The pLISA project in ASTERICS

Giulia De Bonis, Cristiano Bozza, A. Morselli, A. Capone, G. Rodriguez Fernandez
2017 EPJ Web of Conferences  
In the framework of Horizon 2020, the European Commission approved the ASTERICS initiative (ASTronomy ESFRI and Research Infrastructure CluSter) to collect knowledge and experiences from astronomy, astrophysics and particle physics and foster synergies among existing research infrastructures and scientific communities, hence paving the way for future ones. ASTERICS aims at producing a common set of tools and strategies to be applied in Astronomy ESFRI facilities. In particular, it will target
more » ... e so-called multi-messenger approach to combine information from optical and radio telescopes, photon counters and neutrino telescopes. pLISA is a software tool under development in ASTERICS to help and promote machine learning as a unified approach to multivariate analysis of astrophysical data and signals. The library will offer a collection of classification parameters, estimators, classes and methods to be linked and used in reconstruction programs (and possibly also extended), to characterize events in terms of particle identification and energy. The pLISA library aims at offering the software infrastructure for applications developed inside different experiments and has been designed with an effort to extrapolate general, physics-related estimators from the specific features of the data model related to each particular experiment. pLISA is oriented towards parallel computing architectures, with awareness of the opportunity of using GPUs as accelerators demanding specifically optimized algorithms and to reduce the costs of processing hardware requested for the reconstruction tasks. Indeed, a fast (ideally, real-time) reconstruction can open the way for the development or improvement of alert systems, typically required by multi-messenger search programmes among the different experimental facilities involved in ASTERICS.
doi:10.1051/epjconf/201713601006 fatcat:ofzrw43dynfiphhpwmb77ea32q