High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. If the likelihood is known, inference can proceed using standard techniques. However, when the likelihood is intractable or unknown, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations when coupled with machine learning. We introduce an extension of the recently proposed likelihood-free frequentist inference ( Code to reproduce all of our results is available on
We present the first release of the Gravitational Wave AfterglowPy Analysis (GWAPA) webtool (Available at
- NSF-PAR ID:
- 10487069
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- Research Notes of the AAS
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2515-5172
- Format(s):
- Medium: X Size: Article No. 27
- Size(s):
- Article No. 27
- Sponsoring Org:
- National Science Foundation
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