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Fast access to large catalogs is required for some astronomical applications. Here we introduce the catsHTM tool, consisting of several large catalogs reformatted into HDF5-based file format, which can be downloaded and used locally. To…
A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic…
As astronomical data grows in volume and complexity, the scalability of analysis software becomes increasingly important. At the same time, astrophysics analysis software relies heavily on open-source contributions, so languages and tools…
This paper presents an efficient object detection method from satellite imagery. Among a number of machine learning algorithms, we proposed a combination of two convolutional neural networks (CNN) aimed at high precision and high recall,…
The James Webb Space Telescope will enable a wealth of new scientific investigations in the near- and mid-infrared, with sensitivity and spatial/spectral resolution greatly surpassing its predecessors. In this paper, we focus upon Solar…
We have investigated and applied machine-learning algorithms for Infrared Colour Selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the GLIMPSE catalogue of the infrared objects in the Galactic plane can be classified into…
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification…
In preparation for observations with the $\textit{James Webb Space Telescope}$ ($\textit{JWST}$), we have identified new members of the nearby, young M dwarf sample and compiled an up to date list of these stars. Here we summarize our…
Measuring properties of young stellar objects (YSOs) is necessary for probing the pre-main-sequence evolution of stars. As YSOs exhibit complex geometry, measurement generally entails comparing observed radiation to template populations of…
The generation and evolution of dust in galaxies are important tracers for star formation, and can characterize the rest-frame ultraviolet to infrared emission from the galaxies. In particular understanding dust in high-redshift galaxies…
We present a distant T$-$type brown dwarf candidate at $\approx2.55$ kpc discovered in the Cosmic Evolution Early Release Science (CEERS) fields by James Webb Space Telescope (JWST) NIRCam. In addition to the superb sensitivity, we utilised…
The direct characterization of exoplanetary systems with high contrast imaging is among the highest priorities for the broader exoplanet community. As large space missions will be necessary for detecting and characterizing exo-Earth twins,…
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 James Webb Space Telescope (JWST) is a large, infrared space telescope that has recently started its science program which will enable breakthroughs in astrophysics and planetary science. Notably, JWST will provide the very first…
We present a first-look analysis of the JWST ERO data in the SMACS J0723.3-7327 cluster field. We begin by reporting 10 new spectroscopic redshifts from $\lambda_\mathrm{obs}=1.8-5.2\mu$m NIRSpec medium-resolution ($R=\lambda/\Delta\lambda…
The NIRCam instrument on the upcoming James Webb Space Telescope (JWST) will offer an unprecedented view of the most distant galaxies. In preparation for future deep NIRCam extragalactic surveys, it is crucial to understand the color…
Machine-learning algorithms offer immense possibilities in the development of several cognitive applications. In fact, large scale machine-learning classifiers now represent the state-of-the-art in a wide range of object…
For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images. However, it is very expensive and time-consuming to pairwise label…
In order to analyse the large numbers of Seyfert galaxy spectra available at present, we are testing new techniques to derive their physical parameters fastly and accurately. We present an experiment on such a new technique to segregate old…
We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of >65000 color templates. The method aims for extracting the information content of object colors in a statistically…