Analysis of thermal dielectron channels (status)...4.9.20 Etienne Bechtel - Dilepton meeting - GSI,...

Analysis of thermal dielectron channels (status)

Dilepton meeting GSI, Germany

September 2020

Etienne Bechtel University of Frankfurt

Etienne Bechtel - Dilepton meeting - GSI, Germany4.9.20 2

Simulation overviewSoftware OCT19 release

• The TRD software was on the status of APR20 Event-based simulation and reconstruction GEANT3 was used

Collision system 5 Million 12 A GeV Au+Au (10% most central) collisions as calculated with UrQMD LMVM cocktail added as pluto input Thermal radiation added with a uniform mass distribution an rescaled to the expected yields

Cuts > 2 STS hits > 5 RICH hits > 2 TRD hits

< 3 Pre-pairing mass > 25 keV

Target 25 mu gold target

Setup Sis100_electron_setup

χ2 /NDF

90% e-eff in RICH 80% e-eff in TRD


Dielectron signal input

Full phase space pluto input for the dielectron analysis

•The y-axis is scaled to the expected yields per event

•The signals were calculated with a temperature of 0.13 GeV

•The thermal radiation is calculated with a fireball model and a coarse-graining approach

Galatyuk, T., Hohler, P., Rapp, R. et al., Eur. Phys. J. A 52, 131 (2016)

Ralf Rapp, Hendrik van Hees., Physics Letters B, 753:586, Feb 2016

For the expected yields see: https://cbm-wiki.gsi.de/foswiki/bin/view/PWG/CbmDileptonInfoFilesAuAu11000

*as shown by Tetyana in the last collab. meeting


Phase space of in-medium rho

Comparison of MC (top) and reconstructed (bottom) pt_y distribution

•We have sufficient mid-radpidity coverage

6−10

5−10

4−10

Yiel

d [1

/eve

nts]

2− 1− 0 1 2 3 4 5 6MClabY

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

(GeV

/c)

MC

Tp

Au+Au 12A GeV - 0-10% centrality

= 1.66cmy

6−10

5−10

4−10

Yiel

d [1

/eve

nts]

2− 1− 0 1 2 3 4 5 6labY

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

(GeV

/c)

Tp

Au+Au 12A GeV - 0-10% centrality

= 1.66cmy


Detection efficiency and correction matrix

Rapidity and transverse momentum dependence •The detection efficiency is in the order of 1-2%

• A 2D correction matrix was extracted and applied to the reconstructed spectra

• A 3D correction as a function of pt,y and invariant mass was not possible due to statistics

0 0.5 1 1.5 2 2.5 3 (GeV)

Tp

5

10

15

20

25

30

35

403−10×

det.

prob

.

Au+Au 12A GeV0-10% centrality

ρin-med.

QGP

1

10

210

310

Cor

rect

ion

fact

or

0 0.5 1 1.5 2 2.5 3 3.5 4labY

0

0.5

1

1.5

2

2.5

3

3.5

4

(GeV

/c)

Tp


Combinatorial background

Invariant mass spectrum of all uncorrelated unlike-sign pairs from the same event

• The background is dominated by electron-electron and electron-pion contributions

• The electron-pion contribution is dominant

from about 1.5 GeV/ • The pion-pion contribution is suppressed but still significant (especially at larger masses)

• Pion suppression seems to be a bit low

c2

0 0.5 1 1.5 2 2.5)2 (GeV/ceeM

10−10

9−10

8−10

7−10

6−10

5−10

4−10

3−10

2−10

1−10

1

10)2 (1

/GeV

/cee

dN/d

M

SE+-ee (comb.)

(comb.)πe (comb.)ππ


Signal-to-background ratio

Impact of the electron-pion dominance at larger invariant masses

• The signal-to-background ratio is in the order

of 1:10000 for masses larger than 1.5 GeV/ • This is a result of the remaining pions

• Additionally, the ratio is further decreasing towards larger masses

c2

0 0.5 1 1.5 2 2.5)2 (GeV/ceeM

5−10

4−10

3−10

2−10

1−10

1

10

S/B

12 GeV


Corrected invariant mass spectrum of embedded signalsThermal radiation • The thermal component has very good statistics and shows the expected yields per event

• The intermediate mass range looks very promising for the extraction of the inverse slope parameter

Vectormesons The vectormeson cocktail is also well reconstructed and suffers less from statistical limitations

Unlike-sign spectrum (SE+-) Very low background statistics in the IMR

• Background subtraction is not reasonable with these fluctuations

0 0.5 1 1.5 2 2.5)2 (GeV/ceeM

8−10

7−10

6−10

5−10

4−10

3−10

2−10

1−10

1

10

210)2 (c

orr.)

[1/e

v.] (

1/G

eV/c

eedN

/dM

SE+--e+ eγ → 0π

-e+ eγ → η-e+ e0π → ω

-e+ e→ ω-e+ e→ φ

-e+ eγ → 'η-e+ e→ 0ρ

-e+ p e→ Δρin-medium

thermal radiation (Rapp)


Inverse slope parameter

Background subtraction was done with MC information due to statistics

The original information seems to be simulated and reconstructed very well

0 0.5 1 1.5 2 2.5 3)2 (GeV/ceeM

9−10

7−10

5−10

3−10

1−10

10

310

510

710

910

1110) [

corre

cted

.]2

(1/G

eV/c

eedN

/dM

/ ndf 2χ 05 / 1198− 1.319ep0 9.03e+06± 9.07e+04 p1 2.1067± 0.1787

6Theory spectra x 10

3MC Signal x 10

+ QGPρRec in-med

Au+Au 12 A GeV 0-10%CBM simulation

0.5 MeV±Theory T: 178.6 1 MeV±MC T: 176.6 7 MeV±REC T: 174.9

f(Mee) = c ⋅ M3/2ee ⋅ exp−Mee/T


Improvement of the pion suppression with machine learning

Idea Using an ANN to enhance pion suppression via the combination of information

Architectures I tried different network architectures of fully connected and convolutional networks

Input As input I combined detector information from multiple sub-systems:


Combinatorial background with ANN

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2)2 (GeV/ceeM

5−10

4−10

3−10

2−10

1−10

1

10

210

S/B regular cuts

linear convolutional

softmax fully connected

regular cuts + softmax fully connected

0 0.5 1 1.5 2 2.5)2 (GeV/ceeM

8−10

7−10

6−10

5−10

4−10

3−10

2−10

1−10

1

10

210)2 (c

orr.)

[1/e

v.] (

1/G

eV/c

eedN

/dM

SE+-ee (comb.)

(comb.)πe (comb.)ππ

Signal-to-background comparison (top) •The usage of machine learning seems to improve the pion-suppresion in general

•However, the best performance was achieved via a combination of regular PID cuts and machine learning (blue line)

Background contributions •The pion-pion contributions are far stronger suppressed and the electron-electron contributions is dominant over the whole invariant mass range

•The statistics from the pairing process reduce event further, which increases fluctuations even more


ConclusionAnalysis procedure • The general dielectron analysis chain works very well • Event-mixing and corrections are possible • Analysis is limited by background statistics

Thermal signal • The embedding with a uniform mass distribution provides sufficient statistics for the

IMR • Invariant mass spectra scaled to the yield per event look as expected

Machine learning • The usage of machine learning looks promising • Further investigations could be made but are time intensive

Discussion: How can the fluctuations in the IMR background be reduced?

-> Realistic estimation of the errors?

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Analysis of thermal dielectron channels (status)...4.9.20 Etienne Bechtel - Dilepton meeting - GSI,...

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