Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD
Goswami J, Clarke DA, Dimopoulos P, Renzo FD, Schmidt-Sonntag C, Singh S, Zambello K (2024)
arXiv:2401.05651.
Preprint | Englisch
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Autor*in
Goswami, JishnuUniBi ;
Clarke, D. A.;
Dimopoulos, P.;
Renzo, F. Di;
Schmidt-Sonntag, ChristianUniBi ;
Singh, SimranUniBi;
Zambello, K.
Abstract / Bemerkung
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. e extrapolate to the QCD critical point
using an appropriate scaling ansatz.
Erscheinungsjahr
2024
Zeitschriftentitel
arXiv:2401.05651
Seite(n)
4
Page URI
https://pub.uni-bielefeld.de/record/2986081
Zitieren
Goswami J, Clarke DA, Dimopoulos P, et al. Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD. arXiv:2401.05651. 2024.
Goswami, J., Clarke, D. A., Dimopoulos, P., Renzo, F. D., Schmidt-Sonntag, C., Singh, S., & Zambello, K. (2024). Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD. arXiv:2401.05651
Goswami, Jishnu, Clarke, D. A., Dimopoulos, P., Renzo, F. Di, Schmidt-Sonntag, Christian, Singh, Simran, and Zambello, K. 2024. “Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD”. arXiv:2401.05651.
Goswami, J., Clarke, D. A., Dimopoulos, P., Renzo, F. D., Schmidt-Sonntag, C., Singh, S., and Zambello, K. (2024). Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD. arXiv:2401.05651.
Goswami, J., et al., 2024. Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD. arXiv:2401.05651.
J. Goswami, et al., “Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD”, arXiv:2401.05651, 2024.
Goswami, J., Clarke, D.A., Dimopoulos, P., Renzo, F.D., Schmidt-Sonntag, C., Singh, S., Zambello, K.: Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD. arXiv:2401.05651. (2024).
Goswami, Jishnu, Clarke, D. A., Dimopoulos, P., Renzo, F. Di, Schmidt-Sonntag, Christian, Singh, Simran, and Zambello, K. “Exploring the Critical Points in QCD with Multi-Point Padé and Machine Learning Techniques in (2+1)-flavor QCD”. arXiv:2401.05651 (2024).