Photometric redshifts from SDSS images using a convolutional neural network
Open accessAbstract
We developed a deep convolutional neural network (CNN), used as a classifier, to estimate photometric redshifts and associated probability distribution functions (PDF) for galaxies in the Main Galaxy Sample of the Sloan Digital Sky Survey at z < 0.4. Our method exploits all the information present in the images without any feature extraction. The input data consist of 64 × 64 pixel ugriz images centered on the spectroscopic targets, plus the galactic reddening value on the line-of-sight. For training sets of 100k objects or more (≥20% of the database), we reach a dispersion σ MAD < 0.01, significantly lower than the current best one obtained from another machine learning technique on the same sample. The bias is lower than 10 −4 , independent of photometric redshift. The PDFs are shown to have very good predictive power. We also find that the CNN redshifts are unbiased with respect to galaxy inclination, and that σ MAD decreases with the signal-to-noise ratio (S/N), achieving values below 0.007 for S / N > 100, as in the deep stacked region of Stripe 82. We argue that for most galaxies the precision is limited by the S/N of SDSS images rather than by the method. The success of this experiment at low redshift opens promising perspectives for upcoming surveys.
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The CNN of Pasquet et al. (2019) achieved significantly lower dispersion than previous image-based ML models.
“For easier comparison against established baselines on SDSS data, we closely follow the setup of Pasquet et al. (2019), whose CNN achieved significantly lower dispersion than previous image-based ML models.”
From Self-supervised Representation Learning for Astronomical Images (2021) · cites this paper as Pasquet et al. 2019
Section 4.2 reports that Pasquet et al.’s CNN had substantially lower dispersion than B16, the study it calls the only comparable one. But the Summary identifies B16’s method as KNN applied to photometric measurements, not an image-based ML model. The Introduction discusses earlier image-based CNNs without establishing that Pasquet et al.’s dispersion was lower than theirs. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPasquet et al. (2019) highlight that a CNN based only on raw postage stamps, without additional information, is blind to observing condition factors such as varying noise and PSF.
“Whereas flux measurements can be standardised to account for varying noise and PSF, a CNN based only on the raw postage stamps, without additional information, is blind to these observing condition factors.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Pasquet et al. (2019)
Section 6.6 (Influence of the PSF) says surveys with more variable observing conditions may need PSF information supplied explicitly alongside pixel data. But it finds the CNN’s redshift dispersion relatively immune to PSF variation in the SDSS data; Section 6.5 (Effect of noise) shows its performance changes with signal-to-noise ratio. These passages support a concern about missing explicit observing-condition information, not the categorical claim that a CNN using images is blind to varying noise and PSF. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe prediction bias 〈∆z〉 is negligibly small, and the supervised baseline model matches the accuracy of Pasquet et al. (2019) in σ_MAD and η.
“Similar to the results of Pasquet et al. (2019), the prediction bias 〈 ∆ z 〉 (a noisedominated metric) is negligibly small, and our supervised baseline model matches the accuracy of Pasquet et al. (2019) in σ MAD and η .”
From Self-supervised Representation Learning for Astronomical Images (2021) · cites this paper as Pasquet et al. 2019
Pasquet et al. (2019), based on SDSS Main Galaxy Sample ugriz images, illustrated how the CNN is able to automatically make use of pixel-level data to extract information beyond colours, improving redshift estimation.
“This study, based again on galaxies from the SDSS Main Galaxy Sample using ugriz images, illustrated in particular how the CNN is able to automatically make use of pixel-level data to extract information beyond colours, improving redshift estimation.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Pasquet et al. (2019)
Pasquet et al. (2019) report a noticeable bias as a function of seeing for the CNN.
“The authors report for instance a noticeable bias as a function of seeing for the CNN,”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Pasquet et al. (2019)
Pasquet et al. (2019) used a much deeper convolutional network comprised of one input convolution layer and 5 inception blocks, trained under a redshift bin classification loss.
“Pasquet et al. (2019), which used a much deeper convolutional network, comprised of one input convolution layer and 5 inception blocks, trained under a redshift bin classification loss.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Pasquet et al. (2019)
For star-forming galaxies, the colour-based k-NN photometric redshift estimate exhibits a strong inclination-dependent bias caused by various amounts of dust attenuation as a function of viewing angle, whereas the CNN shows comparatively very little bias.
“As can be seen, the colour-based approach exhibits a strong inclination-dependent bias caused by the various amounts of dust attenuation as a function of the viewing angle. The CNN shows however comparatively very little bias,”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Pasquet et al. (2019)
Campagne (2020) highlighted that inception models like the one proposed in Pasquet et al. (2019) can be highly sensitive to adversarial attacks.
“Campagne (2020) which highlighted that inception models like the one proposed in Pasquet et al. (2019) can be highly sensitive to adversarial attacks.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Pasquet et al. (2019)
Dey et al. (2021) find that this approach leads to a better scaling with size of the training set than Pasquet et al. (2019), especially at very small training set sizes, but the benefits are not as significant as those offered by the self-supervised approach of Hayat et al. (2021).
“The authors find that this approach leads to a better scaling with size of the training set than (Pasquet et al. 2019), especially at very small training set sizes, but the benefits are not as significant as the ones offered by the self-supervised approach of Hayat et al. (2021).”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Pasquet et al. (2019)
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Hayat, M. A., Stein, G., Harrington, P., Lukić, Z., & Mustafa, M. (2021) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/abf2c7 · Cited by 79 · see citing works
Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning
Tohill, C., Ferreira, L., Conselice, C. J., Bamford, S. P., & Ferrari, F. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac033c · Cited by 34 · see citing works
- Authors
- Pasquet, J., Bertin, E., Treyer, M., Arnouts, S., & Fouchez, D.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- Astronomy & Astrophysics
- Publisher
- EDP Sciences
- Published
- 2018-12-21
- Volume
- 621
- Pages
- A26
- ISSN
- 0004-6361 (ISSN-L)
- 1432-0746
- OpenAlex
- W2808496877
- Cited by
- 197
- References
- 55
- Citations by year
- 2026: 17 · 2025: 25 · 2024: 34 · 2023: 28 · 2022: 27 · 2021: 27 · 2020: 15 · 2019: 23 · 2018: 1
- FWCI
- 8.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Astronomy and Astrophysics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- HAL AMU (submittedVersion, cc-by)
- Springer Link (Chiba Institute of Technology) (submittedVersion)
- HAL (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- U.S. Department of Energy
- Alfred P. Sloan Foundation
- Yale University
- Vanderbilt University
- Princeton University
- Ohio State University
- Harvard University
- University of Washington
- Johns Hopkins University
- University of Arizona
- Carnegie Mellon University
- University of Portsmouth
- New Mexico State University
- York University
- Agence Nationale de la Recherche (ANR-11-IDEX-0001, ANR11IDEX000102, ANR-11-IDEX, ANR-13-BS05-0005, IDEX-0001-02, ANR-11-LABX-0060)
- Office of Science
- College of Engineering, Michigan State University
- Brookhaven National Laboratory
- Lawrence Berkeley National Laboratory
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Engineering › Computational Mechanics › Astronomical Observations and Instrumentation
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Keywords
- photometric redshifts, convolutional neural networks, galaxy classification, probability distribution functions, Sloan Digital Sky Survey, signal-to-noise ratio, low-redshift galaxies
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