AstroAI Lunch Talk - October 7, 2024 - Bonny Wang

Speaker: Bonny Wang (Carnegie Mellon University)
Title: Machine-Learning Cosmology from Cosmic Voids
Abstract: Cosmic voids, the underdense regions in the galaxy distribution, are dominated by dark energy and account for most of the volume of the Universe. Thanks to their underdense feature, voids are particularly sensitive to cosmological information. In the past, the low number of voids and small survey volumes limited the research on voids, but recent large-scale surveys now enable big data approaches to fully explore voids' cosmological implications. Current methods of extracting cosmological information from voids are limited by the progress in modeling void statistics, typically focusing on void size functions and void-galaxy cross-correlation functions. The relationship between other void properties and cosmological parameters remains underexplored. Machine learning provides a well-established framework for performing this task. Furthermore, recently, relying on machine learning, extracting cosmological constraints from the properties of one galaxy proved to be possible. However, these results embed underlying questions that still need to be answered: How should we select the one galaxy or group of galaxies to optimize the constraints? Machine-learning approach enabled us to address this question by considering galaxies in cosmic voids. We show that void galaxies provide stronger constraints on the matter density parameter compared to randomly selected galaxies. This result suggests that the distinctive characteristics of void galaxies may provide a cleaner and more effective environment for extracting cosmological information.