%0 Generic %1 lim2022galaxyflow %A Lim, Sung Hak %A Raman, Kailash A. %A Buckley, Matthew R. %A Shih, David %D 2022 %K library %T GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Gaia Mock Catalogs %U http://arxiv.org/abs/2211.11765 %X Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires üpsampling" the star particles into individual stars following the same phase-space density. In this paper, we demonstrate that normalizing flows provide a viable upsampling method that greatly improves on conventionally-used kernel smoothing algorithms such as EnBiD. We demonstrate our flow-based upsampling technique, dubbed GalaxyFlow, on a neighborhood of the Solar location in two simulated galaxies: Auriga 6 and h277. By eye, GalaxyFlow produces stellar distributions that are smoother than EnBiD-based methods and more closely match the Gaia DR3 catalog. For a quantitative comparison of generative model performance, we introduce a novel multi-model classifier test. Using this classifier test, we show that GalaxyFlow more accurately estimates the density of the underlying star particles than previous methods.

@misc{lim2022galaxyflow, abstract = {Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires "upsampling" the star particles into individual stars following the same phase-space density. In this paper, we demonstrate that normalizing flows provide a viable upsampling method that greatly improves on conventionally-used kernel smoothing algorithms such as EnBiD. We demonstrate our flow-based upsampling technique, dubbed GalaxyFlow, on a neighborhood of the Solar location in two simulated galaxies: Auriga 6 and h277. By eye, GalaxyFlow produces stellar distributions that are smoother than EnBiD-based methods and more closely match the Gaia DR3 catalog. For a quantitative comparison of generative model performance, we introduce a novel multi-model classifier test. Using this classifier test, we show that GalaxyFlow more accurately estimates the density of the underlying star particles than previous methods.}, added-at = {2022-11-23T06:58:42.000+0100}, author = {Lim, Sung Hak and Raman, Kailash A. and Buckley, Matthew R. and Shih, David}, biburl = {https://www.bibsonomy.org/bibtex/2c4ceb44ce6470b2c98aae7ec37e25399/gpkulkarni}, description = {GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Gaia Mock Catalogs}, interhash = {f3df4707e4c56ce8822b8f87be7ba6bd}, intrahash = {c4ceb44ce6470b2c98aae7ec37e25399}, keywords = {library}, note = {cite arxiv:2211.11765Comment: 17 pages, 11 figures}, timestamp = {2022-11-23T06:58:42.000+0100}, title = {GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Gaia Mock Catalogs}, url = {http://arxiv.org/abs/2211.11765}, year = 2022 }