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Band-to-band photoluminescence (PL) imaging is one of the experimental techniques widely used to assess non-radiative recombination rates at a fixed incident light intensity. Minority carrier lifetimes in semiconductors such as mc-Si are also affected by optical injection levels. These can be measured by transient photoconductance (TPC). In this paper, PL imaging of shunts and TPC lifetime results for incident intensities of up to 50 Suns are compared for multiple samples of mc-Si.
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This content will become publicly available on May 8, 2026
A Machine Learning Method for Monte Carlo Calculations of Radiative Processes
Radiative processes such as synchrotron radiation and Compton scattering play an important role in astrophysics. Radiative processes are fundamentally stochastic in nature, and the best tools currently used for resolving these processes computationally are Monte Carlo (MC) methods. These methods typically draw a large number of samples from a complex distribution such as the differential cross section for electron–photon scattering, and then use these samples to compute the radiation properties such as angular distribution, spectrum, and polarization. In this work, we propose a machine learning (ML) technique for efficient sampling from arbitrary known probability distributions that can be used to accelerate MC calculation of radiative processes in astrophysical scenarios. In particular, we apply our technique to inverse Compton radiation and find that our ML method can be up to an order of magnitude faster than traditional methods currently in use.
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- Award ID(s):
- 2308111
- PAR ID:
- 10632076
- Publisher / Repository:
- American Astronomical Society
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 984
- Issue:
- 2
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 172
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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