Morpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data

Hausen, Ryan; Robertson, Brant E.

doi:10.3847/1538-4365/ab8868

Cited by 110Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network ApplicationsAstronomy and Astrophysical Research

Morpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data

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Hausen, Ryan, Robertson, Brant E.·2020·The Astrophysical Journal Supplement Series

DOI 10.3847/1538-4365/ab8868

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Abstract

We present Morpheus, a new model for generating pixel-level morphological classifications of astronomical sources. Morpheus leverages advances in deep learning to perform source detection, source segmentation, and morphological classification pixel-by-pixel via a semantic segmentation algorithm adopted from the field of computer vision. By utilizing morphological information about the flux of real astronomical sources during object detection, Morpheus shows resiliency to false-positive identifications of sources. We evaluate Morpheus by performing source detection, source segmentation, morphological classification on the Hubble Space Telescope data in the five CANDELS fields with a focus on the GOODS South field, and demonstrate a high completeness in recovering known GOODS South 3D-HST sources with H < 26 AB. We release the code publicly, provide online demonstrations, and present an interactive visualization of the Morpheus results in GOODS South.

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Hausen & Robertson (2020) successfully classify every pixel of the CANDELS survey in five morphological classes using a Unet.
“Using a sliding window, the Unet successfully classifies every pixel of the CANDELS survey in five morphological classes”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Hausen & Robertson (2020)

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The Spitzer Survey of Stellar Structure in Galaxies ( S 4 G )
Sheth, K., Regan, M., Hinz, J., de Paz, A. D., Men'endez-Delmestre, K., Muńoz-Mateos, J., Seibert, M., Kim, T., Laurikainen, E., Salo, H., et al. (2010) · Publications of the Astronomical Society of the Pacific · doi:10.1086/657638 · Cited by 238 · see citing works
WHAT TURNS GALAXIES OFF? THE DIFFERENT MORPHOLOGIES OF STAR-FORMING AND QUIESCENT GALAXIES SINCE z ∼ 2 FROM CANDELS
Bell, E., van der Wel, A., Papovich, C., Kocevski, D., Lotz, J., Mcintosh, D. H., Kartaltepe, J., Faber, S., Ferguson, H., Koekemoer, A., et al. (2011) · The Astrophysical Journal · doi:10.1088/0004-637x/753/2/167 · Cited by 227 · see citing works
Classification and Morphology of External Galaxies
de Vaucouleurs, G. D. (1959) · Handbuch der Physik · doi:10.1007/978-3-642-45932-0_7 · Cited by 221 · see citing works
Hubble Space Telescope and Spitzer Imaging of Red and Blue Galaxies at z ~ 2.5: A Correlation between Size and Star Formation Activity from Compact Quiescent Galaxies to Extended Star-forming Galaxies
Toft, S., van Dokkum, P. V., Franx, M., Labb'e, I., Schreiber, N. F., Wuyts, S., Webb, T., Rudnick, G., Zirm, A., Kriek, M., et al. (2007) · doi:10.1086/521810 · Cited by 215
The Luminosity, Stellar Mass, and Number Density Evolution of Field Galaxies of Known Morphology from z = 0.5 to 3
Conselice, C., Blackburne, J. A., & Papovich, C. (2004) · The Astrophysical Journal · doi:10.1086/426102 · Cited by 199 · see citing works
Galaxy Morphologies in the Hubble Ultra Deep Field: Dominance of Linear Structures at the Detection Limit
Elmegreen, D., Elmegreen, B., Rubin, D., & Schaffer, M. (2005) · The Astrophysical Journal · doi:10.1086/432502 · Cited by 166 · see citing works
NICMOS Imaging of DRGs in the HDF-S: A Relation between Star Formation and Size at z ~ 2.5
Zirm, A., van der Wel, A., Franx, M., Labb'e, I., Trujillo, I., van Dokkum, P. V., Toft, S., Daddi, E., Rudnick, G., Rix, H., et al. (2006) · doi:10.1086/510713 · Cited by 164
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2816 · Cited by 160 · cited 1× · see citing works
“The efficiency of users varies, but by leveraging the power of the public interest and enthusiasm, and now machine learning (Beck et al. 2018; Walmsley et al. 2019), the project can use scalability to offset variability in the performance of individual users.”
