66 citations · 348 across the 23 of their papers we have counts for
10 papers · 1 filter
Leveraging Transfer Learning for Astronomical Image Analysis
Stefano Cavuoti, Lars Doorenbos, Demetra De Cicco +7
The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibili…
Identification of problematic epochs in astronomical time series through transfer learning
Stefano Cavuoti, Demetra De Cicco, Lars Doorenbos +4
We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm with the aim of id…
Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era
M. Brescia, M. Salvato, S. Cavuoti +5
With the launch of eROSITA (extended Roentgen Survey with an Imaging Telescope Array), successfully occurred on 2019 July 13, we are facing the challenge of computing reliable phot…
A Comparison of Photometric Redshift Techniques for Large Radio Surveys
Ray P. Norris, M. Salvato, G. Longo +14
Future radio surveys will generate catalogues of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, sp…
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…
Data Driven Discovery in Astrophysics
G. Longo, M. Brescia, S. G. Djorgovski +2
We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" sci…