Abstract Machine learning (automated processes that learn by example in order to classify,
predict, discover, or generate new data) and artificial intelligence (methods by which a …

The advent of wide-field sky surveys has led to the growth of transient and variable source
discoveries. The data deluge produced by these surveys has necessitated the use of …

We present the result of searching for clusters of galaxies based on weak gravitational
lensing analysis of the∼ 160 deg2 area surveyed by Hyper Suprime-Cam (HSC) as a …

We present a catalog of 316 trans-Neptunian bodies (TNOs) detected from the first four
seasons (" Y4" data) of the Dark Energy Survey (DES). The survey covers a contiguous 5000 …

Abstract Machine learning (ML) is the branch of computer science that studies computer
algorithms that can learn from data. It is mainly divided into supervised learning, where the …

This paper presents a combined method of Gaussian mixture model and random forest to
compute membership probabilities of stars by using large, high-dimensional data sets. A …

The work presented in this paper aims to develop new imputation methods to better handle
missing values encountered in astronomical data analysis, especially the classification of …

Identifying and characterizing young populations of star-forming regions are crucial to
unraveling their properties. In this regard, Gaia-DR3 data and machine-learning tools are …

In this work, we propose several enhancements to a geometric transformation based model
for anomaly detection in images (GeoTranform). The model assumes that the anomaly class …

Wide-field small aperture telescopes are the workhorses of fast sky surveying. Transient
discovery is one of their main tasks. Classification of candidate transient images between …