Background
The DEEP3 Galaxy Redshift Survey: the impact of environment on the size evolution of massive early-type galaxies at intermediate redshift
Cooper, M., Griffith, R., Newman, J., Coil, A., Davis, M., Dutton, A., Faber, S., Guhathakurta, P., Koo, D., Lotz, J., et al. (2011) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2011.19938.x · Cited by 158 · see citing works
The Wide Field Infrared Survey Telescope: 100 Hubbles for the 2020sPreprint
Akeson, R., Armus, L., Bachelet, É., Bailey, V., Bartusek, L., Bellini, A., Benford, D., Bennett, D., Bhattacharya, A., Bohlin, R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.05569 · Cited by 157 · cited 2× · see citing works
“With the advent of large imaging data sets such those provided by Dark Energy Survey (Dark Energy Survey Collaboration et al. 2016) and Hyper Suprime-Cam (Aihara et al. 2018a,b), and next-generation surveys to be conducted by Large Synoptic Survey Telescope (Ivezić et al. 2019; Robertson et al. 2019), Euclid (Laureij…”
BackgroundSelf-citation
The Morphologies of Massive Galaxies at 1<z<3 in the CANDELS-UDS Field: Compact Bulges, and the Rise and Fall of Massive Disks
Bruce, V., Dunlop, J., Cirasuolo, M., McLure, R., Targett, T., Bell, E., Croton, D., Dekel, A., Faber, S., Ferguson, H. C., et al. (2012) · Proceedings of the International Astronomical Union · doi:10.1111/j.1365-2966.2012.22087.x · Cited by 143 · cited 1× · see citing works
“…shape or ellipticity vary with stellar mass and luminosity (e.g., Kormendy 1977; Roberts & Haynes 1994; Shen et al. 2003; Sheth et al. 2010; Bruce et al. 2012; van der Wel et al. 2012, 2014; Morishita et al. 2014; Huertas-Company et al. 2015; Allen et al. 2017; Jiang et al. 2018; Miller et…”
Background
Galaxy morphology classification with deep convolutional neural networks
Zhu, X., Dai, J.-M., Bian, C., Chen, Y., Chen, S., & Hu, C. (2019) · Astrophysics and Space Science · doi:10.1007/s10509-019-3540-1 · Cited by 132 · cited 1× · see citing works
“Dieleman et al. (2015) (D15) and Dai & Tong (2018) use deep learning classify the Galaxy Zoo Survey.”
Background
Topological gray-scale watershed transformation
Couprie, M., & Bertrand, G. (1997) · Optics & Photonics · doi:10.1117/12.292778 · Cited by 131 · cited 4×
“The Sobel & Feldman (1968) algorithm is applied to the background image b to produce a Sobel edge image s. Morpheus then applies the watershed algorithm of Couprie & Bertrand (1997), using the Sobel image s as the “input image” and the background mask m as the “marker set”.”
MethodInfluential
The JWST Extragalactic Mock Catalog: Modeling Galaxy Populations from the UV through the Near-IR over 13 Billion Years of Cosmic History
Williams, C., Curtis-Lake, E., Hainline, K., Chevallard, J., Robertson, B., Charlot, S., Endsley, R., Stark, D., Willmer, C., Alberts, S., et al. (2018) · doi:10.3847/1538-4365/aabcbb · Cited by 129 · cited 1×
“The galaxy population discovered by JWST will show a rich range of morphologies, star formation histories, stellar masses, and angular sizes (Williams et al. 2018), which makes identifying NIRCam-selected samples for spectroscopic follow-up with NIRSpec challenging.”
Self-citation
A Catalog of Morphological Types in 10 Distant Rich Clusters of Galaxies
Smail, I., Dressler, A., Couch, W., Ellis, R., Oemler, A., Butcher, H., & Sharples, R. (1996) · doi:10.1086/312997 · Cited by 128
The Luminosity-Size and Mass-Size Relations of Galaxies out to z ~ 3
Trujillo, I., Rudnick, G., Rix, H., Labb'e, I., Franx, M., Daddi, E., van Dokkum, P. V., Schreiber, N. F. F., Kuijken, K., Moorwood, A., et al. (2003) · doi:10.1086/382060 · Cited by 123
CANDELS VISUAL CLASSIFICATIONS: SCHEME, DATA RELEASE, AND FIRST RESULTS
Kartaltepe, J., Mozena, M., Kocevski, D., Mcintosh, D. H., Lotz, J., Bell, E., Faber, S., Ferguson, H., Koo, D., Bassett, R., et al. (2014) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/221/1/11 · Cited by 118 · see citing works
Effect of Local Environment and Stellar Mass on Galaxy Quenching and Morphology at 0.5 < z < 2.0
Kawinwanichakij, L., Papovich, C., Quadri, R., Glazebrook, K., Kacprzak, G., Allen, R., Bell, E., Croton, D., Dekel, A., Ferguson, H., et al. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa8b75 · Cited by 115 · see citing works
MAPPING THE GALAXY COLOR–REDSHIFT RELATION: OPTIMAL PHOTOMETRIC REDSHIFT CALIBRATION STRATEGIES FOR COSMOLOGY SURVEYS
