Modeling the Initial Conditions of Interacting Galaxy Pairs Using Identikit
S. Alireza Mortazavi, Jennifer M. Lotz, Joshua E. Barnes, Gregory F., Snyder

TL;DR
This paper presents an automated method using Identikit to model galaxy interactions, reducing subjective bias and testing its accuracy with simulations to recover key parameters within uncertainties.
Contribution
The authors develop an automated approach for modeling galaxy interactions with Identikit, systematically assessing uncertainties and biases using simulated data.
Findings
Successfully recover merger parameters within 3σ for good/fair convergence cases.
Edge-on systems yield less biased results than face-on systems.
Retrograde and polar systems require additional constraints for accurate modeling.
Abstract
We develop and test an automated technique to model the dynamics of interacting galaxy pairs. We use Identikit (Barnes & Hibbard 2009, Barnes 2011) as a tool for modeling and matching the morphology and kinematics of the interacting pairs of equal-mass galaxies. In order to reduce the effect of subjective human judgement, we automate the selection of phase-space regions used to match simulations to data, and we explore how selection of these regions affects the random uncertainties of parameters in the best-fit model. In this work, we use an independent set of GADGET SPH simulations as input data to determine the systematic bias in the measured encounter parameters based on the known initial conditions of these simulations. We test both cold gas and young stellar components in the GADGET simulations to explore the effect of choosing HI vs. H as the line of sight velocity tracer.…
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