Seminario Aula ore 15.00: Maria Giovanna Dainotti ( Stanford University, USA)

Titolo: Use of supervised machine learning for determination of redshifts of  Gamma-Ray Bursts

Abstract:

Gamma-ray bursts (GRBs) by virtue of their high luminosities are observed up to redshift z=9.4 (Cucchiara et al. 2011), far beyond the most distant quasars or galaxies. and thus have the potential to be vital cosmological probes of earlier processes in the universe, such as reionization, evolution of the star formation rate (SFR), in general, and formation of the first generation (Population III) stars. This requires a relatively large sample of GRBs with known redshifts and well defined observational selection effects. Most GRB instruments provide samples with a well defined prompt gamma-ray peak flux threshold. However, samples with redshift, requiring localization at X-rays and optical-UV follow up observations, suffer from more complex truncations,which hampers the progress to this end. The Swift satellite, the most successful instruments for measuring spectroscopic redshifts of GRBs, has provided redshifts only about one-third of GRBs itdetected. The situation is even less promising for other instruments. Thus, for more than 20 years there have been attempts to increase the number of GRBs with known z via a theoretical estimate of redshift (so called pseudo-redshifts) using GRB relations, but these approaches have led to inaccurate predictions. Thus, we adopt here supervised machine learning approaches to estimate redshifts for GRBs using existing data from the Swift-(satellite). These methods will also allow us to estimate possible non-linear relations between the redshift and other GRB characteristics. Our approach brings a novelty on this research area,because, for the first time, it adds the afterglow plateau emission characteristics. We obtained best results using the “superlearner” ensamble model with a correlation coefficient of 0.86 between the predicted and the observed redshift. We also show that using the predicted redshifts we obtain distributions and cosmological evolutions very similar to those obtained from actual measured redshifts.