Masters, D., Capak, P., Stern, D., Ilbert, O., Salvato, M., Schmidt, S., Longo, G., Rhodes, J., Paltani, S., Mobasher, B., et al. (2015) · The Astrophysical Journal · doi:10.1088/0004-637x/813/1/53 · Cited by 113 · see citing works
Deep learning for galaxy surface brightness profile fitting
Tuccillo, D., Huertas-Company, M., Decencière, E., Velasco-Forero, S., Domínguez Sánchez, H., & Dimauro, P. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx3186 · Cited by 93 · see citing works
Transfer learning for galaxy morphology from one survey to another
Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J. L., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Brooks, D., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3497 · Cited by 91 · see citing works
GALAXY STRUCTURE AS A DRIVER OF THE STAR FORMATION SEQUENCE SLOPE AND SCATTER
Whitaker, K., Franx, M., Bezanson, R., Brammer, G., van Dokkum, P. V., Kriek, M., Labb'e, I., Leja, J., Momcheva, I., Nelson, E., et al. (2015) · The Astrophysical Journal Letters · doi:10.1088/2041-8205/811/1/l12 · Cited by 84 · see citing works
Galaxy Zoo: Quantitative visual morphological classifications for 48 000 galaxies from CANDELS
Simmons, B., Lintott, C., Willett, K., Masters, K., Kartaltepe, J., Häußler, B., Kaviraj, S., Krawczyk, C., Kruk, S., Mcintosh, D. H., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2587 · Cited by 83 · see citing works
Galaxy detection and identification using deep learning and data augmentation
Gonz'alez, R., Muñoz, R., & Hern'andez, C. A. (2018) · Astronomy and Computing · doi:10.1016/j.ascom.2018.09.004 · Cited by 76 · cited 1× · see citing works
“González et al. (2018) used deep learning to perform galaxy detection and morphological classification, an approach that has also been used to characterize Dark Energy Survey galaxy morphologies (Tarsitano et al. 2018).”
Method
Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging
Willett, K., Galloway, M. A., Bamford, S., Lintott, C., Masters, K., Scarlata, C., Simmons, B., Beck, M., Cardamone, C., Cheung, E., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2568 · Cited by 73 · see citing works
Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
M. Huertas-Company, Primack, J., Dekel, A., Koo, D., Lapiner, S., Ceverino, D., Simons, R., Snyder, G., Bernardi, M., Chen, Z., et al. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aabfed · Cited by 72 · see citing works
Galaxy Zoo:CANDELS barred discs and bar fractions
Simmons, B., Melvin, T., Lintott, C., Masters, K., Willett, K., Keel, W., Smethurst, R., Cheung, E., Nichol, R., Schawinski, K., et al. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu1817 · Cited by 70 · cited 1× · see citing works
“The presence and size of bulge, disk, and bar components also vary with mass and redshift (Sheth et al. 2008; Simmons et al. 2014; Margalef-Bentabol et al. 2016; Dimauro et al. 2018), and provide information about the merger rate (e.g., Lofthouse et al. 2017; Weigel et al. 2017).”
Background
Mass assembly and morphological transformations since z ∼ 3 from CANDELS
M. Huertas-Company, Bernardi, M., P'erez-Gonz'alez, P., Ashby, M., Barro, G., Conselice, C., Daddi, E., Dekel, A., Dimauro, P., Faber, S., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw1866 · Cited by 69 · cited 1× · see citing works
“…these measures of galaxy morphology may show variations with cosmic environment (Dressler et al. 1997; Smail et al. 1997; Cooper et al. 2012; Huertas-Company et al. 2016; Kawinwanichakij et al. 2017), redshift (Abraham & van den Bergh 2001; Trujillo et al. 2004; Conselice et al. 2005;…”
Background
An automatic taxonomy of galaxy morphology using unsupervised machine learning
Hocking, A., Geach, J., Sun, Y., & Davey, N. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx2351 · Cited by 67 · see citing works
Galaxy Zoo: the dependence of the star formation–stellar mass relation on spiral disc morphology
Willett, K., Schawinski, K., Simmons, B., Masters, K., Skibba, R., Kaviraj, S., Melvin, T., Wong, O. I., Nichol, R., Cheung, E., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv307 · Cited by 64 · cited 1× · see citing works
“…descriptions of∼50,000 galaxies (Simmons et al. 2017) in the CANDELS survey (Grogin et al. 2011; Koekemoer et al. 2011), probes of the connection between star formation rate and morphology in spiral galaxies (Willett et al. 2015), and measuring galaxy merger rates (Weigel et al. 2017).”
