期刊
JOURNAL OF HIGH ENERGY PHYSICS
卷 -, 期 3, 页码 -出版社
SPRINGER
DOI: 10.1007/JHEP03(2021)273
关键词
QCD Phenomenology
资金
- JSPS KAKENHI [20J10506, 18H01211, 19K21874]
- Grants-in-Aid for Scientific Research [20J10506, 19K21874] Funding Source: KAKEN
This study demonstrates that incorporating noise fluctuations corresponding to observational uncertainties in the training data when using deep learning inference for the neutron star equation of state can effectively accommodate uncertainties and predict potential phase transitions. Additionally, observational data augmentation can also help mitigate overfitting in the model.
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.
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