66 citations · 348 across the 23 of their papers we have counts for
23 papers
Cosmology with galaxy clusters using machine learning. Application to eROSITA Data
Fucheng Zhong, Nicola R. Napolitano, Johan Comparat +8
Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Fo…
ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
Olena Torbaniuk, Lars Doorenbos, Maurizio Paolillo +3
Modern sky surveys produce vast amounts of observational data, making the application of classical methods for estimating galaxy properties challenging and time-consuming. This cha…
Selection of optically variable active galactic nuclei via a random forest algorithm
Demetra De Cicco, Gaetano Zazzaro, Stefano Cavuoti +5
Context. A defining characteristic of active galactic nuclei (AGN) that distinguishes them from other astronomical sources is their stochastic variability, which is observable acro…
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…
Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models
Lars Doorenbos, Eva Sextl, Kevin Heng +6
Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a ge…
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…