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Found 11 papers, 5 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.
Fast Regression of the Tritium Breeding Ratio in Fusion Reactors
The tritium breeding ratio (TBR) is an essential quantity for the design of modern and next-generation D-T fueled nuclear fusion reactors.
Lossless Compression and Generalization in Overparameterized Models: The Case of Boosting
Successful learning algorithms like DNNs, kernel methods or ensemble learning methods, have been known to produce models that exhibit good generalization despite being drawn from overparameterized model families.
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.
PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch
We present a new open source python package, based on PyLightcurve and PyTorch, tailored for efficient computation and automatic differentiation of exoplanetary transits.
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.
Inferring Causal Direction from Observational Data: A Complexity Approach
At the heart of causal structure learning from observational data lies a deceivingly simple question: given two statistically dependent random variables, which one has a causal effect on the other?
Margin Maximization as Lossless Maximal Compression
The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples.
Better Boosting with Bandits for Online Learning
The outputs of the ensemble need to be properly calibrated before they can be used as probability estimates.
Detrending Exoplanetary Transit Light Curves with Long Short-Term Memory Networks
We train a probabilistic Long Short-Term Memory (LSTM) network to predict the next data point of the light curve during the out-of-transit, and use this model to reconstruct a transit-free light curve - i. e. including only the systematics - during the in-transit.
Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics
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
