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.
Exploring the critical points in QCD with multi-point Padé and machine learning techniques in (2+1)-flavor QCD / Goswami, J.; Clarke, D. A.; Dimopoulos, P.; Di Renzo, F.; Schmidt, C.; Singh, S.; Zambello, K.. - In: EPJ WEB OF CONFERENCES. - ISSN 2101-6275. - 296:(2024). (Intervento presentato al convegno 30th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions, Quark Matter 2023 tenutosi a Houston - USA nel 3 September 2023 through 9 September 2023) [10.1051/epjconf/202429606007].
Exploring the critical points in QCD with multi-point Padé and machine learning techniques in (2+1)-flavor QCD
Dimopoulos P.;Di Renzo F.;Zambello K.
2024-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
