Galaxy blending effects in deep imaging cosmic shear probes of cosmology

Nourbakhsh, Erfan; Tyson, J. Anthony; Schmidt, Samuel J.; Collaboration, The LSST Dark Energy Science

doi:10.48550/arxiv.2112.07659

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…One obvious such area is deblending galaxies with small angular separations. Blending is a major issue in many ground-based surveys; it affects the number counts, photometries, measured shapes, and other parameters (see Dawson et al 2016;Mandelbaum 2018;Melchior et al 2018Melchior et al , 2021Samuroff et al 2018;Nourbakhsh et al 2021 for a review). The inaccuracies due to blending are alarming for many of the next generation of large cosmology surveys where precision measurements of the expansion history of the universe as well as the growth rate of large-scale structure rely heavily on photometric data sets.…”

Section: Resultsmentioning

confidence: 99%

Deblending Galaxies with Generative Adversarial Networks

Hemmati

Huff

Nayyeri

et al. 2022

ApJ

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Deep generative models including generative adversarial networks (GANs) are powerful unsupervised tools in learning the distributions of data sets. Building a simple GAN architecture in PyTorch and training on the CANDELS data set, we generate galaxy images with the Hubble Space Telescope (HST) resolution starting from a noise vector. We proceed by modifying the GAN architecture to improve Subaru Hyper Suprime-Cam (HSC) ground-based images by increasing their resolution to the HST resolution. We use the super-resolution GAN on a large sample of blended galaxies, which we create using CANDELS cutouts. In our simulated blend sample, ∼20% would unrecognizably be blended even in the HST-resolution cutouts. In the HSC-like cutouts this fraction rises to ∼90%. With our modified GAN we can lower this value to ∼50%. We quantify the blending fraction in the high, low, and GAN resolutions over the whole manifold of angular separation, flux ratios, sizes, and redshift difference between the two blended objects. The two peaks found by the GAN deblender result in improvement by a factor of 10 in the photometry measurement of the blended objects. Modifying the architecture of the GAN, we also train a multiwavelength GAN with HST cutouts in seven optical + near-infrared bands. This multiwavelength GAN improves the fraction of detected blends by another ∼10% compared to the single-band GAN. This is most beneficial to the current and future precision cosmology experiments (e.g., LSST, SPHEREx, Euclid, Roman), specifically those relying on weak gravitational lensing, where blending is a major source of systematic error.

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Section: Resultsmentioning

confidence: 99%

Deblending Galaxies with Generative Adversarial Networks

Hemmati

Huff

Nayyeri

et al. 2022

ApJ

View full text Add to dashboard Cite

show abstract

“…One obvious such area is deblending galaxies with small angular separations. Blending is a major issue in many ground-based surveys; it affects the number counts, photometries, measured shapes and other parameters (see Dawson et al 2016, Samuroff et al 2018, Mandelbaum 2018, Melchior et al 2018, Nourbakhsh et al 2021, and Melchior et al 2021 for a review). The inaccuracies due to blending are alarming for many of the next generation of large cosmology surveys where precision measurements of the expansion history of the Universe as well as the growth rate of large scale structure, heavily rely on photometric data sets.…”

Section: Network Architecturesmentioning

confidence: 99%

Deblending Galaxies with Generative Adversarial Networks

Hemmati,

Huff,

Nayyeri

et al. 2022

Preprint

View full text Add to dashboard Cite

Deep generative models including generative adversarial networks (GANs) are powerful unsupervised tools in learning the distributions of data sets. Building a simple GAN architecture in PyTorch and training on the CANDELS data set, we generate galaxy images with the Hubble Space Telescope resolution starting from a noise vector. We proceed by modifying the GAN architecture to improve the Subaru Hyper Suprime-Cam ground-based images by increasing their resolution to the HST resolution. We use the super resolution GAN on a large sample of blended galaxies which we create using CANDELS cutouts. In our simulated blend sample, ∼ 20% would unrecognizably be blended even in the HST resolution cutouts. In the HSC-like cutouts this fraction rises to ∼ 90%. With our modified GAN we can lower this value to ∼ 50%. We quantify the blending fraction in the high, low and GAN resolutions over the whole manifold of angular separation, flux ratios, sizes and redshift difference between the two blended objects. The two peaks found by the GAN deblender result in ten times improvement in the photometry measurement of the blended objects. Modifying the architecture of the GAN, we also train a Multi-wavelength GAN with seven band optical+NIR HST cutouts. This multi-wavelength GAN improves the fraction of detected blends by another ∼ 10% compared to the single-band GAN. This is most beneficial to the current and future precision cosmology experiments (e.g., LSST, SPHEREx, Euclid, Roman), specifically those relying on weak gravitational lensing, where blending is a major source of systematic error.

show abstract

Galaxy blending effects in deep imaging cosmic shear probes of cosmology

Cited by 2 publications

References 36 publications

Deblending Galaxies with Generative Adversarial Networks

Deblending Galaxies with Generative Adversarial Networks

Deblending Galaxies with Generative Adversarial Networks

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