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
Stellar variability is a limiting factor for planet detection and characterization, particularly around active M-type stars. Here we revisit one of the most active stars from the Kepler mission, the M4 star GJ 1243, and use a sample of 414 flare events from 11 months of 1-minute cadence light curves to study the empirical morphology of white-light stellar flares. We use a Gaussian process detrending technique to account for the underlying starspots. We present an improved analytic, continuous flare template that is generated by stacking the flares onto a scaled time and amplitude and uses a Markov Chain Monte Carlo analysis to fit the model. Our model is defined using classical flare events but can also be used to model complex, multipeaked flare events. We demonstrate the utility of our model using TESS data at the 10-minute, 2-minute, and 20 s cadence modes. Our new flare model code is made publicly available on GitHub.5
- Award ID(s):
- 1907342
- NSF-PAR ID:
- 10368333
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astronomical Journal
- Volume:
- 164
- Issue:
- 1
- ISSN:
- 0004-6256
- Format(s):
- Medium: X Size: Article No. 17
- Size(s):
- ["Article No. 17"]
- Sponsoring Org:
- National Science Foundation
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