Exploring the Critical Points in QCD with Multi-Point Pad\'e and Machine Learning Techniques in (2+1)-flavor QCD

Author:

Jishnu Goswami, D. A. Clarke, P. Dimopoulos, F. Di Renzo, C. Schmidt, S. Singh, K. Zambello

Keyword:

High Energy Physics - Lattice, High Energy Physics - Lattice (hep-lat), High Energy Physics - Phenomenology (hep-ph)

journal:

date:

2024-01-11 00:00:00

Abstract

Using simulations at multiple imaginary chemical potentials for $(2+1)$-flavor QCD, we construct multi-point Pad\'e approximants. We determine the singularties of the Pad\'e 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.

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