A differentiable binary microlensing model using adaptive contour integration method
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915153722736640 |
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| author | Ren, Haibin Zhu, Wei |
| author_facet | Ren, Haibin Zhu, Wei |
| contents | We present microlux, which is a Jax-based code that can compute the binary microlensing light curve and its derivatives both efficiently and accurately. The key feature of microlux is the implementation of a modified version of the adaptive sampling algorithm that was originally proposed by V. Bozza to account for the finite-source effect most efficiently. The efficiency and accuracy of microlux have been verified across the relevant parameter space for binary microlensing. As a differentiable code, microlux makes it possible to apply gradient-based algorithms to the search and posterior estimation of the microlensing modeling. As an example, we use microlux to model a real microlensing event and infer the model posterior via both Fisher information matrix and Hamiltonian Monte Carlo, neither of which would have been possible without the access to accurate model gradients. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07268 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A differentiable binary microlensing model using adaptive contour integration method Ren, Haibin Zhu, Wei Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics We present microlux, which is a Jax-based code that can compute the binary microlensing light curve and its derivatives both efficiently and accurately. The key feature of microlux is the implementation of a modified version of the adaptive sampling algorithm that was originally proposed by V. Bozza to account for the finite-source effect most efficiently. The efficiency and accuracy of microlux have been verified across the relevant parameter space for binary microlensing. As a differentiable code, microlux makes it possible to apply gradient-based algorithms to the search and posterior estimation of the microlensing modeling. As an example, we use microlux to model a real microlensing event and infer the model posterior via both Fisher information matrix and Hamiltonian Monte Carlo, neither of which would have been possible without the access to accurate model gradients. |
| title | A differentiable binary microlensing model using adaptive contour integration method |
| topic | Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2501.07268 |