an eos-meter of qcd transition from deep...

An EoS-meter of QCD transition from deep learning

Nan Su

Frankfurt Institute for Advanced Studies

with Long-Gang Pang, Kai Zhou (FIAS), Hannah Petersen, Horst Stocker (FIAS/UniFrankfurt/GSI), Xin-Nian Wang (CCNU/LBNL)

[arXiv:1612.04262]

University of Chinese Academy of SciencesMay 26, 2017

Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 1 / 18

Introduction booming of deep learning

AlphaGo obsession

AlphaGo 4 : Lee Sedol 1Seoul, March 2016

AlphaGo Master vs Ke JieWuzhen, May 2017

Google DeepMind, LondonNature 529, 484–489 (2016)



deep learning in a nutshell

deep learning is a branch of machine learning aiming at understandinghigh-level representations of data using a deeper structure of multipleprocessing layers



more examples

generation of artistic style paintings

Gatys, Ecker, Bethge, arXiv:1508.06576 generation of Chinese poetry

Zhang et al., arXiv:1705.03773

Google DeepMind, LondonNature 529, 484–489 (28 January 2016)



industrial & social impacts


Introduction applications in physics

physics applications: particle physics



physics applications: condensed matter physics

“Discovering phase transitions with unsupervised learning”, L. Wang,Phys. Rev. B 94, 195105 (2016)

“Machine learning phases of matter”, J. Carrasquilla and R. G.Melko, Nature Physics 13, 431–434 (2017)

“Learning phase transitions by confusion”, E. P. L. van Nieuwenburg,Y.-H. Liu and S. D. Huber, Nature Physics 13, 435–439 (2017)

PHASE CLASSIFICATION: machine/deep learning is formidable inextracting pertinent features especially for complex non-linear systems withhigh-order correlations that beyond the scope of conventional techniques



convolutional neural network(pattern recognition, image classification)


Heavy-Ion Physics and QCD transition open challenges

relativistic heavy-ion collisions (RHIC & LHC)

QCD transition and quark-gluon plasma

tem

pera

ture

T

µBbaryon chemical potential

hadronic matter

quark gluon plasma

color superconductor

cros

sove

r

first order phase transition

critical point

EOS




exp measurement: final-state spectra ρ(pT ,Φ) – highly complex

direct access to QGP bulk properties impossibleno noticeable and unique mapping b.t. ρ(pT ,Φ) and bulk properties(e.g. EoS) using conventional observables – setup dependence

significant uncertainties in testing non-perturbative QCD in the bulkthrough heavy-ion experiments!




CAUTION: model (e.g. event generators) dependence in training“Parton shower uncertainties in jet substructure analyses with deep neural networks”, J. Barnard, E. N. Dawe, M. J.

Dolan, and N. Rajcic, Phys. Rev. D 95, 014018 (2017)


Heavy-Ion Physics and QCD transition an EoS-meter of QCD transition

training dataset

CLVisc hydro package: L.-G. Pang, Q. Wang, and X.-N. Wang, Phys. Rev. C 86, 024911 (2012)



testing dataset

iEBE-VISHNU hydro package: C. Shen, Z. Qiu, H.-C. Song, J. Bernhard, S. Bass, and U. Heinz, Comput. Phys. Commun. 199,

61 (2016)



CNN architecture

crossover

1st order

flattened fc 128

outputlayer

EOS

...

...

particlespectra15x48

16features15x48

32features

8x24

8x8 conv, 16dropout(0.2)bn, PReLu

7x7x16 conv, 32dropout(0.2)bn, avgpool, PReLu

dropout(0.5)bn,sigmoid



testing results



importance maps

“Visualizing Deep Neural Network Decisions: Prediction Difference Analysis”, L. M Zintgraf, T. S. Cohen, T. Adel, M. Welling,

arXiv:1702.04595



novel perspectives

1st application of deep learning to high-energy nuclear physics

with CNN, we demonstrate the existence of discriminative andtraceable projections – “encoders” – from the QCD transition ontoρ(pT ,Φ) in the complex and highly dynamical heavy-ion collisions

CNN provides a powerful and efficient “decoder” for extracting EoSinformation directly from ρ(pT ,Φ) – “EoS-meter”

extend to other properties and real experimental data

a new angle on the experimental search for QCD critical point


Outlook

opportunities as physicists

“Computers will not completely replace human, at least for one kind,which is those who can set the objective function. If you are able to take areal-world problem and formulate it into a mathematical form for theobjective function, you are going to be a master of the future AI system”– Yang Qiang, HKUST

physics and related (e.g. chemistry, engineering) problems are muchbetter defined than conventional deep learning ones (e.g.image/natural language processing) – much more economic andefficient in tackling

deep learning is a black box – simple physical systems as benchmark

renormalization groupprinciple component analysis


an eos-meter of qcd transition from deep...

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