Computing and Reconstruction in PANDA Stefano Spataro ISTITUTO - PowerPoint PPT Presentation

Computing and Reconstruction in PANDA Stefano Spataro ISTITUTO ¡NAZIONALE ¡ DI ¡FISICA ¡NUCLEARE ¡ Sezione ¡di ¡Torino ¡ Monday, 12 th May 2014

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Overview Ø Introduction to the Panda Experiment Ø The offline framework (PandaRoot) Ø Tracking and Offline Reconstruction Ø Triggerless Data Acquisition Ø Online Reconstruction

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA What is FAIR? forest forest GSI ??? Facility or forest Antiproton and Ion Research

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Let’s speak about future experiments Darmstadt (Germany) GSI F acility for A ntiproton and I on R esearch In the upcoming future (taking data from 2018) …

High Energy Storage Ring Storage Ring ¤ p = 1.5 – 15 GeV/c High intensity mode ž L = 10 32 cm -2 s -1 , σ p /p = 10 -4 ž Electric cooling High resolution mode ž L = 10 31 cm -2 s -1 , σ p /p = 10 -5 ž Stochastic cooling

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA pp, pA collisions 1.5 ⇒ 15 GeV/c (p momentum) Ø Charmonium (cc) spectroscopy Ø Open charm spectroscopy Ø Search for gluonic excitations (hybrids - glueballs) Ø Charmed hadrons in nuclei Ø Drell-Yan Ø Single and double Hypernuclei Ø Parton Dist., EM Form Factor… 2017: Detector Assembly 2018: First Data taking More than 500 physicists from more than 54 institutions in 17 countries

Anti-proton power

Drawbacks of pp collisions Fundamental a high performance trigger

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Software manpower is limited and busy also with other activities ü Data objects format What do you need from ü Geometry handling a reconstruction software? ü I/O Manager ü Database connection (which DB?) ü Simulation of physics processes (G3, G4, Fluka, ?) ü Event Display ü Advanced Analysis Tools The Panda Solution Use a framework already used by other experiments Ø Less software developments for our computing group Ø More people using the same code → better debug Ø Share of the same tools by larger community

The FairRoot History Start ¡tesOng ¡ Panda ¡decided ¡ EIC ¡(Electron ¡ SOFIA the ¡VMC ¡ ¡ R3B ¡joined ¡ to ¡join-­‑> ¡ Ion ¡Collider ¡ (Studies On concept ¡for ¡ BNL) ¡ Fission with FairRoot: ¡same ¡ CBM ¡ Aladin) ¡ Base ¡package ¡ EICRoot ¡ for ¡different ¡ experiments ¡ 2013 2004 ¡ 2010 ¡ 2012 ¡ 2006 ¡ 2011 ¡ ENSAR-­‑ROOT ¡ First ¡Release ¡of ¡ MPD ¡(NICA) ¡ GEM-­‑TPC ¡ ASYEOS ¡joined ¡ CollecOon ¡of ¡ CbmRoot ¡ ¡ start ¡also ¡using ¡ seperated ¡ modules ¡used ¡by ¡ (ASYEOSRoot) ¡ FairRoot ¡ from ¡PANDA ¡ structural ¡nuclear ¡ branch ¡ phsyics ¡exp. ¡ (FOPIRoot) ¡ M. Al-Turany

The PandaRoot Code Design G3VMC Geant3 Geometry Virtual MC G4VMC Geant4 Cuts, Application IO Manager Root ¡files ¡ processes ¡Hits, ¡ ¡ Digits, ¡ ¡ Track Run Manager RTDataBase FairRoot Tracks ¡ propagation Root files M.Al-Turany, Event D.Bertini, Oracle Display F.Uhlig, Event Magnetic MySQL TSQLServer R.Karabowicz Detector base Generator Tasks Field Postgresql PandaRoot Hit STT DPM Dipole Map CbmRoot Producers TOF EvtGen Solenoid EMC R3BRoot digitizers Map ASCII MUO Track const. MPDRoot (NICA) Pythia MVD finding field DIRC ASYEOSRoot FTS Panda Code EICRoot GEM

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Data and Analysis Structure ü No executable Root macros to define the experimental setup, the tasks for reco/analysis, the configuration ü No fixed simulation model Different simulation models with the same user code (VMC) ü No fixed output structure Dynamic event structure based on Root TFolder and TTree

ROOT Geometry and Event Display TGeoManager TEve

MultiVariate Particle Identification implementation of ROOT TMVA methods e/ π separation in EMC ü K- Nearest Neighbors (KNN) ü Learning Vector Quantization (LVQ) ü Multi Layer Perceptron (MLP) ü … MLP Ø EMC shower shape analysis ← Correlation Variables →

