Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910645028388864 |
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| author | Cox, Nicholas Grundler, Xavier Li, Bao-An |
| author_facet | Cox, Nicholas Grundler, Xavier Li, Bao-An |
| contents | Applications of new techniques in machine learning are speeding up progress in research in various fields. In this work, we construct and evaluate a deep neural network (DNN) to be used within a Bayesian statistical framework as a faster and more reliable alternative to the Gaussian Process (GP) emulator of an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model simulator of heavy-ion reactions at intermediate beam energies. We found strong evidence of DNN being able to emulate the IBUU simulator's prediction on the strengths of protons' directed and elliptical flow very efficiently even with small training datasets and with accuracy about ten times higher than the GP. Limitations of our present work and future improvements are also discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18421 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies Cox, Nicholas Grundler, Xavier Li, Bao-An Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment Applications of new techniques in machine learning are speeding up progress in research in various fields. In this work, we construct and evaluate a deep neural network (DNN) to be used within a Bayesian statistical framework as a faster and more reliable alternative to the Gaussian Process (GP) emulator of an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model simulator of heavy-ion reactions at intermediate beam energies. We found strong evidence of DNN being able to emulate the IBUU simulator's prediction on the strengths of protons' directed and elliptical flow very efficiently even with small training datasets and with accuracy about ten times higher than the GP. Limitations of our present work and future improvements are also discussed. |
| title | Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies |
| topic | Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment |
| url | https://arxiv.org/abs/2406.18421 |