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Found 5 papers, 2 papers with code
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer
Astrophysical light curves are particularly challenging data objects due to the intensity and variety of noise contaminating them.
Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values
Secondly, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features.
Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals
We then present an extensive analysis of the predictions of DNNs, which can inform us - among other things - of the credibility limits for atmospheric parameters for a given instrument and model.
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots.
Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning
Further advances in exoplanet detection and characterisation require sampling a diverse population of extrasolar planets.
Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics
