Machine learning approaches to the QCD transition
- We study the high temperature transition in pure SU(3) gauge theory and in full QCD with 3D-convolutional neural networks trained as parts of either unsupervised or semi-supervised learning problems. Pure gauge configurations are obtained with the MILC public code and full QCD are from simulations of Nf=2+1+1 Wilson fermions at maximal twist. We discuss the capability of different approaches to identify different phases using as input the configurations of Polyakov loops. To better expose fluctuations, a standardized version of Polyakov loops is also considered.
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| Author: | Andrea PalermoORCiD, Lucio AnderliniORCiDGND, Maria Paola LombardoORCiDGND, Andrey KotovORCiD |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-705976 |
| DOI: | https://doi.org/10.48550/arXiv.2111.05216 |
| ArXiv Id: | http://arxiv.org/abs/2111.05216 |
| Document Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2022/04/07 |
| Date of first Publication: | 2022/04/07 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Contributing Corporation: | International Symposium on Lattice Field Theory (38. : 2021 : Online) |
| Release Date: | 2023/01/24 |
| Page Number: | 7 |
| Institutes: | Physik / Physik |
| Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
