An EoS-meter of QCD transition from deep learning

Transcription

1 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 Stöcker (FIAS/Uni Frankfurt/GSI), Xin-Nian Wang (CCNU/LBNL) [arxiv: ] University of Chinese Academy of Sciences May 26, 2017 Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 1 / 18

2 Introduction booming of deep learning AlphaGo obsession AlphaGo 4 : Lee Sedol 1 Seoul, March 2016 AlphaGo Master vs Ke Jie Wuzhen, May 2017 Google DeepMind, London Nature 529, (2016) Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 2 / 18

3 Introduction booming of deep learning deep learning in a nutshell deep learning is a branch of machine learning aiming at understanding high-level representations of data using a deeper structure of multiple processing layers Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 3 / 18

4 Introduction booming of deep learning more examples generation of artistic style paintings Gatys, Ecker, Bethge, arxiv: generation of Chinese poetry Zhang et al., arxiv: Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 4 / 18

5 Introduction booming of deep learning industrial & social impacts Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 5 / 18

6 Introduction applications in physics physics applications: particle physics Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 6 / 18

7 Introduction applications in physics physics applications: condensed matter physics Discovering phase transitions with unsupervised learning, L. Wang, Phys. Rev. B 94, (2016) Machine learning phases of matter, J. Carrasquilla and R. G. Melko, Nature Physics 13, (2017) Learning phase transitions by confusion, E. P. L. van Nieuwenburg, Y.-H. Liu and S. D. Huber, Nature Physics 13, (2017) PHASE CLASSIFICATION: machine/deep learning is formidable in extracting pertinent features especially for complex non-linear systems with high-order correlations that beyond the scope of conventional techniques Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 7 / 18

8 Introduction applications in physics convolutional neural network (pattern recognition, image classification) Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 8 / 18

9 Heavy-Ion Physics and QCD transition open challenges relativistic heavy-ion collisions (RHIC & LHC) QCD transition and quark-gluon plasma quark gluon plasma temperature T crossover critical point first order phase transition EOS hadronic matter color superconductor baryon chemical potential µ B Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 9 / 18

10 Heavy-Ion Physics and QCD transition open challenges relativistic heavy-ion collisions (RHIC & LHC) exp measurement: final-state spectra ρ(p T, Φ) highly complex direct access to QGP bulk properties impossible no noticeable and unique mapping b.t. ρ(p T, Φ) and bulk properties (e.g. EoS) using conventional observables setup dependence significant uncertainties in testing non-perturbative QCD in the bulk through heavy-ion experiments! Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 10 / 18

11 Heavy-Ion Physics and QCD transition open challenges relativistic heavy-ion collisions (RHIC & LHC) 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, (2017) Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 11 / 18

12 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, (2012) Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 12 / 18

13 Heavy-Ion Physics and QCD transition an EoS-meter of QCD transition 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) Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 13 / 18

14 Heavy-Ion Physics and QCD transition an EoS-meter of QCD transition CNN architecture particle spectra 15x48 16 features 15x48 32 features 8x24 flattened fc 128 output layer EOS crossover st order 8x8 conv, 16 dropout(0.2) bn, PReLu 7x7x16 conv, 32 dropout(0.2) bn, avgpool, PReLu dropout(0.5) bn,sigmoid Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 14 / 18

15 Heavy-Ion Physics and QCD transition an EoS-meter of QCD transition testing results Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 15 / 18

16 Heavy-Ion Physics and QCD transition an EoS-meter of QCD transition importance maps Visualizing Deep Neural Network Decisions: Prediction Difference Analysis, L. M Zintgraf, T. S. Cohen, T. Adel, M. Welling, arxiv: Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 16 / 18

17 Heavy-Ion Physics and QCD transition an EoS-meter of QCD transition novel perspectives 1st application of deep learning to high-energy nuclear physics with CNN, we demonstrate the existence of discriminative and traceable projections encoders from the QCD transition onto ρ(p T, Φ) in the complex and highly dynamical heavy-ion collisions CNN provides a powerful and efficient decoder for extracting EoS information directly from ρ(p T, Φ) EoS-meter extend to other properties and real experimental data a new angle on the experimental search for QCD critical point Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 17 / 18

18 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 a real-world problem and formulate it into a mathematical form for the objective 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 much better defined than conventional deep learning ones (e.g. image/natural language processing) much more economic and efficient in tackling deep learning is a black box simple physical systems as benchmark renormalization group principle component analysis Nan Su (FIAS) QCD transition & deep learning UCAS, 26/05/17 18 / 18