The Machine Learning to reconstruct GRB lightcurves

The current knowledge in cosmology deals with open problems whose solutions are still under investigation. The main issue is the so-called Hubble constant ($H_0$) tension, namely, the $4-6 \sigma$ discrepancy between the local value of $H_0$ obtained with Cepheids+Supernovae Ia (SNe Ia) and the cosmological one estimated from the observations of the Cosmic Microwave Background (CMB). For the investigation of this problem, probes that span all over the redshift $z$ ranges are needed. Cepheids are local objects, SNe Ia reached up to $z=2.9$, and CMB is observed at $z=1100$. In this context, the use of probes at intermediate redshift $z>3$ is auspicious for casting more light on modern cosmology. The Gamma-ray Bursts (GRBs) are particularly suitable for this task, given their observability up to $z=9.4$. The use of GRBs as standardizable candles requires the use of tight and reliable astrophysical correlations and the presence of gaps in the GRB time-domain data represents an obstacle in this sense. In this work, we propose to improve the precision of the lightcurve (LC) parameters through a reconstruction process performed with the functional forms of GRB LCs and the Gaussian Processes (GP). The filling of gaps in the GRB LCs through these processes shows an improvement up to $41.5\%$ on the precision of the LC parameters fitting, which lead to a reduced scatter in the astrophysical correlations and, thus, in the estimation of cosmological parameters.