activity
20242026
most citedDART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

astro-ph.GA2026

Rubin J122659.4+090236: An Extremely Low Surface Brightness Galaxy Candidate Discovered in the Rubin LSST Early Data Preview 2

Dipanjan Mitra, Kanak Saha, Sugata Kaviraj +28

We report the serendipitous discovery of an exceptionally low surface brightness galaxy (LSBG) candidate, Rubin J122659.4+090236, in Rubin Observatory imaging of the interacting NG…

astro-ph.GA2026

Dissecting ultra-diffuse galaxies in the field

A. Vanzanella, K. Malek, Junais +16

Context. Ultra-diffuse galaxies (UDGs) in the field are faint, diffuse systems that remain poorly represented in the literature due to the need for spectroscopic confirmation and t…

astro-ph.GA2026

From DES to KiDS: Domain adaptation for cross-survey detection of low-surface-brightness galaxies

Hareesh Thuruthipilly, Krzysztof Lisiecki, Junais +19

Low-surface-brightness galaxies (LSBGs) are vital for understanding galaxy formation, but their diffuse nature makes them challenging to detect. Upcoming large-scale surveys are ex…

astro-ph.EP20252 cited

DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates

Stefano Fiscale, Laura Inno, Alessandra Rotundi +12

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 vo…

astro-ph.EP2024

How much earlier would LSST have discovered currently known long-period comets?

Laura Inno, Margherita Scuderi, Ivano Bertini +10

Among solar system objects, comets coming from the Oort Cloud are an elusive population, intrinsically rare and difficult to detect. Nonetheless, as the more pristine objects we ca…