Approximations to galaxy star formation rate histories: properties and uses of two examples
J.D. Cohn

TL;DR
This paper explores simplified models, specifically lognormal and PCA approximations, to describe galaxy star formation histories, aiding comparison of galaxy formation models and understanding galaxy evolution.
Contribution
It introduces and evaluates two approximation methods for galaxy star formation histories and demonstrates their utility in classifying galaxy types and comparing models.
Findings
Lognormal and PCA approximations fit galaxy histories well.
Machine learning can recover these fits from halo histories.
Approximations outperform final stellar mass in galaxy classification.
Abstract
Galaxies evolve via a complex interaction of numerous different physical processes, scales and components. In spite of this, overall trends often appear. Simplified models for galaxy histories can be used to search for and capture such emergent trends, and thus to interpret and compare results of galaxy formation models to each other and to nature. Here, two approximations are applied to galaxy integrated star formation rate histories, drawn from a semi-analytic model grafted onto a dark matter simulation. Both a lognormal functional form and principal component analysis (PCA) approximate the integrated star formation rate histories fairly well. Machine learning, based upon simplified galaxy halo histories, is somewhat successful at recovering both fits. The fits to the histories give fixed time star formation rates which have notable scatter from their true fixed time rates at final…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
