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

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.

arXiv comment4 pages, prepared for Quark Matter 2023

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Cited by in corpus (1)

• Coordinate versus momentum cuts and effects of collective flow on critical fluctuations