In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (
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.
- Award ID(s):
- 1907342
- Publication Date:
- NSF-PAR ID:
- 10368333
- Journal Name:
- The Astronomical Journal
- Volume:
- 164
- Issue:
- 1
- Page Range or eLocation-ID:
- Article No. 17
- ISSN:
- 0004-6256
- Publisher:
- DOI PREFIX: 10.3847
- Sponsoring Org:
- National Science Foundation
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