Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce. To quantify the suitability of deep learning to estimate the individual spins, effective spin and m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-09-01
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| Series: | Physics Letters B |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0370269320304317 |