Background
Data-driven Reconstruction of Gravitationally Lensed Galaxies Using Recurrent Inference Machines
Morningstar, W., Levasseur, L. P., Hezaveh, Y., Blandford, R., Marshall, P., Putzky, P., Rueter, T. D., Wechsler, R., & Welling, M. (2019) · Astrophysical Journal · doi:10.3847/1538-4357/ab35d7 · Cited by 62 · see citing works
Scientific Synergy between LSST and Euclid
Rhodes, J., Nichol, R., Aubourg, '., Bean, R., Boutigny, D., Bremer, M., Capak, P., Cardone, V., Carry, B., Conselice, C., et al. (2017) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aa96b0 · Cited by 61 · see citing works
Self-citation
Photometry of high-redshift blended galaxies using deep learning
Boucaud, A., Huertas-Company, M., Heneka, C., Ishida, E. E. O., Sedaghat, N., de Souza, R. S., Moews, B., Dole, H., Castellano, M., Merlin, E., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3056 · Cited by 60 · cited 1× · see citing works
“Deep learning models have been further applied to infer the surface brightness profiles of galaxies (Tuccillo et al. 2018) and measure their fluxes (Boucaud et al. 2019), and now to simulate entire surveys (Smith & Geach 2019).”
Method
The Morphological Evolution of Galaxies
Abraham, R., & van den Bergh, S. (2001) · Science · doi:10.1126/science.1060855 · Cited by 59 · see citing works
The evolution of galaxy shapes in CANDELS: from prolate to discy
Zhang, H.-W., Primack, J., Faber, S., Koo, D., Dekel, A., Chen, Z., Ceverino, D., Chang, Y.-Y., Fang, J. J., Guo, Y.-C., et al. (2018) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stz339 · Cited by 53 · cited 1× · see citing works
“…vary with stellar mass and luminosity (e.g., Kormendy 1977; Roberts & Haynes 1994; Shen et al. 2003; Sheth et al. 2010; Bruce et al. 2012; van der Wel et al. 2012, 2014; Morishita et al. 2014; Huertas-Company et al. 2015; Allen et al. 2017; Jiang et al. 2018; Miller et al. 2019; Zhang et al. 2019).”
Background
Integrating human and machine intelligence in galaxy morphology classification tasks
Beck, M., Scarlata, C., Fortson, L., Lintott, C., Simmons, B., Galloway, M. A., Willett, K., Dickinson, H., Masters, K., Marshall, P., et al. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty503 · Cited by 53 · cited 1× · see citing works
“The efficiency of users varies, but by leveraging the power of the public interest and enthusiasm, and now machine learning (Beck et al. 2018; Walmsley et al. 2019), the project can use scalability to offset variability in the performance of individual users.”
Background
Data mining for gravitationally lensed quasars
Agnello, A., Kelly, B., Treu, T., & Marshall, P. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv037 · Cited by 51 · cited 1× · see citing works
“29 30 deployed, such as those that discover and model gravitational lenses (Agnello et al. 2015; Hezaveh et al. 2017; Morningstar et al. 2018, 2019).”
Background
Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey
Kelly, B., & Mckay, T. (2003) · The Astronomical Journal · doi:10.1086/380934 · Cited by 51 · see citing works
CANDELS: THE CORRELATION BETWEEN GALAXY MORPHOLOGY AND STAR FORMATION ACTIVITY AT z ∼ 2
Lee, B., Giavalisco, M., Williams, C., Guo, Y.-C., Lotz, J., van der Wel, A., Ferguson, H., Faber, S., Koekemoer, A., Grogin, N., et al. (2013) · doi:10.1088/0004-637x/774/1/47 · Cited by 50
TIGHT CORRELATIONS BETWEEN MASSIVE GALAXY STRUCTURAL PROPERTIES AND DYNAMICS: THE MASS FUNDAMENTAL PLANE WAS IN PLACE BY z ∼ 2
Bezanson, R., van Dokkum, P. V., van de Sande, J., Franx, M., Leja, J., & Kriek, M. (2013) · doi:10.1088/2041-8205/779/2/l21 · Cited by 49
The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning
M. Huertas-Company, Rodriguez-Gomez, V., Nelson, D., Pillepich, A., Bottrell, C., Bernardi, M., Domínguez-Sánchez, H., Genel, S., Pakmor, R., Snyder, G., et al. (2019) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2191 · Cited by 46 · see citing works
Major mergers are not significant drivers of star formation or morphological transformation around the epoch of peak cosmic star formation
Lofthouse, E., Kaviraj, S., Conselice, C., Mortlock, A., & Hartley, W. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw2895 · Cited by 46 · see citing works
THE EVOLUTION OF GALAXY SIZE AND MORPHOLOGY AT z ∼ 0.5–3.0 IN THE GOODS-N REGION WITH HUBBLE SPACE TELESCOPE/WFC3 DATA
Morishita, T., Ichikawa, T., & Kajisawa, M. (2014) · The Astrophysical Journal · doi:10.1088/0004-637x/785/1/18 · Cited by 45 · cited 1× · see citing works
“…vary with stellar mass and luminosity (e.g., Kormendy 1977; Roberts & Haynes 1994; Shen et al. 2003; Sheth et al. 2010; Bruce et al. 2012; van der Wel et al. 2012, 2014; Morishita et al. 2014; Huertas-Company et al. 2015; Allen et al. 2017; Jiang et al. 2018; Miller et al. 2019; Zhang et al. 2019).”
