Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning
Sunil Mucesh, William G. Hartley, Ciar\'an M. Gilligan-Lee, Ofer Lahav

TL;DR
This paper applies causal inference methods to galaxy formation data, revealing how environment influences star formation differently across cosmic time, and emphasizing the importance of considering both internal and external factors.
Contribution
It introduces a novel causal modeling approach to disentangle nature and nurture effects on galaxy evolution using simulations, highlighting the dynamic impact of environment.
Findings
Environment suppresses star formation by up to 100 times at present day.
Environment boosts star formation by up to 10 times at redshift ~1.
Controlling for stellar mass alone is insufficient to isolate causal effects.
Abstract
Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenomena, and particularly their importance, remain far from known, as correlations have primarily been established rather than the underlying causality. We address this challenge by applying the causal inference framework. Specifically, we tackle the fundamental open question of whether galaxy formation and evolution depends more on nature (i.e., internal processes) or nurture (i.e., external processes), by estimating the causal effect of environment on star-formation rate in the IllustrisTNG simulations. To do so, we develop a comprehensive causal model and employ cutting-edge techniques from epidemiology to overcome the long-standing problem of…
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Taxonomy
TopicsAstronomy and Astrophysical Research
MethodsCausal inference
