Nonparametric galaxy morphology from UV to submm wavelengths

Baes, M.; Nersesian, Angelos; Casasola, V.; Bianchi, S.; Cassarà, L. P.; Clark, Christopher; Dobbels, Wouter; Fritz, J.; Galametz, M.; Galliano, F.; Madden, S.; Mosenkov, Aleksandr V.; Viaene, S.; Trčka, Ana; Xilouris, E. M.

doi:10.1051/0004-6361/202038470

Cited by 22 publications

(21 citation statements)

References 124 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a particular issue for those measuring asymmetries in HI gas (e.g Holwerda et al 2011;Giese et al 2016;Reynolds et al 2020). A recent work by Baes et al (2020) explored the change in asymmetry in a range of wavelengths from the UV to the submm, as well as subsequent derived data products such as stellar mass, dust mass, and starformation rate. Though they saw trends reflective of understood physical models, without assessing the varying signal-to-noise levels of those observations it is not clear how much of those trends are driven by noise effects.…”

Section: Discussionmentioning

confidence: 99%

Towards robust determination of non-parametric morphologies in marginal astronomical data: resolving uncertainties with cosmological hydrodynamical simulations

Thorp

Bluck

Ellison

et al. 2021

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

Quantitative morphologies, such as asymmetry and concentration, have long been used as an effective way to assess the distribution of galaxy starlight in large samples. Application of such quantitative indicators to other data products could provide a tool capable of capturing the 2-dimensional distribution of a range of galactic properties, such as stellar mass or star-formation rate maps. In this work, we utilize galaxies from the Illustris and IllustrisTNG simulations to assess the applicability of concentration and asymmetry indicators to the stellar mass distribution in galaxies. Specifically, we test whether the intrinsic values of concentration and asymmetry (measured directly from the simulation stellar mass particle maps) are recovered after the application of measurement uncertainty and a point spread function (PSF). We find that random noise has a non-negligible systematic effect on asymmetry that scales inversely with signal-to-noise, particularly at signal-to-noise less than 100. We evaluate different methods to correct for the noise contribution to asymmetry at very low signal-to-noise, where previous studies have been unable to explore due to systematics. We present algebraic corrections for noise and resolution to recover the intrinsic morphology parameters. Using Illustris as a comparison dataset, we evaluate the robustness of these fits in the presence of a different physics model, and confirm these correction methods can be applied to other datasets. Lastly, we provide estimations for the uncertainty on different correction methods at varying signal-to-noise and resolution regimes.

show abstract

Section: Discussionmentioning

confidence: 99%

Towards robust determination of non-parametric morphologies in marginal astronomical data: resolving uncertainties with cosmological hydrodynamical simulations

Thorp

Bluck

Ellison

et al. 2021

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

show abstract

“…Assessing the level of agreement between the morphologies of the full populations of observed and simulated galaxies is a hard task, due to the intrinsic complexity of galaxy shapes. The approach followed by some authors (Snyder et al 2015, Bottrell et al 2017b, Bottrell et al 2017a, Rodriguez-Gomez et al 2019, Bignone et al 2019, Baes et al 2020) consisted in making use of integrated, parametric and nonparametric quantities as diagnostics (such as the popular − − − − 20 statistics (e.g., Abraham et al 1994, Conselice 2003, Lotz et al 2004, with the aim of describing galaxy morphologies with only a few numbers. However, such an approach may still not grasp the full complexity of a galaxy image.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations

Zanisi

Huertas-Company

Lanusse

et al. 2020

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here, we propose an unsupervised deep learning approach to perform a stringent test of the fine morphological structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and TNG50) simulations against observations from a subsample of the Sloan Digital Sky Survey. Our framework is based on PixelCNN, an autoregressive model for image generation with an explicit likelihood. We adopt a strategy that combines the output of two PixelCNN networks in a metric that isolates the small-scale morphological details of galaxies from the sky background. We are able to quantitatively identify the improvements of IllustrisTNG, particularly in the high-resolution TNG50 run, over the original Illustris. However, we find that the fine details of galaxy structure are still different between observed and simulated galaxies. This difference is mostly driven by small, more spheroidal, and quenched galaxies that are globally less accurate regardless of resolution and which have experienced little improvement between the three simulations explored. We speculate that this disagreement, that is less severe for quenched discy galaxies, may stem from a still too coarse numerical resolution, which struggles to properly capture the inner, dense regions of quenched spheroidal galaxies.

show abstract

“…In this work we use the exact same procedure as Kapoor et al (2021) to obtain multi-wavelength sets of the elliptical half-light radii and of the CAS indices for a subset of ARTEMIS disc galaxies. This allows us to compare these morphological properties with those already calculated by Kapoor et al (2021) for similarly selected Auriga galaxies and by Baes et al (2020) for a set of well-resolved DustPedia spiral galaxies.…”

Section: Statmorphmentioning

confidence: 92%

High-resolution synthetic UV-submm images for Milky Way-mass simulated galaxies from the ARTEMIS project

Camps,

Kapoor,

Trčka

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

We present redshift-zero synthetic dust-aware observations for the 45 Milky Way-mass simulated galaxies of the ARTEMIS project, calculated with the SKIRT radiative transfer code. The post-processing procedure includes components for star-forming regions, stellar sources, and diffuse dust. We produce and publicly release realistic high-resolution images for 50 commonly-used broadband filters from ultraviolet to sub-millimetre wavelengths and for 18 different viewing angles. We compare the simulated ARTEMIS galaxies to observed galaxies in the DustPedia database with similar stellar mass and star formation rate, and to synthetic observations of the simulated galaxies of the Auriga project produced in previous work using a similar post-processing technique. In all cases, global galaxy properties are derived using SED fitting. We find that, similar to Auriga, the post-processed ARTEMIS galaxies generally reproduce the observed scaling relations for global fluxes and physical properties, although dust extinction at FUV/UV wavelengths is underestimated and representative dust temperatures are lower than observed. At a resolved scale, we compare multi-wavelength non-parametric morphological properties of selected disc galaxies across the data sets. We find that the ARTEMIS galaxies largely reproduce the observed morphological trends as a function of wavelength, although they appear to be more clumpy and less symmetrical than observed. We note that the ARTEMIS and Auriga galaxies occupy adjacent regions in the specific star formation versus stellar mass plane, so that the synthetic observation data sets supplement each other.

show abstract

Nonparametric galaxy morphology from UV to submm wavelengths

Cited by 22 publications

References 124 publications

Towards robust determination of non-parametric morphologies in marginal astronomical data: resolving uncertainties with cosmological hydrodynamical simulations

Towards robust determination of non-parametric morphologies in marginal astronomical data: resolving uncertainties with cosmological hydrodynamical simulations

A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations

High-resolution synthetic UV-submm images for Milky Way-mass simulated galaxies from the ARTEMIS project

Contact Info

Product

Resources

About