Background
Morphology Dependence of Stellar Age in Quenched Galaxies at Redshift ∼1.2:Massive Compact Galaxies Are Older than More Extended Ones
Williams, C., Giavalisco, M., Bezanson, R., Cappelluti, N., Cassata, P., Teng, L., Lee, B., Tundo, E., & Vanzella, E. (2016) · doi:10.3847/1538-4357/aa662f · Cited by 43
A catalog of polychromatic bulge-disc decompositions of ∼17.600 galaxies in CANDELS
Dimauro, P., M. Huertas-Company, Daddi, E., P'erez-Gonz'alez, P., Bernardi, M., Barro, G., Buitrago, F., Caro, F., Cattaneo, A., Domínguez-Sánchez, H., et al. (2018) · doi:10.1093/mnras/sty1379 · Cited by 41 · cited 1×
“The presence and size of bulge, disk, and bar components also vary with mass and redshift (Sheth et al. 2008; Simmons et al. 2014; Margalef-Bentabol et al. 2016; Dimauro et al. 2018), and provide information about the merger rate (e.g., Lofthouse et al. 2017; Weigel et al. 2017).”
Background
THE STRUCTURAL EVOLUTION OF MILKY-WAY-LIKE STAR-FORMING GALAXIES SINCE z ∼ 1.3
Patel, S. G., Fumagalli, M., Franx, M., van Dokkum, P. V., van der Wel, A., Leja, J., Labb'e, I., Brammer, G., Skelton, R., Momcheva, I., et al. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/778/2/115 · Cited by 41 · see citing works
The Size Evolution of Star-forming Galaxies since z ∼ 7 Using ZFOURGE
Allen, R., Kacprzak, G., Glazebrook, K., Labb'e, I., Tran, K., Spitler, L., Cowley, M., Nanayakkara, T., Papovich, C., Quadri, R., et al. (2016) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/834/2/l11 · Cited by 39 · see citing works
Beyond spheroids and discs: classifications of CANDELS galaxy structure at 1.4 < z < 2 via principal component analysis
Peth, M., Lotz, J., Freeman, P., McPartland, C., Mortazavi, S., Snyder, G., Barro, G., Grogin, N., Guo, Y.-C., Hemmati, S., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw252 · Cited by 39 · cited 1× · see citing works
“…including measures of galaxy asymmetry, concentration, flux distribution (e.g., Abraham et al. 1994, 1996; Conselice et al. 2000; Conselice 2003; Lotz et al. 2004), shapelet decompositions (Kelly & McKay 2004; Kelly & McKay 2005), and morphological principal component analyses (Peth et al. 2016).”
Background
THE RELATION BETWEEN GALAXY STRUCTURE AND SPECTRAL TYPE: IMPLICATIONS FOR THE BUILDUP OF THE QUIESCENT GALAXY POPULATION AT 0.5 < z < 2.0
Yano, M., Kriek, M., van der Wel, A., & Whitaker, K. (2016) · The Astrophysical Journal Letters · doi:10.3847/2041-8205/817/2/l21 · Cited by 34 · see citing works
Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networksPreprint
Morningstar, W., Hezaveh, Y., Levasseur, L. P., Blandford, R., Marshall, P., Putzky, P., & Wechsler, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.00011 · Cited by 33 · cited 1× · see citing works
“29 30 deployed, such as those that discover and model gravitational lenses (Agnello et al. 2015; Hezaveh et al. 2017; Morningstar et al. 2018, 2019).”
Background
The formation of bulges, discs and two-component galaxies in the CANDELS Survey at z < 3
Margalef-Bentabol, B., Conselice, C., Mortlock, A., Hartley, W., Duncan, K., Ferguson, H., Koekemoer, A., Dekel, A., & Primack, J. (2016) · doi:10.1093/mnras/stw1451 · Cited by 32 · cited 1×
“The presence and size of bulge, disk, and bar components also vary with mass and redshift (Sheth et al. 2008; Simmons et al. 2014; Margalef-Bentabol et al. 2016; Dimauro et al. 2018), and provide information about the merger rate (e.g., Lofthouse et al. 2017; Weigel et al. 2017).”
