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arXiv · 2401.05651

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

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

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Jishnu Goswami, D. A. Clarke, P. Dimopoulos, F. Di Renzo, C. Schmidt, S. Singh, K. Zambello. 2024-01-11. Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD. https://doi.org/10.1051/epjconf%2F202429606007

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