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Galaxy interactions in IllustrisTNG-100, I: The power and limitations of visual identification
Kelly Blumenthal, Jorge Moreno, Joshua E. Barnes +6
We present a sample of 446 galaxy pairs constructed using the cosmological simulation IllustrisTNG-100 at z = 0, with M = 10-10 M. We produce…
Simulating dust grain-radiation coupling on a moving mesh
Ryan McKinnon, Rahul Kannan, Mark Vogelsberger +3
We present a model for the interaction between dust and radiation fields in the radiation hydrodynamic code AREPO-RT, which solves the moment-based radiative transfer equations on…
Variations in the slope of the resolved star-forming main sequence: a tool for constraining the mass of star-forming regions
Maan H. Hani, Christopher C. Hayward, Matthew E. Orr +5
The correlation between galaxies' integrated stellar masses and star formation rates (the `star formation main sequence'; SFMS) is a well-established scaling relation. Recently, su…
Early-type galaxy density profiles from IllustrisTNG: II. Evolutionary trend of the total density profile
Yunchong Wang, Mark Vogelsberger, Dandan Xu +8
We study the evolutionary trend of the total density profile of early-type galaxies (ETGs) in IllustrisTNG. To this end, we trace ETGs from to and measure the power-law…
Simulating the interstellar medium of galaxies with radiative transfer, non-equilibrium thermochemistry, and dust
Rahul Kannan, Federico Marinacci, Mark Vogelsberger +4
We present a novel framework to self-consistently model the effects of radiation fields, dust physics and molecular chemistry (H) in the interstellar medium (ISM) of galaxies.…
Deep learning predictions of galaxy merger stage and the importance of observational realism
Connor Bottrell, Maan H. Hani, Hossen Teimoorinia +7
Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be…