1. Applying the Deep learning technique to NA49/61 data.
For the Na61 collaboration meeting, September 13th, CERN
Jan Steinheimer
In collaboration with Kai Zhou

2. From hydro distributions to finite number of particles
Smooth hydro result is not really realistic.
Finite number of particles

3. Taking onto account the hadronic re-scattering
Usually done in hadronic transport, i.e. UrQMD
Also takes care of resonance decays

4. Acceptance is not perfect
In addition:
Efficiency
Weak decays

5. Things that are currently being considered
Finite number of particle sampling: Only small decrease of prediction quality (5-15%)
Quality of result depends on particle number in event! (Yulin Du PhD student)
Result also seems independent on event averaging. (Yulin Du PhD student)
Hadronic re-scattering: Is being done in the UrQMD transport model (Y. Du PhD student)
Consistence between hydro+ EoS and microscopic transport + EoS
Study with JAM transport model is underway (Y. Nara)
Here any EoS can be implemented in the transport model
Comparison between hydro and transport to check robustness of result!

6. Things that are currently being considered
Finite number of particle sampling: Only small decrease of prediction quality (5-15%)
Quality of result depends on particle number in event! (Yulin Du PhD student)
Result also seems independent on event averaging. (Yulin Du PhD student)
Hadronic re-scattering: Is being done in the UrQMD transport model (Y. Du PhD student)
Consistence between hydro+ EoS and microscopic transport + EoS
Study with JAM transport model is underway (Y. Nara)
Here any EoS can be implemented in the transport model
Comparison between hydro and transport to check robustness of result!
In principle any acceptance can be implemented in the event generator
Efficiency is more complicated
Try to find signals which depend weakly on efficiency.

7. Is the EoS really a classification? Or rather regression?
Currently only 2 cases for the EoS are compared.
Wolf OR Dog
Phase transition OR Crossover
What if reality is more like this?
A weak phase transition
or strong crossover?
Possible solution: Study different phase transitions with varying latent heat as regression parameter etc.
Conclusion: Most issues can be overcome,
making machine-/deep-learning a valuable tool for heavy ion data analysis!

8. What can NA49/61 provide to do the analysis?
Deep-Learning analysis relies on a large set of events as input.
Can only da an event-by-event statistical analysis
Each event can only be classified with a certain probability
Questions?
Can NA49/61 provide event-by-event differential spectra?
For pions, pions+protons or charged particles?
Are these corrected for weak feed-down?
Find a balance between necessary corrections and event-by-event data
Big chance to re-analyse old data use new data!