Neural networks and standard cosmography with newly calibrated high redshift GRB observations

Escamilla-Rivera, C., Carvajal, M., Zamora, C. and Hendry, M. (2022) Neural networks and standard cosmography with newly calibrated high redshift GRB observations. Journal of Cosmology and Astroparticle Physics, 2022(4), 016. (doi: 10.1088/1475-7516/2022/04/016)

Abstract

Gamma-ray bursts (GRBs) detected at high redshift can be used to trace the cosmic expansion history. However, the calibration of their luminosity distances is not an easy task in comparison to Type Ia Supernovae (SNeIa). To calibrate these data, correlations between their luminosity and other observed properties of GRBs need to be identified, and we must consider the validity of our assumptions about these correlations over their entire observed redshift range. In this work, we propose a new method to calibrate GRBs as cosmological distance indicators using SNeIa observations with a machine learning architecture. As well we include a new data GRB calibrated sample using extended cosmography in a redshift range above z > 3.6. An overview of this machine learning technique was developed in [1] to study the evolution of dark energy models at high redshift. The aim of the method developed in this work is to combine two networks: a Recurrent Neural Network (RNN) and a Bayesian Neural Network (BNN). Using this computational approach, denoted RNN+BNN, we extend the network's efficacy by adding the computation of covariance matrices to the Bayesian process. Once this is done, the SNeIa distance-redshift relation can be tested on the full GRB sample and therefore used to implement a cosmographic reconstruction of the distance-redshift relation in different regimes. Thus, our newly-trained neural network is used to constrain the parameters describing the kinematical state of the Universe via a cosmographic approach at high redshifts (up to z ≈ 10), wherein we require a very minimal set of assumptions on the deep learning arquitecture itself that do not rely on dynamical equations for any specific theory of gravity.

• Item Type: Articles
• Keywords: Neural networks, cosmography, gamma ray bursts.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Hendry, Professor Martin
• Authors: Escamilla-Rivera, C., Carvajal, M., Zamora, C., and Hendry, M.
• Subjects: Q Science > QB Astronomy; Q Science > QC Physics
• College/School: College of Science and Engineering > School of Physics and Astronomy
• Research Centre: College of Science and Engineering > School of Physics and Astronomy > Institute for Gravitational Research
• Journal Name: Journal of Cosmology and Astroparticle Physics
• Publisher: IOP Publishing
• ISSN: 1475-7516
• ISSN (Online): 1475-7516
• Published Online: 08 April 2022
• Copyright Holders: Copyright © 2022 IOP Publishing Ltd and Sissa Medialab
• First Published: First published in Journal of Cosmology and Astroparticle Physics 2022(4): 016
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
169451		Investigations in Gravitational Radiation	Sheila Rowan	Science and Technology Facilities Council (STFC)	ST/L000946/1	P&S - Physics & Astronomy