Related papers: Detecting Exoplanet Transits through Machine Learn…
Transiting planet systems offer an unique opportunity to observationally constrain proposed models of the interiors (radius, composition) and atmospheres (chemistry, dynamics) of extrasolar planets. The spectacular successes of ground-based…
Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP decomposition and a few others have been applied in…
This paper describes a simple activity for plotting and characterizing the light curve from an exoplanet transit event by way of differential photometry analysis. Using free digital imaging software, participants analyse a series of…
In the preparation for ESA's Euclid mission and the large amount of data it will produce, we train deep convolutional neural networks on Euclid simulations classify solar system objects from other astronomical sources. Using transfer…
The interpretation of the origin of observed exoplanets is usually done only qualitatively due to uncertainties of key parameters in planet formation models. To allow a quantitative methodology which traces back in time to the planet birth…
To date a dozen transiting "Tatooines" or circumbinary planets (CBPs) have been discovered, by eye, in the data from the Kepler mission; by contrast, thousands of confirmed circumstellar planets orbiting around single stars have been…
This study employs scientific machine learning to identify transient time series of dynamical systems near a fold bifurcation of periodic solutions. The unique aspect of this work is that a convolutional neural network (CNN) is trained with…
Transit photometry is perhaps the most successful method for detecting exoplanets to date. However, a substantial amount of signal processing is needed since the dip in the signal detected, an indication that there is a planet in transit,…
We describe work in progress by a collaboration of astronomers and statisticians developing a suite of Bayesian data analysis tools for extrasolar planet (exoplanet) detection, planetary orbit estimation, and adaptive scheduling of…
The interest for change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly…
The field of collaborative robotics and human-robot interaction often focuses on the prediction of human behaviour, while assuming the information about the robot setup and configuration being known. This is often the case with fixed…
A unique analytical solution of planet and star parameters can be derived from an extrasolar planet transit light curve under a number of assumptions. This analytical solution can be used to choose the best planet transit candidates for…
Gravitational microlensing is one of the methods to detect exoplanets; planets outside our solar system. Here we focus on theoretical modeling of three lens systems and in particular circumbinary systems. Circumbinary systems include two…
Here we describe a story behind the discovery of Kepler-46, which was the first exoplanetary system detected and characterized from a method known as the transit timing variations (TTVs). The TTV method relies on the gravitational…
Convolutional Neural Network (CNN) techniques have proven to be very useful in image-based anomaly detection applications. CNN can be used as deep features extractor where other anomaly detection techniques are applied on these features.…
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…
The precise derivation of transit depths from transit light curves is a key component for measuring exoplanet transit spectra, and henceforth for the study of exoplanet atmospheres. However, it is still deeply affected by various kinds of…
Detecting exoplanet transits at X-ray wavelengths would provide a window into the effects of high energy irradiation on the upper atmospheres of planets. However, stars are relatively dim in the X-ray, making exoplanet transit detections…
Despite their successes in the field of self-learning AI, Convolutional Neural Networks (CNNs) suffer from having too many trainable parameters, impacting computational performance. Several approaches have been proposed to reduce the number…
Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W…