Background
Photometric Redshift Calibration Requirements for WFIRST Weak-lensing Cosmology: Predictions from CANDELS
Hemmati, S., Capak, P., Masters, D., Davidzon, I., Dor'e, O., Kruk, J., Mobasher, B., Rhodes, J., Scolnic, D., & Stern, D. (2018) · Astrophysical Journal · doi:10.3847/1538-4357/ab1be5 · Cited by 30 · see citing works
Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning
Smith, M. J., & Geach, J. E. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2886 · Cited by 27 · cited 1× · see citing works
“Deep learning models have been further applied to infer the surface brightness profiles of galaxies (Tuccillo et al. 2018) and measure their fluxes (Boucaud et al. 2019), and now to simulate entire surveys (Smith & Geach 2019).”
Method
A New View of the Size–Mass Distribution of Galaxies: Using r20 and r80 Instead of r50
Miller, T., van Dokkum, P. V., Mowla, L., & van der Wel, A. (2019) · Astrophysical Journal · doi:10.3847/2041-8213/ab0380 · Cited by 26 · cited 1×
“…vary with stellar mass and luminosity (e.g., Kormendy 1977; Roberts & Haynes 1994; Shen et al. 2003; Sheth et al. 2010; Bruce et al. 2012; van der Wel et al. 2012, 2014; Morishita et al. 2014; Huertas-Company et al. 2015; Allen et al. 2017; Jiang et al. 2018; Miller et al. 2019; Zhang et al. 2019).”
Background
Galaxy Zoo: Major Galaxy Mergers Are Not a Significant Quenching Pathway
Weigel, A. K., Schawinski, K., Caplar, N., Carpineti, A., Hart, R., Kaviraj, S., Keel, W., Kruk, S., Lintott, C., Nichol, R., et al. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa8097 · Cited by 25 · see citing works
A catalogue of structural and morphological measurements for DES Y1
Tarsitano, F., Hartley, W., Amara, A., Bluck, A., Bruderer, C., Carollo, M., Conselice, C., Melchior, P., Moraes, B., Réfrégier, A., et al. (2018) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1970 · Cited by 21 · cited 1× · see citing works
“González et al. (2018) used deep learning to perform galaxy detection and morphological classification, an approach that has also been used to characterize Dark Energy Survey galaxy morphologies (Tarsitano et al. 2018).”
Method
MORPHOLOGY AND THE COLOR–MASS DIAGRAM AS CLUES TO GALAXY EVOLUTION AT z ∼ 1
Powell, M., Urry, C., Cardamone, C., Simmons, B., Schawinski, K., Young, S., & Kawakatsu, M. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/835/1/22 · Cited by 21 · see citing works
Galaxy formation and evolution science in the era of the Large Synoptic Survey Telescope
Robertson, B., Banerji, M., Brough, S., Davies, R., Ferguson, H., Hausen, R., Kaviraj, S., Newman, J., Schmidt, S., Tyson, J., et al. (2019) · Nature Reviews Physics · doi:10.1038/s42254-019-0067-x · Cited by 19 · cited 2× · see citing works
“Indeed, morphological classification methods have been identified as keystone preparatory science tasks by in the LSST Galaxies Science Roadmap (Robertson et al. 2017, see also Robertson et al. 2019.).”
BackgroundMethodSelf-citation
The Hubble Legacy Fields (HLF-GOODS-S) v1.5 Data Products: Combining 2442 Orbits of GOODS-S/CDF-S Region ACS and WFC3/IR Images
Illingworth, G., Magee, D., Bouwens, R., Oesch, P., Labb'e, I., van Dokkum, P. V., Whitaker, K., Holden, B., Franx, M., & González, V. (2016) · doi:10.48550/arxiv.1606.00841 · Cited by 19 · cited 11×
“• Applying Morpheus to the Hubble Legacy Fields (Illingworth et al. 2016) v2.”
BackgroundMethodInfluential
The bulge-disc decomposition of AGN host galaxies
Bruce, V., Dunlop, J., Mortlock, A., Kocevski, D., McGrath, E., & Rosario, D. (2015) · doi:10.1093/mnras/stw467 · Cited by 17 · cited 1×
“…(Toft et al. 2007; Zirm et al. 2007; Wuyts et al. 2011; Bell et al. 2012; Lee et al. 2013; Whitaker et al. 2015), internal dynamics (Bezanson et al. 2013), the presence of active galactic nuclei (Kocevski et al. 2012; Bruce et al. 2016; Powell et al. 2017), and stellar age (Williams et al. 2017).”
Background
Large Synoptic Survey Telescope Galaxies Science RoadmapPreprint
Robertson, B., Banerji, M., Cooper, M., Davies, R., Driver, S., Ferguson, A., Ferguson, H., Gawiser, E., Kaviraj, S., Knapen, J., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.01617 · Cited by 15 · cited 1× · see citing works
“Indeed, morphological classification methods have been identified as keystone preparatory science tasks by in the LSST Galaxies Science Roadmap (Robertson et al. 2017, see also Robertson et al. 2019.).”
