IllustrisTNG in the HSC-SSP: image data release and the major role of mini mergers as drivers of asymmetry and star formation

Bottrell, Connor; Yesuf, Hassen M; Popping, Gergö; Omori, Kiyoaki Christopher; Tang, Shenli; Ding, Xuheng; Pillepich, Annalisa; Nelson, Dylan; Eisert, Lukas; Gao, Hua; Goulding, Andy D; Kalita, Boris S; Luo, Wentao; Greene, Jenny E; Shi, Jingjing; Silverman, John D

doi:10.1093/mnras/stad2971

Cited by 12 publications

(6 citation statements)

References 319 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second component of the mass-to-light conversion framework is the dust model, which handles the scattering and absorption of stellar light by dust. The synthetic images from Bottrell et al (2024) use a dust model calibrated to gas mass, gas metallicity, and dust mass estimates for galaxies in the local Universe (Rémy-Ruyer et al 2014;Popping et al 2022). While empirical, the model is physically intuitive-the gas-to-dust mass ratio should scale with the metal content of the gas at temperatures where dust grains can form.…”

Section: Discussionmentioning

confidence: 99%

“…Below, we briefly describe the forward model. We refer readers to Bottrell et al (2024) for full details.…”

Section: Datamentioning

confidence: 99%

“…TNG50 galaxies spanning 0. z 0.7 and M * 10 9 M e were forward modeled into synthetic images from the HSC-SSP by Bottrell et al 2024. The forward-modeling procedure first uses the SKIRT dust radiative transfer code 7 (Baes et al 2011;Camps & Baes 2015 to make noise/backgroundfree, high-resolution (idealized) images in the HSC grizy bands 8 (Kawanomoto et al 2018) and several supplementary filters spanning 0.3-5 μm.…”

Section: Synthetic Images and Measurementsmentioning

confidence: 99%

“…Second, HaloFlow is designed to be applied directly to observations. To do this, we use state-of-the-art synthetic galaxy images made with a dust radiative transfer forward model (Bottrell et al 2024). The images are constructed from the IllustrisTNG cosmological magnetohydrodynamical simulations (hereafter TNG; Nelson et al 2018;Pillepich et al 2018b;Weinberger et al 2018) and include the full observational realism of Subaru Hyper Suprime-Cam (HSC) imaging data obtained through the HSC Subaru Strategy Program (HSC-SSP; Aihara et al 2022).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

HaloFlow. I. Neural Inference of Halo Mass from Galaxy Photometry and Morphology

Hahn,

Bottrell,

Lee

2024

ApJ

Self Cite

View full text Add to dashboard Cite

We present HaloFlow, a new machine-learning approach for inferring the mass of host dark matter halos, M h , from the photometry and morphology of galaxies (https://github.com/changhoonhahn/haloflow/). HaloFlow uses simulation-based inference with normalizing flows to conduct rigorous Bayesian inference. It is trained on state-of-the-art synthetic galaxy images from Bottrell et al. that are constructed from the IllustrisTNG hydrodynamic simulation and include realistic effects of the Hyper Suprime-Cam Subaru Strategy Program observations. We design HaloFlow to infer M h and stellar mass, M *, using grizy band magnitudes, morphological properties quantifying characteristic size, concentration and asymmetry, total measured satellite luminosity, and number of satellites. We demonstrate that HaloFlow infers accurate and unbiased posteriors of M h . Furthermore, we quantify the full information content in the photometric observations of galaxies in constraining M h . With magnitudes alone, we infer M h with σ log M h ∼ 0.115 and 0.182 dex for field and group galaxies. Including morphological properties significantly improves the precision of M h constraints, as does total satellite luminosity: σ log M h ∼ 0.095 and 0.132 dex. Compared to the standard approach using the stellar-to-halo mass relation, we improve M h constraints by ∼40%. In subsequent papers, we will validate and calibrate HaloFlow with galaxy–galaxy lensing measurements on real observational data.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Below, we briefly describe the forward model. We refer readers to Bottrell et al (2024) for full details.…”

Section: Datamentioning

confidence: 99%

Section: Synthetic Images and Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

