2021/01/20 by Yuki Fujimoto, Kenji Fukushima, Koichi Murase
Computer Science · Physics and Astronomy · #Artificial neural network #Astrophysical Phenomena and Observations #Convolutional neural network #Data set #Deep learning #Equation of state #Function (biology) #Inference #Model Reduction and Neural Networks #Neutron star #Observational study #Overfitting #Pulsars and Gravitational Waves Research #astro-ph.HE #astro-ph.IM #cs.LG #hep-ph #nucl-th
paper · pdf · doi:10.1007/jhep03(2021)273
published as JHEP 03 (2021) 273 · 45 pages, 25 figures
arxiv created 2021/01/20 · openalex created_date 2021/02/01 · openalex publication_date 2021/03/30 · arxiv updated 2021/06/14 · openalex updated_date 2026/08/05
A bstract We discuss deep learning inference for the neutron star equation of state (EoS) using the real observational data of the mass and the radius. We make a quantitative comparison between the conventional polynomial regression and the neural network approach for the EoS parametrization. For our deep learning method to incorporate uncertainties in observation, we augment the training data with noise fluctuations corresponding to observational uncertainties. Deduced EoSs can accommodate a weak first-order phase transition, and we make a histogram for likely first-order regions. We also find that our observational data augmentation has a byproduct to tame the overfitting behavior. To check the performance improved by the data augmentation, we set up a toy model as the simplest inference problem to recover a double-peaked function and monitor the validation loss. We conclude that the data augmentation could be a useful technique to evade the overfitting without tuning the neural network architecture such as inserting the dropout.