2024/01/23 by Yuki Fujimoto, Kenji Fukushima, Fujimoto, Yuki +5 · 5 citations
Earth and Planetary Sciences · Physics and Astronomy · #FOS: Physical sciences #Gamma-ray bursts and supernovae #Geophysics and Gravity Measurements #High Energy Astrophysical Phenomena (astro-ph.HE) #High Energy Physics - Phenomenology (hep-ph) #Nuclear Theory (nucl-th) #Pulsars and Gravitational Waves Research
paper · pdf · doi:10.48550/arxiv.2401.12688
openalex publication_date 2024/01/23 · openalex created_date 2024/01/25 · openalex updated_date 2026/07/28
We discuss the machine-learning inference and uncertainty quantification for the equation of state (EoS) of the neutron star (NS) matter directly using the NS probability distribution from the observations. We previously proposed a prescription for uncertainty quantification based on ensemble learning by evaluating output variance from independently trained models. We adopt a different principle for uncertainty quantification to confirm the reliability of our previous results. To this end, we carry out the MC sampling of data to infer an EoS and take the convolution with the probability distribution of the observational data. In this newly proposed method, we can deal with arbitrary probability distribution not relying on the Gaussian approximation. We incorporate observational data from the recent multimessenger sources including precise mass measurements and radius measurements. We also quantify the importance of data augmentation and the effects of prior dependence.