ITS
TPC
TRD
HLT
Fluka
VZERO
PMD
STRUCT
G4
TOF
PYTHIA6
PHOS EMCAL RICH MUON ZDC
ROOT
CINT HIST GRAPH TREES CONT
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IO
MATH
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…
AliRoot: Execution Flow
Initialization
Clusters
Tracking
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Event
Generation
Digits/
Raw digits
Particle
Transport
Event
merging
PID
ESD
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Hits
Summable
Digits
Analysis
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Event Model
RAW data; Written once, read up to 3 times.
Size: 1(pp)- 50(PbPb) MB per event.
Event Summary Data (ESD); Written up to 3 times, read many
times. Contains all the information needed for analysis, finetune calibration and further processing into AOD. Single version
for everybody.
Size: ~ 1/10 of raw per event.
Analysis Object Data (AOD); Written many times, read many
times. Specific for a set of analysis tasks.
Size: ~ 1/10 of ESD per event.
Tag; Written few times, read many times.
Size: 100 B – 1 kb per event, exist many per event.
Done for fast event data selection: global experiment tags,
physics working group tags, user defined tags.
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Existing Analysis Examples in
AliRoot
ESD analysis:
V0 & cascade reconstruction/analysis
Open charm
Specific AOD-based analysis:
HBT
Jet
V0
MUON
Any user can contribute with additional
examples and extending the functionalities
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PT Distributions of ,K,p
Experimental PT distributions.
Selection of tracks from the primary vertex: cuts on impact
parameters
PID: using probabilities as weights, taking the most probable
type, taking type above a probability threshold,…
Corrections:
Contamination: comparison of the PID and true MC particle
type. Depends on the way one obtains the exp. PT spectra.
Combined acceptance ~ Geometrical acc. x Decays x
Reconstruction efficiency x PID efficiency. Depends on the
primary vertex position, momentum, and occupancy =>
Comparison between MC kinematics tree and reconstructed
particles.
Resolution: comparison between the MC and reconstructed
momenta.
=> The framework provides easy access to the kinematics tree.
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Example Histograms: PbPb events
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 Meson Mass, Width, PT Distribution, Yield
Selection of tracks from the vertex, PID of kaons.
Cuts to increase the significance (5 parameters for each pair: PT,
azimuthal and polar angles, azimuthal and polar angles in the
rest frame).
Effective mass distribution for all pairs of kaon candidates
(global and for different PT intervals).
Effective mass distribution for the (combinatorial) background.
Event mixing: positive particles from the same event,
negative from a different, but “similar” one. Similarity: close
event impact parameters and number of negative particles.
Event plane? One might rotate all the negative tracks
accordingly.
Mass resolution, combined acceptance from MC.
=> The framework provides easy access to events for mixing.
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 Meson Mass in pp Events
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V0s and Cascades: PT Spectra, Yields
V0: pair of tracks with opposite charge, selected according to
the following criteria:
Min. Allowed impact parameter(in XY) for each track.
Max. Allowed DCA between the two tracks.
Max. Allowed cosine of the V0 pointing angle.
Min. And max. Radius of fiducial volume.
Max. 2 => needs covariance matrices of the track
parameters.
Cascades: V0 and “bachelor” track selected according the cuts
above and in addition.
Mass window for V0.
Effective mass distribution in different PT intervals -> fit (or
background subtraction from mixed events) -> experimental PT.
Corrections => Need for MC for combined acceptance,
resolutions,…
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D*  D0 
Search for D0 candidate with some “loose” cuts. (See
the D0 analysis classes.) combined with pion from the
primary vertex -> Distribution of m(D0) - m(D0)
Secondary vertex close to the primary one. Need
for precise secondary vertex finding/fitting,
especially in the case of proton-proton events.