MethodSelf-citation
Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey. II. Multiwavelength Classification
Kelly, B., & Mckay, T. (2004) · The Astronomical Journal · doi:10.1086/427999 · Cited by 5 · see citing works
The Isophotal Structure of Star-forming Galaxies at 0.5 < z < 1.8 in CANDELS: Implications for the Evolution of Galaxy Structure
Jiang, D.-F., Liu, F.-S., Zheng, X.-Z., Yesuf, H. M., Koo, D., Faber, S., Guo, Y.-C., Koekemoer, A., Wang, W., Fang, J. J., et al. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aaa5ad · Cited by 3 · cited 1× · see citing works
“…vary with stellar mass and luminosity (e.g., Kormendy 1977; Roberts & Haynes 1994; Shen et al. 2003; Sheth et al. 2010; Bruce et al. 2012; van der Wel et al. 2012, 2014; Morishita et al. 2014; Huertas-Company et al. 2015; Allen et al. 2017; Jiang et al. 2018; Miller et al. 2019; Zhang et al. 2019).”
Background

Flagged first, then by citations

10 of the 130 references couldn’t be matched to a known paper.

Sorted by citations

Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023
Ansari, Y., Mourad, O., Qaraqe, K. A., & Serpedin, E. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1246746 · Cited by 323 · see citing works
“, 2021), classification, image, and pixel analysis (Hausen and Robertson, 2020; Ansari et al., 2022c; Ansari and Qaraqe, 2023), risk analysis (Akhtar et al.”
COSMOS-Web: An Overview of the JWST Cosmic Origins Survey
Casey, C., Kartaltepe, J., Drakos, N., Franco, M., Harish, S., Paquereau, L., Ilbert, O., Rose, C., Cox, I., Nightingale, J., et al. (2022) · Astrophysical Journal · doi:10.3847/1538-4357/acc2bc · Cited by 212 · see citing works
Self-citation
Deep learning predictions of galaxy merger stage and the importance of observational realism
Bottrell, C., Hani, M. H., Teimoorinia, H., Ellison, S. L., Moreno, J., Torrey, P., Hayward, C. C., Thorp, M., Simard, L., & Hernquist, L. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2934 · Cited by 113 · see citing works
“…significant traction in general astronomy and, in particular, galaxy astronomy are machine learning and deep learning (e.g. Teimoorinia & Ellison 2014; Hezaveh et al. 2017; Bluck et al. 2019; Ribli et al. 2019; Jacobs et al. 2019; Snyder et al. 2019; Ntampaka et al. 2019; Hausen & Robertson 2019).”
Background
The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., & Lanusse, F. (2023) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2022.55 · Cited by 102 · see citing works
“However, until now, and with the exception of the work by Hausen & Robertson (2020), the majority of the works have focused more on testing and demonstrating the performance of these deep learning based approaches.”
BackgroundMethod
Deblending and classifying astronomical sources with Mask R-CNN deep learning
Burke, C., Aleo, P., Chen, Y.-C., Liu, X., Peterson, J., Sembroski, G., & Lin, J. (2019) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2845 · Cited by 78 · see citing works
“The recent work of Hausen & Robertson (2019) combined deep learning semantic segmentation with mask separation algorithms to classify and deblend galaxies in the Hubble Space Telescope images.”
Background
A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges
Luengo, J., Moreno, R., Sevillano-García, I., Charte, D., Peláez-Vegas, A., Fernández-Moreno, M., Mesejo, P., & Herrera, F. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.09.018 · Cited by 77 · see citing works
Astronomia ex machina: a history, primer and outlook on neural networks in astronomy
Smith, M. J., & Geach, J. E. (2023) · Royal Society Open Science · doi:10.1098/rsos.221454 · Cited by 74 · see citing works
Morpheus Reveals Distant Disk Galaxy Morphologies with JWST: The First AI/ML Analysis of JWST Images
Robertson, B., Tacchella, S., Johnson, B., Hausen, R., Alabi, A., Boyett, K., Bunker, A., Carniani, S., Egami, E., Eisenstein, D., et al. (2022) · Astrophysical Journal Letters · doi:10.3847/2041-8213/aca086 · Cited by 50
“This Letter presents first results of applying artificial intelligence/machine-learning (AI/ML) techniques to JWST images, searching for distant disk galaxies by analyzing JWST data with the Morpheus deep-learning framework (Hausen & Robertson 2020, hereafter H20).”