Tracking on Proof on Demand 2 worker nodes 4 worker nodes 4 worker nodes a lot of work to modify the code to make it “ Proof compatible ” PoD on external CPUs with SSH (4CPUs)+(8CPUs)+(8CPUs) R. Karabowicz

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Compiled and running on many Linux distributions and on MAC OS X Everybody in his desktop, laptop, local farm can run the code w/o problems (hopefully) Using a set of self-configurating scripts (CMake) and regular checks (DashBoard)

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Also compatibility with Nightly → all nights “ stable ” data sample Continuous → each commit …and Rule Checker Experimental → on demand F.Uhlig

PandaGrid: an Alien2-based GRID Ø It can run on all platforms (source distribution) Why Alien? Ø Several Panda institutions were hosting Alien sites ü “ Reuse ” of currently existing manpower Beta-tester ü Use of parts of already existing resources (now 2.20) ü Strong collaboration with Alien developers Successfully used for: ü Central Tracker TDR ü MVD TDR ü Many Analyses Ø Alien3? No further developments for Alien2 Ø PanDa? Ø Big batch farm? Still some time to design the distributed computing

Distributed T0/T1 centre embedded in Grid/Cloud APPA in 2018 CBM 300000 cores + grid 40 PB disk LQCD 40PB/y archive NuStar Panda (66k cores,12PB disks,12PB/y tape)

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Geometry from TGeoManager forward spectrometer field map target forward p target spectrometer

Global Tracking FTS p MVD STT GEM

Tracking: Global Fit Energy loss Not homogeneous magnetic field Different detector hits Ø 3D points – (TPC) Ø planar hits – MVD/GEM Ø tube + drift time – STT/FTS barrel forward Kalman Filter Detector Hits Prefit Track (GENFIT- Munich*) Track Follower (GEANE – Pavia**) **A.Fontana, L.Lavezzi, A.Rotondi same geometry for simulation and track following * C. Höppner, S.Neubert

Barrel Tracking: Pattern Recognition STT 1° step – STT (+SciTil) pattern recognition 2°step – Correlation of STT tracks with MVD hits MVD 3° step – Extrapolation to GEM planes 4° step – Kalman Filter (Genfit) G.Boca, R. Karabowicz , L.Lavezzi

How STT Pattern Recognition works Parallel tubes Skewed tubes Available information Ø Position/orientation of the tube Ø Drift radius (from drift time) Y X STT ü XY roadmap track finding ü Association of skewed tubes ü From skewed tubes -> Z ü SciTil for track cleaning

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Barrel Tracking: Performances STT stand-alone STT+MVD+GEM Large Improvement from MVD/GEM detectors W. Erni et al, EPJA 49 (2013) 25 S.Costanza, L.Lavezzi

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Other Pattern Recognition Algorithms Ø Riemann Track Finder (T. Stockmanns et al.) (see Andreas Herten talk tomorrow) Ø Barrel Track Finder (R. Karabowicz) Use at the same time of MVD + STT + GEM Ø V0 Track Finder (L. Lavezzi) For particles decaying far from the interaction point Ø Cellular Automaton @ FIAS (just started, not yet in PandaRoot)

Forward Tracking FTS Ø Ideal Pattern Recognition + muons Ø Kalman Filter MVD + GEM |B| [T] _ p @ [GeV/c] 15.00 11.91 8.90 4.06 1.50 E.Fioravanti, I.Garzia, R.Kliemt

Realistic Forward PR (ongoing) Hough Trasformation Based on M. Galuska et. al., PoS(Bormio 2013)023

Data Acquisition Challenges Ø Interaction rates of 20 MHz (50 MHz peak) Ø Event size of ∼ 15 kB Ø Data rates after front end preprocessing: 80GB/s ‐ 300GB/s I peak /I avg ≈ 2-2.5 H.Xu, TIPP2011, Chicago

Where is our signal? Many benchmark channels Background & signal similar

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Events/Data acquired by DAQ (temporarily buffered) Software Trigger Algorithms „Trickle“ of events stored on disc • Required reduction factor: ~1/1000 (all triggers in total) • A lot of physics channel triggers → even higher reduction factor required

12 th May 2014 Computing and Reconstruction Stefano Spataro In PANDA Physics Book criteria: J/psi ( " base for many charmonia) • – Invariant Mass: Tracking/Momentum – Electron ID: Tracking, cluster energy, track/cluster match – Muon ID: Tracking, Muon detector information – Vertex: Tracking • D/Ds Mesons – Pi0s: EMC clusters – Inv. Mass: Tracking – Kaon, Pion ID: dE/dx, DIRC info (w/ track match), ToF (track match) – Vertex: Tracking • Baryons – Inv. Mass: Tracking – proton, pion ID: DIRC info (w/ track match) – Vertex: Tracking Tracking & momentum • Full events: 4C fitting → key information