Simulation-guided galaxy evolution inference: A case study with strong lensing galaxies
Andreas Filipp, Yiping Shu, Ruediger Pakmor, Sherry H. Suyu, Xiaosheng, Huang

TL;DR
This paper introduces a simulation-guided method to infer the evolution of individual galaxies by identifying their descendants through cosmological simulations, demonstrated on strong lensing galaxies, and highlights its potential with future data.
Contribution
It proposes a novel strategy for galaxy evolution analysis by combining observational data with cosmological simulations to track galaxy descendants.
Findings
Successfully identified descendant galaxies matching observed properties.
Confirmed feasibility of the strategy with current data and simulations.
Highlighted potential for future studies with larger datasets.
Abstract
Understanding the evolution of galaxies provides crucial insights into a broad range of aspects in astrophysics, including structure formation and growth, the nature of dark energy and dark matter, baryonic physics, and more. It is, however, infeasible to track the evolutionary processes of individual galaxies in real time given their long timescales. As a result, galaxy evolution analyses have been mostly based on ensembles of galaxies that are supposed to be from the same population according to usually basic and crude observational criteria. We propose a new strategy of evaluating the evolution of an individual galaxy by identifying its descendant galaxies as guided by cosmological simulations. As a proof of concept, we examined the evolution of the total mass distribution of a target strong lensing galaxy at using the proposed strategy. We selected 158 galaxies from the…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Metaheuristic Optimization Algorithms Research
