STAR Software Walk-Through

Doing analysis in a large collaboration: Overview The experiment: – Collider runs for many weeks every year. – A lot of data to look at! In 2007, ~70M minimum bias events. – Need computers to be able to analyze vast dataset. Analysis Software: – Find relevant data – Develop analysis code Interface to the data Analysis code: plots and correlations of experimentally measured quantities – Submitting jobs to the batch farm – Plotting results

Computing resources Facilities available for STAR – PDSF (in Berkeley, part of NERSC) Parallel Distributed Systems Facility – RCF (in Brookhaven Lab) RHIC Computing Facility Log in to PDSF: pdsf.nersc.gov – Note for PDSF: make sure you have a.chos file. I selected /auto/redhat8 as my environment. – Without an environment properly set, perl won’t work! Web page: – FAQ – USER accounts – Monitoring of farm conditions – … Log in to RCF: rssh.rhic.bnl.gov Web page:

Doing Analysis 101 For real or simulated data that has already been produced into a standard format: Find Data HPSS NFS local. Tools: FileCatalog Run analysis on data. Tools: MuDST StMcEvent Plot results of analysis. Tools: ROOT classes.

Tools: FileCatalog (get_file_list.pl) – Finding Files (of course) that satisfy certain conditions: – production library used, trigger setup used, run numbers, collision system, … i.e. it is a database of our data. Examples of usage found in link above. Production Location – STAR → Computing → Production Location –

Tools: Scheduler – Used to submit jobs to the RCAS linux farm in batch mode. – RCAS: ~10 interactive nodes, ~150 batch nodes. – How to use it: XML script that specifies – files to be used (e.g. using a catalog query) – macro to be executed (i.e. analysis to be done) – what to do with the output – STAR → Computing → Scheduler

Analyzing Example: Real Data, Step I Find events of interest – Au Au collisions, 200 GeV. – p+p collisions, 200 GeV Many collisions and triggers have been used. Example: Looking at “Minimum bias” triggers – This trigger is meant to capture almost all interactions. – Every trigger detector introduces a bias, this trigger is meant to reduce the bias introduced as much as possible. Trigger ID: – Each file can have events that were selected by various trigger conditions – ID picks out a given trigger condition. – 2004 list of triggers: – 2009 data:

A file catalog query… output: path and filename of file. query conditions: – Production: P10ic, production from 2010, real data, official production (pp2009 data) – Filetype: daq_reco_mudst: came from DAQ (real data), processed through the reconstruction chain, stored into a micro Data Summary Tape. – storage: NFS. Mounted on hard disks accessible through the Networked File System, as opposed to the files stored on tape drives in HPSS. ~/afsdir/wrk/jpsi/offline/> get_file_list.pl -keys 'path,filename' -cond 'production=P10ic,filetype=daq_reco_mudst,storage=NFS,sanity=1,filename~st _upsilon' -limit 10 /star/data81/reco/production2009_200Gev_Single/ReversedFullField/P10ic/2009/1 38/ ::st_upsilon_adc_ _raw_ MuDst.root /star/data76/reco/production2009_200Gev_Single/ReversedFullField/P10ic/2009/1 39/ ::st_upsilon_adc_ _raw_ MuDst.root /star/data81/reco/production2009_200Gev_Single/ReversedFullField/P10ic/2009/1 38/ ::st_upsilon_adc_ _raw_ MuDst.root

What’s in a MuDST? The result of the reconstruction of an event. – Trigger information. Signal in ZDC, BBC, VPD, EMC, … – Track information. Number of points found by tracker and used in fit Momentum at first point and last point (and helix parameters) Covariance matrix of track fit dE/dx – nSigma, PID probability charge id of track in event (unique number for a track in an event) type (0=global, 1=primary)  2 Topology Map: bit pattern of hits in detector Distance of closest approach to vertex (for global) Position of first and last points TBrowser, check contents of file. ROOT objects can be drawn quickly.

Define an Analysis Task Examples: – Multiplicity Distribution ~Probability to find events with Nch tracks. Nch: number of charged particles in the event (typically, per unit rapidity at midrapidity). – pT distribution of charged tracks for all events. ~Probability to find a track with a given pT.

The “Maker” framework “Makers” are a way to standardize the way we do analysis: – All have to “prepare” or initialize e.g. book histograms and trees – All do something every event e.g. calculate or obtain distributions of interest – All have to clean up when the job is done e.g. write the histograms

Example Maker code: In PDSF: /auto/pdsfdv39/starspec/pdsfdv34/starspec/calderon/tutorial s/StRoot/StMuDstExampleAnalysisMaker All Makers live under a directory called StRoot Compilation of the analysis code is done in same directory where StRoot directory (or link) is found – cons +StMuDstExample Running is done in same directory where compilation was done. Example in StRoot/macros/examples/ – root4star –b –q ‘RunMuDstExample.C(500)'

Plotting the results Open the output file (dummyFile00.root) in root. Can issue C++ commands on the root classes interactively. Set a sensible color scheme – gStyle->SetPalette(1,0); Create canvases (TCanvas) – TCanvas* cnv1 = new TCanvas(“cnv1”,”multiplicity”,600,600); For drawing histograms: – TH1::Draw() – mMult->Draw(); Can change line color, width, style – mMult->SetLineColor(2); – mMult->SetLineWidth(3); – mMult->SetLineStyle(11); Can draw markers – mMult->SetMarkerStyle(20); – mMult->Draw(“P”); For reproducibility, can also put all the commands into a macro, and just execute the macro: –.x plotMultExample.C

Analyzing Example: Simulation, Step I Generate events of interest – e.g. Lambda_c, Upsilon, J/Psi particles according to a distribution – e.g. use event generators PYTHIA HERWIG HIJING NEXUS AMPT MPC For large datasets, request is done officially to STAR simulation (Maxim Potekhin, simulation leader)