From NS observations to nuclear matter properties: a machine learning approach
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914957440843776 |
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| author | Carvalho, Valéria Ferreira, Márcio Providência, Constança |
| author_facet | Carvalho, Valéria Ferreira, Márcio Providência, Constança |
| contents | This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the uncertainties associated with its predictions. To simulate different noise levels on the $M(R)$ observations, we create three different sets of mock data. Our results show BNNs as an accurate and reliable tool for predicting the nuclear matter properties whenever the true values are not completely outside the training dataset statistics, i.e., if the model is not heavily dependent on its extrapolating capacities. Using real mass-radius pulsar data, the model predicted, for instance, $L_{\text{sym}}=39.80\pm17.52 $ MeV and $K_{\text{sym}}=-101.67\pm62.86 $ MeV ($2σ$ interval). Our study provides a valuable inference framework when new NS data becomes available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From NS observations to nuclear matter properties: a machine learning approach Carvalho, Valéria Ferreira, Márcio Providência, Constança Nuclear Theory High Energy Astrophysical Phenomena High Energy Physics - Phenomenology This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the uncertainties associated with its predictions. To simulate different noise levels on the $M(R)$ observations, we create three different sets of mock data. Our results show BNNs as an accurate and reliable tool for predicting the nuclear matter properties whenever the true values are not completely outside the training dataset statistics, i.e., if the model is not heavily dependent on its extrapolating capacities. Using real mass-radius pulsar data, the model predicted, for instance, $L_{\text{sym}}=39.80\pm17.52 $ MeV and $K_{\text{sym}}=-101.67\pm62.86 $ MeV ($2σ$ interval). Our study provides a valuable inference framework when new NS data becomes available. |
| title | From NS observations to nuclear matter properties: a machine learning approach |
| topic | Nuclear Theory High Energy Astrophysical Phenomena High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2401.05770 |