Multi-tasking the growth of cosmological structures

Louis Perenon*, Matteo Martinelli, Stéphane Ilić, Roy Maartens, Michelle Lochner, Chris Clarkson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Next-generation large-scale structure surveys will deliver a significant increase in the precision of growth data, allowing us to use `agnostic' methods to study the evolution of perturbations without the assumption of a cosmological model. We focus on a particular machine learning tool, Gaussian processes, to reconstruct the growth rate ƒ, the root mean square of matter fluctuations σ8, and their product ƒσ8. We apply this method to simulated data, representing the precision of upcoming Stage IV galaxy surveys. We extend the standard single-task approach to a multi-task approach that reconstructs the three functions simultaneously, thereby taking into account their inter-dependence. We find that this multi-task approach outperforms the single-task approach for future surveys and will allow us to detect departures from the standard model with higher significance. By contrast, the limited sensitivity of current data severely hinders the use of agnostic methods, since the Gaussian processes parameters need to be fine tuned in order to obtain robust reconstructions.
Original languageEnglish
Article number100898
Pages (from-to)1-11
Number of pages11
JournalPhysics of the Dark Universe
Volume34
Early online date20 Oct 2021
DOIs
Publication statusPublished - 1 Dec 2021

Keywords

  • cosmology
  • growth of structures
  • Gaussian processes
  • UKRI
  • STFC
  • ST/S000550/1
  • ST/P000592/1

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