Automated distant galaxy merger classifications from Space Telescope images using the Illustris simulation
Open accessAbstract
We present image-based evolution of galaxy mergers from the Illustris cosmological simulation at 12 time-steps over 0.5 < z < 5. To do so, we created approximately one million synthetic deep Hubble Space Telescope and James Webb Space Telescope images and measured common morphological indicators. Using the merger tree, we assess methods to observationally select mergers with stellar mass ratios as low as 10:1 completing within ±250 Myr of the mock observation. We confirm that common one- or two-dimensional statistics select mergers so defined with low purity and completeness, leading to high statistical errors. As an alternative, we train redshift-dependent random forests (RFs) based on 5–10 inputs. Cross-validation shows the RFs yield superior, yet still imperfect, measurements of the late-stage merger fraction, and they select more mergers in bulge-dominated galaxies. When applied to CANDELS morphology catalogues, the RFs estimate a merger rate increasing to at least z = 3, albeit two times higher than expected by theory. This suggests possible mismatches in the feedback-determined morphologies, but affirms the basic understanding of galaxy merger evolution. The RFs achieve completeness of roughly $70{{\ \rm per\ cent}}$ at 0.5 < z < 3, and purity increasing from $10{{\ \rm per\ cent}}$ at z = 0.5–60 per cent at z = 3. At earlier times, the training sets are insufficient, motivating larger simulations and smaller time sampling. By blending large surveys and large simulations, such machine learning techniques offer a promising opportunity to teach us the strengths and weaknesses of inferences about galaxy evolution.
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Snyder et al. (2019) model's TPR is significantly lower than our model's.
“which is a significantly lower TPR than that reach by our model.”
From Detecting galaxy tidal features using self-supervised representation learning (2024) · cites this paper as Snyder et al. (2019)
Section 3.3 (p. 10) reports a TPR of approximately 0.7 for Snyder et al.’s random-forest classifier. The paper does not assess the citing authors’ model, so it cannot establish that its own TPR is significantly lower. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSnyder et al. (2019) model reached ~70% completeness for ~30% contamination.
“Their model reached ∼ 70% completeness (TPR) for ∼ 30% contamination (FPR) which is a significantly lower TPR than that reach by our model.”
From Detecting galaxy tidal features using self-supervised representation learning (2024) · cites this paper as Snyder et al. (2019)
Both Bottrell et al. (2019) and Snyder et al. (2019) do not focus explicitly on the detection of tidal features and therefore are not directly comparable to our analysis.
“However, both the works of Bottrell et al. (2019) and Snyder et al. (2019) do not focus explicitly on the detection of tidal features and therefore are not directly comparable to the results of our analysis.”
From Detecting galaxy tidal features using self-supervised representation learning (2024) · cites this paper as Snyder et al. (2019)
Snyder et al. (2019) improved on these early works by exploring Random Forests applied to moments of light on numerical simulations.
“Snyder et al. (2019) improved on these early works by exploring Random Forests applied to moments of light on numerical simulations.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Snyder et al. (2019)
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“Snyder et al. (2019) improved on these early works by exploring Random Forests applied to moments of light on numerical simulations.”
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Detecting galaxy tidal features using self-supervised representation learning
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- Authors
- Snyder, G. F., Rodriguez-Gomez, V., Lotz, J. M., Torrey, P., Quirk, A. C. N., Hernquist, L., Vogelsberger, M., & Freeman, P. E.
- Affiliations
- Space Telescope Science Institute (US)
- Department of Physics & Astronomy, Johns Hopkins University, 3400 N Charles Str, Baltimore, MD 21218, USA
- University of Florida (US)
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- Carnegie Mellon University (US)
- Corresponding author
- Snyder, Gregory F
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-04-13
- Volume
- 486
- Issue
- 3
- Pages
- 3702-3720
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2891965639
- Cited by
- 65
- References
- 59
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 14 · 2023: 14 · 2022: 4 · 2021: 7 · 2020: 10 · 2019: 6
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Space Telescope Science Institute
- National Aeronautics and Space Administration (NAS 5-26555)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysical Phenomena and Observations
- Keywords
- galaxy mergers, merger rate, merger fraction, random forest, galaxy morphology, Illustris simulation, CANDELS, merger trees, stellar mass ratio, high-redshift galaxies, galaxy evolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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