37 citations · 84 across the 9 of their papers we have counts for
9 papers
Repeating Outbursts from the Young Stellar Object Gaia23bab (= SPICY 97589)
Michael A. Kuhn, Robert A. Benjamin, Emille E. O. Ishida +3
The light curve of Gaia23bab (= SPICY 97589) shows two significant ( mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source's quiescent spectr…
SKA Science Data Challenge 2: analysis and results
P. Hartley, A. Bonaldi, R. Braun +105
The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomer…
3D Detection and Characterisation of ALMA Sources through Deep Learning
Michele Delli Veneri, Lukasz Tychoniec, Fabrizia Guglielmetti +2
We present a Deep-Learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALM…
How have astronomers cited other fields in the last decade?
Michele Delli Veneri, Rafael S. de Souza, Alberto Krone-Martins +3
We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade (). We analyze 12,600 ast…
A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on Solar System bodies
Carlo Donadio, Massimo Brescia, Alessia Riccardo +3
Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of…
Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning
Valeria Amaro, Stefano Cavuoti, Massimo Brescia +7
The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by…
Periodic Astrometric Signal Recovery through Convolutional Autoencoders
Michele Delli Veneri, Louis Desdoigts, Morgan A. Schmitz +8
Astrometric detection involves a precise measurement of stellar positions, and is widely regarded as the leading concept presently ready to find earth-mass planets in temperate orb…
Star Formation Rates for photometric samples of galaxies using machine learning methods
M. Delli Veneri, S. Cavuoti, M. Brescia +2
Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts…
Stellar formation rates in galaxies using Machine Learning models
Michele Delli Veneri, Stefano Cavuoti, Massimo Brescia +2
Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require…