Related papers: Introducing SESHAT: A Tool for Object Classificati…
The James Webb Space Telescope (JWST) Optical Simulation Testbed (JOST) is a hardware simulator for wavefront sensing and control designed to produce JWST-like images. A model of the JWST three mirror anas- tigmat is realized with three…
The James Webb Space Telescope (JWST) will revolutionize our understanding of exoplanets with transit spectroscopy of a wide range of mature planets close to their host stars ($<$2 AU) and with coronagraphic imaging and spectroscopy of…
The James Webb Space Telescope (JWST) will have the sensitivity to detect early low-mass black holes as they transition from "seeds" to supermassive black holes (BHs). Based on the JAGUAR mock catalog of galaxies, we present a clean color…
The selection of high-redshift galaxies often involves spectral energy distribution (SED) fitting to photometric data, an expectation for contamination levels, and measurement of sample completeness -- all vetted through comparison to…
The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) aims at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. This is the second paper of this series where we present a…
The advent of JWST (the James Webb Space Telescope) now allows entire star cluster populations to be imaged in galaxies at cosmologically significant redshifts, bringing with it the need to apply K-corrections to their magnitudes and colour…
The James Webb Space Telescope (JWST) will devote significant observing time to the study of exoplanets. It will not be serviceable as was the Hubble Space Telescope, and therefore the spacecraft/instruments will have a relatively limited…
Variability Search Toolkit (VaST) is a software package designed to find variable objects in a series of sky images. It can be run from a script or interactively using its graphical interface. VaST relies on source list matching as opposed…
A major scientific goal of JWST is to probe the epoch of re-ionization of the Universe at z above 6, and up to 20 and beyond. At these redshifts, galaxies are just beginning to form and the observable objects are early black holes,…
Ultracool dwarfs exhibit a remarkably varied set of characteristics which hint at the complex physical processes acting in their atmospheres and interiors. Spectra of these objects not only depend upon their mass and effective temperature,…
In the technical report, we present a novel transformer-based framework for nuScenes lidar-based object detection task, termed Spatial Expansion Group Transformer (SEGT). To efficiently handle the irregular and sparse nature of point cloud,…
We report a potentially new population of objects revealed by the data from the James Webb Space Telescope, which are characterized by their point-like morphology and narrow permitted emission lines. Our sample includes eight objects found…
As the oldest building blocks of our Galaxy, globular clusters retain the archaeological footprint of the early stellar environments. Accurate absolute ages of globular clusters are required to interpret this ancient record. Existing dating…
Large vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they are only able to handle single image inputs, limiting their…
Conventional detection networks usually need abundant labeled training samples, while humans can learn new concepts incrementally with just a few examples. This paper focuses on a more challenging but realistic class-incremental few-shot…
White dwarfs are one of the few types of stellar objects for which we know almost nothing about the possible existence of companion planets. Recent evidence for metal contaminated atmospheres, circumstellar debris disks and transiting…
Recent studies on generalizable object detection have attracted increasing attention with additional weak supervision from large-scale datasets with image-level labels. However, weakly-supervised detection learning often suffers from…
We present the results of a machine learning study to measure the dust content of galaxies observed with JWST at z > 6 through the use of trained neural networks based on high-resolution IllustrisTNG simulations. Dust is an important…
We present DASH (Deep Automated Supernova and Host classifier), a novel software package that automates the classification of the type, age, redshift, and host galaxy of supernova spectra. DASH makes use of a new approach that does not rely…
With the launch and application of next-generation ground- and space-based telescopes, astronomy has entered the era of big data, necessitating more efficient and robust data analysis methods. Most traditional parameter estimation methods…