Related papers: Scientific Domain Knowledge Improves Exoplanet Tra…

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most…

Asteroseismology provides a powerful probe of stellar interiors by detecting stellar oscillations, including solar-like oscillations, which are stochastically excited by near-surface convection. While thousands of solar-like oscillators…

Surveys searching for transiting exoplanets have found many more candidates than they have been able to confirm as true planets. This situation is especially acute with the Kepler survey, which has found over 2300 candidates but has…

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…

Large photometric surveys will image billions of galaxies, but we currently lack quick, reliable automated ways to infer their physical properties like morphology, stellar mass, and star formation rates. Simulations provide galaxy images…

Kepler has revolutionized the study of transiting planets with its unprecedented photometric precision on more than 150,000 target stars. Most of the transiting planet candidates detected by Kepler have been observed as long-cadence targets…

One of the primary mission goals of the Kepler space telescope was to detect Earth-like terrestrial planets in the habitable zone around Sun-like stars. These planets are at the detection limit, where the Kepler detection and vetting…

Red giant asteroseismology can provide valuable information for studying the Galaxy as demonstrated by space missions like CoRoT and Kepler. However, previous observations have been limited to small data sets and fields-of-view. The TESS…

Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets. However, purely data-driven models might struggle when very difficult…

Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the…

Most existing exoplanets are discovered using validation techniques rather than being confirmed by complementary observations. These techniques generate a score that is typically the probability of the transit signal being an exoplanet…

Kepler's short-period exoplanet population has revealed evolutionary features such as the Radius Valley and the Hot Neptune desert that are likely sculpted by atmospheric loss over time. These findings suggest that the primordial planet…

The detection of the first exoplanet paved the way into the era of transit photometry space missions with a revolutionary photometric precision that aim at discovering new exoplanetary systems around different types of stars. With this high…

Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others. Supervised deep learning has shown good performance in this task but using datasets not applicable to real…

We have adapted the algorithmic tools developed during the Kepler mission to vet the quality of transit-like signals for use on the K2 mission data. Using the four sets of publicly-available lightcurves on MAST, we produced a…

Deep learning methods have achieved great success in pedestrian detection, owing to its ability to learn features from raw pixels. However, they mainly capture middle-level representations, such as pose of pedestrian, but confuse positive…

Continuous data releases throughout the TESS primary mission will provide unique opportunities for the exoplanet community at large to contribute to maximizing TESS's scientific return via the discovery and validation of transiting planets.…

Deploying LLMs raises two coupled challenges: (1) monitoring--estimating where a model underperforms as traffic and domains drift--and (2) improvement--prioritizing data acquisition to close the largest performance gaps. We test whether an…

Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine learning techniques such as linear regression and neural networks can…

We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for…