HaloFlow. I. Neural Inference of Halo Mass from Galaxy Photometry and Morphology

Hahn,

Bottrell,

Lee

2024

ApJ

Self Cite

View full text Add to dashboard Cite

show abstract

“…A181, page 11 of 13 Baes, M., et al: A&A, 683, A181 (2024) Note added in proof. Very recently, Bottrell et al (2024) published an atlas of synthetic images of simulated galaxies extracted from the TNG50 and TNG100 simulations. Similar to our work, the images are generated with SKIRT and take into account scattering and absorption by dust.…”

mentioning

confidence: 99%

The TNG50-SKIRT Atlas: Post-processing methodology and first data release

Baes,

Gebek,

Trčka

et al. 2024

A&A

View full text Add to dashboard Cite

Galaxy morphology is a powerful diagnostic to assess the realism of cosmological hydrodynamical simulations. Determining the morphology of simulated galaxies requires the generation of synthetic images through 3D radiative transfer post-processing that properly accounts for different stellar populations and interstellar dust attenuation. We use the SKIRT code to generate the TNG50-SKIRT Atlas, a synthetic UV to near-infrared broadband image atlas for a complete stellar-mass selected sample of 1154 galaxies extracted from the TNG50 cosmological simulation at $z=0$. The images have a high spatial resolution (100 pc) and a wide field of view (160 kpc). In addition to the dust-obscured images, we also release dust-free images and physical parameter property maps with matching characteristics. As a sanity check and preview application we discuss the UVJ diagram of the galaxy sample. We investigate the effect of dust attenuation on the UVJ diagram and find that it affects both the star-forming and the quiescent galaxy populations. The quiescent galaxy region is polluted by younger and star-forming highly inclined galaxies, while dust attenuation induces a separation in inclination of the star-forming galaxy population, with low-inclination galaxies remaining at the blue side of the diagram and high-inclination galaxies systematically moving towards the red side. This image atlas can be used for a variety of other applications, including galaxy morphology studies and the investigation of local scaling relations. We publicly release the images and parameter maps, and we invite the community to use them.

show abstract

Galaxy mergers in Subaru HSC-SSP: A deep representation learning approach for identification, and the role of environment on merger incidence

Omori,

Bottrell,

Walmsley

et al. 2023

A&A

View full text Add to dashboard Cite

Context.Galaxy mergers and interactions are an important process within the context of galaxy evolution, however, there is still no definitive method which identifies pure and complete merger samples is still not definitive. A method for creating such a merger sample is required so that studies can be conducted to deepen our understanding of the merger process and its impact on galaxy evolution.Aims.In this work, we take a deep-learning-based approach for galaxy merger identification in Subaru HSC-SSP, using deep representation learning and fine-tuning, with the aim of creating a pure and complete merger sample within the HSC-SSP survey. We can use this merger sample to conduct studies on how mergers affect galaxy evolution.Methods.We used Zoobot, a deep learning representation learning model pretrained on citizen science votes on Galaxy Zoo DeCALS images. We fine-tuned Zoobot for the purpose of merger classification of images of SDSS and GAMA galaxies in HSC-SSP public data release 3. Fine-tuning was done using ∼1200 synthetic HSC-SSP images of galaxies from the TNG simulation. We then found merger probabilities on observed HSC images using the fine-tuned model. Using our merger probabilities, we examined the relationship between merger activity and environment.Results.We find that our fine-tuned model returns an accuracy on the synthetic validation data of ∼76%. This number is comparable to those of previous studies in which convolutional neural networks were trained with simulation images, but with our work requiring a far smaller number of training samples. For our synthetic data, our model is able to achieve completeness and precision values of ∼80%. In addition, our model is able to correctly classify both mergers and non-mergers of diverse morphologies and structures, including those at various stages and mass ratios, while distinguishing between projections and merger pairs. For the relation between galaxy mergers and environment, we find two distinct trends. Using stellar mass overdensity estimates for TNG simulations and observations using SDSS and GAMA, we find that galaxies with higher merger scores favor lower density environments on scales of 0.5 to 8h−1Mpc. However, below these scales in the simulations, we find that galaxies with higher merger scores favor higher density environments.Conclusions.We fine-tuned a citizen-science trained deep representation learning model for purpose of merger galaxy classification in HSC-SSP, and make our merger probability catalog available to the public. Using our morphology-based catalog, we find that mergers are more prevalent in lower density environments on scales of 0.5–8h−1Mpc.

show abstract

IllustrisTNG in the HSC-SSP: image data release and the major role of mini mergers as drivers of asymmetry and star formation

Cited by 12 publications

References 319 publications

HaloFlow. I. Neural Inference of Halo Mass from Galaxy Photometry and Morphology

HaloFlow. I. Neural Inference of Halo Mass from Galaxy Photometry and Morphology

The TNG50-SKIRT Atlas: Post-processing methodology and first data release

Galaxy mergers in Subaru HSC-SSP: A deep representation learning approach for identification, and the role of environment on merger incidence

Contact Info

Product

Resources

About