The DOI matches its Crossref record; 161 of the 165 references it cites were found in a registry or an index.
Deep learning predictions of galaxy merger stage and the importance of observational realism
Open accessAbstract
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 successful. By combining hydrodynamical simulations, synthetic observations, and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semirealistic images), and (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semireal images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1 per cent classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps ($87.1{{\ \rm per\ cent}}$ compared to $79.6{{\ \rm per\ cent}}$ accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data ($86.0{{\ \rm per\ cent}}$ with r-only compared to $87.1{{\ \rm per\ cent}}$ with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, RealSim, as a companion to this paper.
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“Their model reached high (87%) accuracy even when using simulated images inserted into real SDSS survey fields to train and test the model.”
From Detecting galaxy tidal features using self-supervised representation learning (2024) · cites this paper as Bottrell et al. (2019)
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“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 Bottrell et al. (2019)
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“The main conclusion is that it is more important that images used for training reproduce the observational properties of the sample to be analysed, that is, PSF, noise, background sources, than using full radiative transfer to improve the conversion from stellar particles to light.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Bottrell et al. (2019)
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Metadata
- Authors
- Bottrell, C., Hani, M. H., Teimoorinia, H., Ellison, S. L., Moreno, J., Torrey, P., Hayward, C. C., Thorp, M., Simard, L., & Hernquist, L.
- Affiliations
- University of Victoria (CA)
- California Institute of Technology (US)
- University of Florida (US)
- Flatiron Institute (US)
- National Research Council Canada (CA)
- Center for Astrophysics Harvard & Smithsonian (US)
- Corresponding author
- Bottrell, Connor
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-18
- Volume
- 490
- Issue
- 4
- Pages
- 5390-5413
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2980735226
- Cited by
- 113
- References
- 165
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 22 · 2023: 21 · 2022: 26 · 2021: 9 · 2020: 12 · 2019: 2
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.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) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- NPARC (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Sciences and Engineering Research Council of Canada
- NSF (1516374)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysical Phenomena and Observations
- Keywords
- galaxy mergers, synthetic galaxy images, radiative transfer, convolutional neural networks, point spread function, fluid simulation, galaxy morphology
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