Bringing causality to astronomy

Abstract

Causal discovery aims at learning causal relations among variables from data. It is an emerging field at the interface of machine learning and statistics that found ample application in several disciplines. From a physicist's point of view it can be seen as an operational definition of the concept of causal relation between variables, especially in an observational context where experimental manipulation is precluded. Interestingly, causal discovery has not yet been applied to Astronomy, despite it being the observational science par excellence. Here I will present the first application of causal discovery to Astronomy with the goal of addressing a debated issue in galaxy formation: the origin of the observed scaling relations between supermassive black hole (SMBH) and host galaxy properties. I apply three causal discovery algorithms to a state-of-the-art dataset of SMBH host galaxies with dynamical mass measurements, with the goal of learning a causal structure in terms of a directed acyclic graph. The results are consistent between methods and are amenable to physical interpretation, showing that across the multiplicity of possible causal structures, in elliptical galaxies SMBH mass is predominantly an effect of galaxy properties while in spiral galaxies the reverse holds. I offer an explanation of this finding in terms of the physics of galaxy merging, and address the limitations and the theoretical implications of this new method for galaxy formation in some detail.

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