https://doi.org/10.1051/epjconf/202429606007
Exploring the critical points in QCD with multi-point Padé and machine learning techniques in (2+1)-flavor QCD
1 RIKEN Center for Computational Science, Kobe 650-0047, Japan
2 Department of Physics and Astronomy, University of Utah, Salt Lake City, Utah 84112, United States
3 Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università di Parma and INFN, Gruppo Collegato di Parma I-43100 Parma, Italy
4 Fakultät für Physik, Universität Bielefeld, D-33615 Bielefeld, Germany
5 Dipartimento di Fisica dell’Università di Pisa and INFN–Sezione di Pisa, Largo Pontecorvo 3, I-56127 Pisa, Italy
* e-mail: jishnu.goswami@riken.jp
Published online: 26 June 2024
Using simulations at multiple imaginary chemical potentials for (2 + 1)-flavor QCD, we construct multi-point Padé approximants. We determine the singularties of the Padé approximants and demonstrate that they are consistent with the expected universal scaling behaviour of the Lee-Yang edge singularities. We also use a machine learning model, Masked Autoregressive Density Estimator (MADE), to estimate the density of the Lee-Yang edge singularities at each temperature. This ML model allows us to interpolate between the temperatures. Finally, we extrapolate to the QCD critical point using an appropriate scaling ansatz.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