Estimation of the background distributions => event
mixing
Combined acceptance, resolutions, etc. => from MC
=> Classes for precise secondary vertex
finding/fitting are available within the framework
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Example: Jets
Leading hadrons correlations
Jet reconstruction algorithm: UA1 cone
algorithm modified for HI
Cone size: R < 1
pt cut
Background subtraction
Event by event jet identification
Jet parameters: multiplicity, kt, f(z), g-jet
Jet spectrum
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Jets
Pythia
Hijing
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Tag event
Information (meta-data) to select and analyze events
of interest only via user-selected conditions
The framework (inspired from STAR)
Collects the file identifiers where to find the events
Localizes the files
Passes the events to the analysis program
Metadata (> 70 parameters)
Run specific information
Information concerning the state of LHC per ALICE run
Detector information per run
Information about each event being collected from PWG
Populated during and after ESD creation
Architecture defined, partially implemented, analysis
cuts tested
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Distributed analysis
Prototype demonstrated to work
Used routinely by one single user
Not yet released to general users because of major
middleware reorganization (Alien2/gLite)  end 2005
For most of the analyses the files have been “moved”
to a single location to be analysed
Proposal for a Parallel Analysis Facility
Fast on line processing of data (calibration, tuning of
algorithms, online physics results)
Few users (detectors and PWG experts), many x a few
events, short latency
Low cost farm of dual core processors
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Analysis Job Structure
File Catalogue query
User job (many events)
Data set (ESDs, other)
Job output
Job Optimizer
Grouped by SE
Sub-job 1 Sub-job 2 Sub-job n files location
Job Broker
CE and SE
processing
Output file 1
CE and SE
processing
Output file 2
Submit to CE
with closest SE
CE and SE
processing
Output file n
File merging job
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AliEn2 Development
User Interface (gShell)
Job agents:
Response to analysis
requests
Priorities
Quotas
Universal: only one for
accessing all the Grids
Complete: Provides all the
functionality a user might
need
Robust: Does not crash or
hang up in case of misuse
Simple: Does not require
expert knowledge to run it
Transport protocol: xrootd
Uniformity with Root and
PROOF
API: under active
development, available now
in Root
Installation: new installation
utility for easy installation
New catalog:
Fast
Reliable
Secure data managment:
encrypted access envelopes
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Parallel Analysis Facility
Interactive prompt analysis
Data recorded at CERN T0
After first reconstruction pass analyze
results using PROOF on CERN T1
Check detector functioning and hot
channels, calibration, alignment, physics
Prototyping in progress
Redesign of the existing analysis classes
using Root selectors
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Interactive Analysis with PROOF on a
Large Cluster
PROOF SLAVE
SERVERS
PROOF SLAVE
PROOF SLAVE
SERVERS
SERVERS
Slave servers access
data via xrootd from
local disk pools
PROOF
PROOF
PROOF
PROOF SUB-MASTER
Proofd Startup
SERVER
PROOF
Grid Service Interfaces
PROOF MASTER
SERVER
TGrid UI/Queue UI
Guaranteed site access through
PROOF Sub-Masters calling out
to Master (agent technology)
Grid Access Control Service
Grid/Root Authentication
Grid File/Metadata Catalogue
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USER SESSION
Client retrieves list
of logical files (LFN + MSN)
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Summary of Analysis Tools
The tools work on ESD/AOD.
Basic kinematics from Root: 3-vectors, 4vectors, rotations, boosts, eff. masses, PT
calculations
Geometrical tools: propagation of tracks,
DCA, primary and secondary vertexes.
Specific: V0s, cascades -> AliV0vertexer,
AliCascadeVertexer.
General: AliITSVertexerTracks.
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Summary of Analysis Tools
Impact parameter of the interaction: based on ZDC
energies and number of participants from ESD.
Event plane: AliFlowAnalysis.
Typical tasks:
Comparison with MC and access to the kinematics
tree. Creation of “MC AOD”;
Calculation of combined acceptance (“physics
efficiency”);
Background studies using the event mixing
technique.
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(Trivial) Practical Advises
Instantiate the objects outside the analysis
loops and use setters inside. Use references
instead of new objects.
Do the pre-selection of tracks and particles in
single loop before the main analysis. Use
indexes to access the original objects.
Use standard tools for sorting, minimization,
etc. Do not rewrite the existing ROOT tools!
Load to memory when possible (example:
tree->LoadBaskets())
Foresee checks for the data consistency
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Conclusions
The main tools for data analysis are available in Root
and AliRoot
The analysis code based on ESD / AOD’s seems
adequate, and is evolving according to feedback
The infrastructure tools for distributed analysis are
under active development
Several detailed analysis examples are available
Analysis infrastructure for distributed data will be
offered to test users 1Q06
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