186 citations · 3.1k across the 86 of their papers we have counts for
10 papers · 1 filter
Photometric classification of emission line galaxies with Machine Learning methods
Stefano Cavuoti, Massimo Brescia, Raffaele D'Abrusco +2
In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGN…
Identification of New Gamma-Ray Blazar Candidates With Multifrequency Archival Observations
Philip S. Cowperthwaite, F. Massaro, R. D'Abrusco +3
Blazars are a highly-variable, radio-loud subclass of active galactic nuclei (AGN). In order to better understand such objects we must be able to easily identify candidate blazars…
Blazars spectral properties at 74 MHz
F. Massaro, M. Giroletti, A. Paggi +3
Blazars are the most extreme class of active galactic nuclei (AGNs). Despite a previous investigation at 102 MHz for a small sample of BL Lacs and our recent analysis of blazars de…
BL Lac candidates for TeV observations
F. Massaro, A. Paggi, M. Errando +4
BL Lac objects are the most numerous class of extragalactic TeV-detected sources. One of the biggest difficulties in investigating their TeV emission resides in their limited numbe…
The Two Dimensional Projected Spatial Distribution of Globular Clusters: Method and Application to NGC4261
R. D'Abrusco, G. Fabbiano, J. Strader +7
We present a new method for the determination of the two-dimensional (2D) projected spatial distribution of globular clusters (GCs) in external galaxies. This method is based on th…
Photometric redshifts for Quasars in multi band Surveys
M. Brescia, S. Cavuoti, R. D'Abrusco +2
MLPQNA stands for Multi Layer Perceptron with Quasi Newton Algorithm and it is a machine learning method which can be used to cope with regression and classification problems on co…