Due to the necessity to evaluate photo-z for a variety of huge sky survey data sets, it seemed important to provide the astronomical community with an instrument able to fill this gap. Besides the problem of moving massive data sets over the network, another critical point is that a great part of astronomical data is stored in private archives that are not fully accessible on line. So, in order to evaluate photo-z it is needed a desktop application that can be downloaded and used by everyone locally, i.e. on his own personal computer or more in general within the local intranet hosted by a data center. The name chosen for the application is PhotoRApToR, i.e. Photometric Research Application To Redshift (Cavuoti et al. 2015, 2014; Brescia 2014b). It embeds a machine learning algorithm and special tools dedicated to preand post-processing data. The ML model is the MLPQNA (Multi Layer Perceptron trained by the Quasi Newton Algorithm), which has been revealed particularly powerful for the photo-z calculation on the base of a spectroscopic sample (Cavuoti et al. 2012; Brescia et al. 2013, 2014a; Biviano et al. 2013). The PhotoRApToR program package is available, for different platforms, at the official website (http://dame.dsf.unina.it/dame_photoz.html#photoraptor).

PhotoRaptor - Photometric Research Application To Redshifts

Brescia, Massimo
Conceptualization
;
Longo, Giuseppe
2016

Abstract

Due to the necessity to evaluate photo-z for a variety of huge sky survey data sets, it seemed important to provide the astronomical community with an instrument able to fill this gap. Besides the problem of moving massive data sets over the network, another critical point is that a great part of astronomical data is stored in private archives that are not fully accessible on line. So, in order to evaluate photo-z it is needed a desktop application that can be downloaded and used by everyone locally, i.e. on his own personal computer or more in general within the local intranet hosted by a data center. The name chosen for the application is PhotoRApToR, i.e. Photometric Research Application To Redshift (Cavuoti et al. 2015, 2014; Brescia 2014b). It embeds a machine learning algorithm and special tools dedicated to preand post-processing data. The ML model is the MLPQNA (Multi Layer Perceptron trained by the Quasi Newton Algorithm), which has been revealed particularly powerful for the photo-z calculation on the base of a spectroscopic sample (Cavuoti et al. 2012; Brescia et al. 2013, 2014a; Biviano et al. 2013). The PhotoRApToR program package is available, for different platforms, at the official website (http://dame.dsf.unina.it/dame_photoz.html#photoraptor).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/900837
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