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The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require a…

Because exoplanets are extremely dim, an Electron Multiplying Charged Coupled Device (EMCCD) operating in photon counting (PC) mode is necessary to reduce the detector noise level and enable their detection. Typically, PC images are added…

Faint tidal features around galaxies record their merger and interaction histories over cosmic time. Due to their low surface brightnesses and complex morphologies, existing automated methods struggle to detect such features and most work…

In the identification of new planetary candidates in transit surveys, the employment of Deep Learning models proved to be essential to efficiently analyse a continuously growing volume of photometric observations. To further improve the…

We present a novel machine-learning approach for detecting faint point sources in high-contrast adaptive optics imaging datasets. The most widely used algorithms for primary subtraction aim to decouple bright stellar speckle noise from…

This study presents a comprehensive evaluation of various classification algorithms used for the detection of exoplanets using labeled time series data from the Kepler mission. The study investigates the performance of six commonly employed…

Detection of a planetary ring of exoplanets remains as one of the most attractive but challenging goals in the field. We present a methodology of a systematic search for exoplanetary rings via transit photometry of long-period planets. The…

The increasing number of transiting exoplanets sparked a significant interest in discovering their moons. Most of the methods in the literature utilize timing analysis of the raw light curves. Here we propose a new approach for the direct…

Many moons have been detected around planets in our Solar System, but none has been detected unambiguously around any of the confirmed extrasolar planets. We test the feasibility of a supervised convolutional neural network to classify…

Approximate Nearest Neighbor Search (ANNS) has become fundamental to modern deep learning applications, having gained particular prominence through its integration into recent generative models that work with increasingly complex datasets…

We present a new technique for detecting scattered starlight from transiting, close-orbiting extrasolar giant planets (CEGPs) that has the virtues of simplicity, robustness, linearity, and model-independence. Given a series of stellar…

Efficient on-device Convolutional Neural Network (CNN) training in resource-constrained mobile and edge environments is an open challenge. Backpropagation is the standard approach adopted, but it is GPU memory intensive due to its strong…

We present a fast and efficient hybrid algorithm for selecting exoplanetary candidates from wide-field transit surveys. Our method is based on the widely-used SysRem and Box Least-Squares (BLS) algorithms. Patterns of systematic error that…

Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One…

Here we describe a Jupyter notebook demonstrating methods for the reduction and analysis of exoplanet transit observations taken with the WFC3/UVIS G280 grism. Released on Space Telescope's hst_notebooks GitHub repository, this notebook…

In this work, we propose a new flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved. Compared to traditional methods…

We present TSARDI, an efficient rejection algorithm designed to improve the transit detection efficiency in data collected by large scale surveys. TSARDI is based on the Machine Learning clustering algorithm DBSCAN, and its purpose is to…

We present a GPU-accelerated transient detection pipeline developed for time-domain surveys with the Dark Energy Camera (DECam). It enables real-time-capable image processing, incorporating science-driven candidate filtering to support…

Correlation filter (CF) based tracking algorithms have demonstrated favorable performance recently. Nevertheless, the top performance trackers always employ complicated optimization methods which constraint their real-time applications. How…

Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the GCN-based methods in this area train a deep feed-forward…