participation of lit v.v. ivanov laboratory of information technologies, joint institute for nuclear...

Participation of LIT

V.V. Ivanov

Laboratory of Information Technologies,

Joint Institute for Nuclear Research

International Workshop "JINR Participation in the Experimental Programme on the Future GSI Facility" Dubna, November 20 - 21, 2003

Research with Antiprotons -Hadron Spectroscopy and Hadronic Matter

(PANDA)

Particle identification based on the RICH data

(G. Ososkov ). Study of particle cascading in

nuclei in Pbar A interactions within the reggeon approach (V. Uzhinsky)

Cross sections for event generators (V.Uzhinsky, A.Galoyan)

Implementation of a new mechanism of annihilation in UrQMD, Dual Parton and FRITIOF models(V.Uzhinsky, A.Polanski)

Identification of Charged Particles based on Ring Imaging Cherenkov Detectors

There are two parts: DIRC and Aerogel RICH

DIRC - Detection of Internally Reflected Cherenkov light

Within the TS, at polar angles between 22°-140°, particle identification can be performed by the detection of internally reflected Cherenkov (DIRC) light as realized in the BaBar detector. It will be produced in 1.7 cm thick quartz slabs surrounding the beam line at a radial distance of 48 cm.

The DIRC also serves for the distinction between gammas and relativistic charged particles entering the EM-calorimeter behind and is, therefore, part of the trigger.

Aerogel RICH

Aerogel Cherenkov counters (ACC) with a refractive index of n = 1.02 provide particle identification (PID) and information for higher level triggers, and are located in the endcap of the TS between polar angles of 5° - 22°.

They are specially suited for π/k separation. The optical transmission limits the thickness of the

blocks to about 4 cm. The measurement of the light cones by exploiting

proximity focusing at 10 cm from the exit of the radiator with multi-pad gas detectors allows a compact construction without mirrors and pm-tubes.

This geometry focuses the photons of asymptotic particles to rings of 24±4 mm radius.

Algorithms for particle identification based on the RICH data

Our general approach for particle identification (PID) with the RICH Aerogel Cherenkov Counters (ACC) detector is to test different particle hypothesis for Cherenkov rings formed by Cherenkov light photons.

These photons are detected in the two-dimensional photosensitive counter array of several hundred pads. For each pad its 2D-coordinate and an amplitude related to a corresponding photon energy are registered.

Different particles which are to be identified (π, K) can be characterized by their Cherenkov radius explicitly depending on the particle momentum.

The particle identification procedure

The particle identification procedure we propose is based on the knowledge of sufficiently accurate estimation of a ring center and radius and generalizes the known method with counting the number of pads in fiducial areas calculated for alternative rings.

On the basis of likelihood ratio test (LRT) we propose an algorithm which calculates the sum of the amplitudes of pads occurred in a fiducial area around a tested circle.

Since this sum should be much bigger for the circle corresponding to the true hypothesis, than for the circle related to the alternative hypothesis, the ratio of the first and the second sums must be greater than some chosen constant.

Two Methods Were Tested: (A) Pad Counting in the fiducial Area (PCFA) (B) Amplitude Summing in the fiducial Area (ASFA)

Results of testing both methods, PCFA and ASFA, show the following: the ASFA LRT constant was chosen to have the minimum PID-error probability (1.04%). Then we obtain the probability of ASFA misidentification equal to 2.4% For the PCFA method the PID-error probability is three times worser: 3.2%, while the PCFA misidentification probability is almost on the same level as for ASFA: 2.3%.

There were algorithms developed mainly on the basis of robust approaches, neural networks and cellular automata for tracking, vertex coordinate search, Cherenkov ring recognition and particle identification, as well as for detector calibration and alignment.

A new type of self-constructing radial basis neural networks with fast and effective learning procedure can be applied in developing algorithms for high level triggers.

Thus, this experience could be quite applicable and useful in forthcoming development of data processing algorithms and software for the PANDA experiment at GSI.

Nucleus-Nucleus Collisions -Compressed Baryonic Matter(CBM)

The Superconducting Dipole Magnet calculations in collaboration with VBLHE(P. Akishin)

Tracking for STS (A. Ierusalimov) High-Rate Level-1

Trigger Design Proposal (I. Kisel)

The CBM Superconducting Dipole Magnet

The Modified Superconducting Dipole Magnet

A Possible Variant of Superconducting Dipole Magnet

A Possible Variant of Superconducting Dipole Magnet

CBM: Tracking for STS

The algorithm for the reconstruction of the parameters of the tracks was tested forSilicon Tracking Station (STS) using GEANTsimulated data. Preliminary results showed a good accuracy.

CBM: Tracking for STS

The method for the determination of thecoordinates of the vertex was worked outand tested. Obtained results showed a goodaccuracy of the determination of the vertex position

Subproject: Development of a Level-1 trigger

1. Development of a simulation package for architecture of the Level-1 trigger for the CBM experiment at GSI.

2. Hardware investigation of a low overhead network interface card for possible use in the Level-1 trigger. Typical networking overhead can range from a few microseconds per packet to close to 100 µs per packet in case of Gigabit Ethernet.

3. Development of a Level-1 trigger algorithm for tracking in the inner tracker of CBM. Fast and efficient reconstruction of tracks and vertices is of crucial importance for the trigger.

