Study of spectator distributions in theHADES experiment
E. Zherebtsova
For the HADES Collaboration
Institute for Nuclear Research RAS

Outline HADES experimental setup Forward Wall detector
 Collision geometry
 Comparison of the FWall charge distributions in different
models Machine learning (ML) approach for centrality
 Centrality selection with ML in the HADES Conclusion and outlook
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Aim of this research

New results for comparing experimental data in theHADES experiment and various models at
Au+Au@1.23AGeV and Ag+Ag@1.58AGeV forspectators detected in the forward detector.

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HADES experimental setup

Tracking system:●Multi-wire drift chambers (MDC)
Particle identification:●Time Of Flight (TOF)●Resistive Plate Chambers (RPC)
Event plane reconstruction:●Forward Wall (FWall)
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Forward Wall detector

288 individual scintillator detectors:
small cells 40x40 mm2



medium cells 80x80 mm2large cells 160x160 mm2

In this work spectators charge distributions are studied separately for cells ofdifferent sizes: small, medium and big.

5Collision geometry

 Spectators can be used for centrality selection and the reaction
plane orientation.

6Centrality determination in the HADES
Glauber approachbased on themultiplicity of producedparticles

HADES Collaboration, Eur. Phys. J. A (2018) 54: 85
7Tools
ModelsSystemDCM-QGSM-SMM
SHIELDSMASH+naive
clusterization
Au+Au at 1.23AGeV
Ag+Ag at 1.58AGeV

Transport code

GEANT3Detector response

HYDRA

Trigger

PT3 (0-40% centrality)

ML Framework: N. Karpushkin (INR RAS)

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Comparison of the FWall charge distributions
in various models
Big cells (Angle: 3.27 - 7.27°)

o In all models there is a discrepancy o In DCM-QGSM-SMM there is adiscrepancy for particles withfor particles withZ = 2. SHIELD is in a goodZ = 2.
agreement with data.
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Comparison of the FWall charge distributions
in various models
Medium cells (Angle: 1.96 - 3.27°)

o SMASH and DCM-QGSM-SMM
models have a good agreementwith data except particles with Z=2.

o DCM-QGSM-SMM model has agood agreement with data. SHIELD
has less particle yields for particles
with Z>3.
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Comparison of the FWall charge distributions
in various models
Small cells (Angle: 0.33 - 1.96°)

o DCM-QGSM-SMM provides largero SMASH and SHIELD couldn't
yields for particles with Z>3.
decribe particles charges with Z>3,
while DCM-QGSM-SMM provides
larger yields for it.

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Centrality dependence of charge distributions
for cells of different size
0-10%

10-20%

20-30%

30-40%

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Conclusion for charge distributions in different models
o Сomparison of the FWall charge distributions was carried out forcells of different sizes between experimental data and variousmodels
o The presented models deviate from the data for nuclei with charge
Z>3Future planso Further investigation of discrepancies between data and models isrequired.
o Selection C+Au and C+Ag events from experimental data.
o Configure parameters of other fragmentation models to describe
data from the Forward Wall (PHQMD model).
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Machine learning technique
Input parameters – FWall cells positions and amplitude in each cell
Target variable – number of TOF+RPC hits
Expected result: centrality selection
Space distribution of the FWall amplitudes
Events 0-5% centrality

Events 35-40% centrality

14Machine learning techniques
Supervised approach
1. Train-test split
2. Train the model:
Inputs:

1D arrays of amplitudes in FWall cells(space distribution of Fwall amplitudes)

Centrality class index

Model architecture:

3. Test model accuracy
ML Framework: N. Karpushkin (INR RAS)

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ML for the HADES exp. data
Au+Au at 1.23AGeV
Number of TOF+RPC hits

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ML for the HADES exp. data
Au+Au at 1.23AGeV
Number of TOF+RPC hits

17Summary Supervised ML approach was applied for centrality classes
determination in HADES The results of applying the approach to HADES data andsimulations with different collision energies and systems wereshown

Outlook Further improvement of method will be carried out.
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