Related papers: Evaluating Classification Algorithms: Exoplanet De…

Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well proven object…

The classification of exoplanets has been a longstanding challenge in astronomy, requiring significant computational and observational resources. Traditional methods demand substantial effort, time, and cost, highlighting the need for…

We consider the so-called Keplerian periodogram, in which the putative detectable signal is modelled by a highly non-linear Keplerian radial velocity function, appearing in Doppler exoplanetary surveys. We demonstrate that for planets on…

Vetting of exoplanet candidates in transit surveys is a manual process, which suffers from a large number of false positives and a lack of consistency. Previous work has shown that Convolutional Neural Networks (CNN) provide an efficient…

The radial-velocity (RV) method is one of the most successful in the detection of exoplanets, but is hindered by the intrinsic RV variations of the star, which can easily mimic or hide true planetary signals. kima is a package for the…

We present a preliminary analysis of the sensitivity of Anglo-Australian Planet Search data to the orbital parameters of extrasolar planets. To do so, we have developed new tools for the automatic analysis of large-scale simulations of…

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric…

This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures using a low-dimensional data representation. We use the…

Aims. Construction of a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested…

It has recently been demonstrated that deep learning has significant potential to automate parts of the exoplanet detection pipeline using light curve data from satellites such as Kepler \cite{borucki2010kepler} \cite{koch2010kepler} and…

EXONEST is an algorithm dedicated to detecting and characterizing the photometric signatures of exoplanets, which include reflection and thermal emission, Doppler boosting, and ellipsoidal variations. Using Bayesian Inference, we can test…

This study introduces an approach to detecting exocomet transits in the dataset of the Transiting Exoplanet Survey Satellite (TESS), specifically within its Sector 1. Given the limited number of exocomet transits detected in the observed…

Targeted observations of possible exomoon host systems will remain difficult to obtain and time-consuming to analyze in the foreseeable future. As such, time-domain surveys such as Kepler, K2 and TESS will continue to play a critical role…

We are at a unique timeline in the history of human evolution where we may be able to discover earth-like planets around stars outside our solar system where conditions can support life or even find evidence of life on those planets. With…

We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program of the mission. The methods, originally…

The estimation of periodicity is a fundamental task in many scientific areas of study. Existing methods rely on theoretical assumptions that the observation times have equal or i.i.d. spacings, and that common estimators, such as the…

NASA's Transiting Exoplanet Survey Satellite (TESS) presents us with an unprecedented volume of space-based photometric observations that must be analyzed in an efficient and unbiased manner. With at least $\sim1,000,000$ new light curves…

The new generation of observatories and instruments (VLT/ERIS, JWST, ELT) motivate the development of robust methods to detect and characterise faint and close-in exoplanets. Molecular mapping and cross-correlation for spectroscopy use…

We present the Signal Detection using Random-Forest Algorithm (SIDRA). SIDRA is a detection and classification algorithm based on the Machine Learning technique (Random Forest). The goal of this paper is to show the power of SIDRA for quick…

The second mission of the NASA Kepler satellite, K2, has collected hundreds of thousands of lightcurves for stars close to the ecliptic plane. This new sample could increase the number of known pulsating stars and then improve our…