4. Development of a ring reconstruction algorithm for the RICH detector of CBM.

5. Investigation of the inner tracker and the RICH detector of the CBM experiment using GEANT4 simulations.

CBM: Sketch of Data Flow and Data TopologyCBM: Sketch of Data Flow and Data Topology

IT

TOF

ReactionCounter

RICH

TRDTrackletsearch

Clustersearch

Clustersearch

Readout

L1 TMU

L2Algorithm

HLT / DAQ

Ringsearch

L1L1 L2L2 DAQDAQ

IT-VertexProcessor

Localprocessing

Sub-eventbuilding

Eventprocessing

LL11

CBM: Tracking EfficiencyCBM: Tracking Efficiency

RECO STATISTICS 100 events Refprim efficiency : 98.36 | 46562 Refset efficiency : 94.85 | 49250 Allset efficiency : 90.09 | 64860 Extra efficiency : 77.79 | 15610 Clone probability : 0.11 | 74 Ghost probability : 5.18 | 3358 Reco MC tracks/event : 648 Timing/event : 175 ms

RECO STATISTICS 100 events Refprim efficiency : 98.36 | 46562 Refset efficiency : 94.85 | 49250 Allset efficiency : 90.09 | 64860 Extra efficiency : 77.79 | 15610 Clone probability : 0.11 | 74 Ghost probability : 5.18 | 3358 Reco MC tracks/event : 648 Timing/event : 175 ms

ALL MC TRACKSALL MC TRACKSRECONSTRUCTABLE TRACKS

Number of hits >= 3

REFERENCE TRACKS

Momentum > 1 GeV

Cellular Automaton Method INTRINSICALLY LOCAL AND PARALLEL

Cellular Automaton Method INTRINSICALLY LOCAL AND PARALLEL

HADES 3D Event Display(P. Biryukov)

Three dimensional (3D) Event Display (ED) for the HADES (High Acceptance Di-Electron Spectrometer) experiment can be used for:

• 3D visualization of physical experimental data in order to explore them;• Presentations;• Online data monitoring.

Used software:

• Operation system: Linux.

• 3D graphics: Coin 3D.

• Graphical User Interface: Qt.

• Analysis of experimental data: Hydra and Root.

Event Display

…

GUI

The main GUI

HYDRA

initHydra()

ED детектора

3D Scene

Reading data for an event

ED детектора

3D Scene

Reading data for an event

…GUI of a detectorGUI of a detector

Studies with Relativistic Radioactive Beams

Monte Carlo Modeling of the Relativistic Multi-fragmentation. A. Polanski, Zh. Musulmanbekov, V. Uzhinsky

Monte Carlo Modeling of Relativistic Nuclei Interactions

Ultra Relativistic Quantum Molecular Dynamics (UrQMD) model for high energy

The intranuclear cascade model (ICM) - colliding nuclei are treated as face centered cubic lattices with nucleons occupying the nodes of the lattice

Quantum Molecular Dynamics (QMD) for low energy (less than 250 MeV)

General Evaporation Model (GEM2) with evaporation and fission channels for calculations of de-excitation of nucleus

Multifragmentation Model (MM) with evaporation and fission channels for calculations of the disintegration of excited remnants in nucleus-nucleus collisions using percolation theory

Results of the UrQMD-code calculations

Comparison QMD+GEM2 with Experimental Data

Comparison of the Experimental Mass Distribution of Nuclides Produced in the Reaction P(100 MeV) +238U(circles) With Calculations by the QMD+GEM2

20 40 60 80 1001

10

100

Cros

s Sec

tion

Mass Number

P-Ag at 11.5 GeV

20 40 60 80 100

10

100C-Ag at 4.5 GeV

Cros

s Sec

tion

Mass Number

Comparison of the Experimental Mass Distribution of Nuclides With Calculations by the ICM+MM

Neutron and isotope production cross-sections calculated by QMD + GEM2 and ICM+MM quite well agree with experimental data

These codes can be applied for calculations of the fragmentation of nuclei in spallation reactions

UrQMD + GEM2 needs further development for calculations of interactions of nuclei and antiprotons with nuclei.

Aspects of safety for the design and operation of new accelerator facilities

The shielding design for the anti-proton target and the corresponding separator was done in collaboration with JINR.

(G. Shabratova and M. Yuldasheva)

1. Shielding calculations for the GSI future project have been performed using the radiation transport code FLUKA.

2. Figures- color plot of the dose rates at the anti-proton target area (top and side view).

Methods and software for computer modeling of relativistic heavy ion collisions in the framework of the multifluid-dynamic model for various equations of state

E.G. Nikonov (LIT), V.D. Toneev (BLTP)

Mathematical methods:PIC-method (Particle-in-Cell) for modeling of nuclear matter motion.Newton and other iteration methods for solving the equation of state.Numerical integration methods for calculation of observables.

Used software:Fortran and C++ compilers.Fortran and C++ libraries for standard mathematical functions. IDL (Interface Definition Language) for visualization of computation results.

Evolution of nuclear density for Au+Au collision at 10 AGeV in c.m.s. for impact parameter equal 3 fm

Results of modeling

Evolution of Temperature of nuclear matter for the same reaction