2021
DOI: 10.48550/arxiv.2111.01154
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Predicting resolved galaxy properties from photometric images using convolutional neural networks

Abstract: Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is notoriously difficult. Traditionally such information is best extracted from expensive spectroscopic observations. Here we present the Painting IntrinsiC Attributes onto SDSS Objects (PICASSSO) project and test the information content of photometric multi-band images of galaxies. We… Show more

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Cited by 7 publications
(4 citation statements)
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References 101 publications
(113 reference statements)
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“…The workhorse algorithm is the convolutional neural network (CNN; for an introduction, see O 'Shea & Nash 2015), most often used in image recognition and feature extraction. CNNs can be used for general classification (e.g., early-versus late-type galaxies) or to extract specific morphological features of galaxies, such as bars and spiral arms; many works have demonstrated their effectiveness at this (e.g., Ackermann et al 2018;Jacobs et al 2019;Bickley et al 2021;Buck & Wolf 2021;Walmsley et al 2022a). Pearson et al (2022) demonstrated the power of CNNs for finding interacting and merging galaxies specifically, finding 2109 in 5.4 deg 2 of Hyper Suprime-Cam imagery-a large sample for the small area covered.…”
Section: Introductionmentioning
confidence: 99%
“…The workhorse algorithm is the convolutional neural network (CNN; for an introduction, see O 'Shea & Nash 2015), most often used in image recognition and feature extraction. CNNs can be used for general classification (e.g., early-versus late-type galaxies) or to extract specific morphological features of galaxies, such as bars and spiral arms; many works have demonstrated their effectiveness at this (e.g., Ackermann et al 2018;Jacobs et al 2019;Bickley et al 2021;Buck & Wolf 2021;Walmsley et al 2022a). Pearson et al (2022) demonstrated the power of CNNs for finding interacting and merging galaxies specifically, finding 2109 in 5.4 deg 2 of Hyper Suprime-Cam imagery-a large sample for the small area covered.…”
Section: Introductionmentioning
confidence: 99%
“…CNNs have also been successfully applied to a variety of galaxy photometry-related studies, such as nonparametric light profile extraction (e.g., Smith et al 2021;Stone et al 2021), source deblending (e.g., Boucaud et al 2020), and galaxy stellar population analysis (e.g., Buck & Wolf 2021). Tuccillo et al (2018) for the first time applied CNNs to twodimensional light profile galaxy fitting on Hubble Space Telescope (HST)/CANDELS data.…”
Section: Introductionmentioning
confidence: 99%
“…Such ML techniques have been applied in a large number of astronomical use-cases related to galaxy properties (e.g. Dieleman et al 2015;Beck et al 2018;Hocking et al 2018;Dawson et al 2020;Buck & Wolf 2021).…”
Section: Introductionmentioning
confidence: 99%