BackgroundMethodSelf-citation
Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks
Cheng, T.-Y., Conselice, C., Aragón-Salamanca, A., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bluck, A., Brooks, D., Burke, D., et al. (2021) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2142 · Cited by 35 · see citing works
Generative Adversarial Neural Networks and Deep Learning: Successful Cases and Advanced Approaches
Striuk, O., & Kondratenko, Y. (2021) · Int. J. Comput. · doi:10.47839/ijc.20.3.2278 · Cited by 33 · see citing works
“Ryan Hausen and Brant Robertson designed and described a deep learning framework for pixel-level analysis of astronomical image data – Morpheus [15].”
BackgroundInfluential
Deep learning in astronomy: a tutorial perspective
Meher, S., & Panda, G. (2021) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-021-00207-9 · Cited by 32 · see citing works
Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations
Wilde, J., Serjeant, S., Bromley, J. M., Dickinson, H., Koopmans, L. V. E., & Metcalf, R. B. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac562 · Cited by 31 · see citing works
The challenge of blending in large sky surveys
Melchior, P., Joseph, R., Sanchez, J., MacCrann, N., & Gruen, D. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00353-y · Cited by 30 · see citing works
Identification of tidal features in deep optical galaxy images with convolutional neural networks
Sánchez, H. D., Martin, G., Damjanov, I., Buitrago, F., Huertas-Company, M., Bottrell, C., Bernardi, M., Knapen, J. H., Vega-Ferrero, J., Hausen, R., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad750 · Cited by 28 · see citing works
Self-citation
Mask galaxy: Morphological segmentation of galaxies
Farias, H., Ortiz, D., Damke, G., Arancibia, M. J., & Solar, M. (2020) · Astronomy and Computing · doi:10.1016/j.ascom.2020.100420 · Cited by 25 · see citing works
PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models
Mishra-Sharma, S., Song, Y., & Thaler, J. (2024) · arXiv.org · doi:10.48550/arxiv.2403.08851 · Cited by 22
“…representations, having shown to correlate with physical characteristics of imaged objects, can also be fine-tuned via transfer learning to adapt to either specific tasks e.g., classification (Wei et al., 2020) or segmentation (Hausen & Robertson, 2020), or observations imaged by other telescopes.”
Background
Pushing automated morphological classifications to their limits with the Dark Energy Survey
Vega-Ferrero, J., Sánchez, H. D., Bernardi, M., M. Huertas-Company, Morgan, R., Margalef, B., Aguena, M., Allam, S., Annis, J., Ávila, S., et al. (2020) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stab594 · Cited by 22 · see citing works
Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4
Nakoneczny, S., Bilicki, M., Pollo, A., Asgari, M., Dvornik, A., Erben, T., Giblin, B., Heymans, C., Hildebrandt, H., Kannawadi, A., et al. (2020) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202039684 · Cited by 22 · see citing works
“This problem is usually not addressed in the ML analyses, and the results on faint end data, which are most affected by overfitting, are rarely reported (e.g., Hausen & Robertson 2020).”
Background
mirkwood: Fast and Accurate SED Modeling Using Machine Learning
Gilda, S., Lower, S., & Narayanan, D. (2021) · Astrophysical Journal · doi:10.3847/1538-4357/ac0058 · Cited by 20 · see citing works
A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning
Steinhardt, C., Weaver, J., Maxfield, J., Davidzon, I., Faisst, A., Masters, D., Schemel, M., & Toft, S. (2020) · Astrophysical Journal · doi:10.3847/1538-4357/ab76be · Cited by 20 · see citing works
“…as the use of Convolutional Neural Networks to measure galaxy morphology (Dieleman et al. 2015; Domı́nguez Sánchez et al. 2018; Cheng et al. 2019; Hausen & Robertson 2019) and shape (Ribli et al. 2019), perform light profile fitting (Tuccillo et al. 2018), identify mergers (Bottrell et al.…”
Background
Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles
Vizcarra, J. C., Pearce, T. M., Dugger, B. N., Keiser, M. J., Gearing, M., Crary, J. F., Kiely, E. J., Morris, M., White, B., Glass, J. D., et al. (2023) · Acta Neuropathologica Communications · doi:10.1186/s40478-023-01691-x · Cited by 19 · see citing works
“Indeed we believe it can provide utility in the various pathology subfields, such as cancer and nephrology, and even fields as far flung as Astronomy, which similarly processes exceptionally large and complex imaging data [67, 68].”
Background
SCSS-Net: solar corona structures segmentation by deep learning
Mackovjak, Š., Harman, M., Krešňáková, V., & Butka, P. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2536 · Cited by 18 · see citing works
Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
Ostdiek, B., Rivero, A. D., & Dvorkin, C. (2020) · Astrophysical Journal · doi:10.3847/1538-4357/ac2d8d · Cited by 19 · see citing works
“2 Architectures similar to the U-Net have been used in astrophysics/ cosmology, e.g., Hausen & Robertson (2020) used it to predict galaxy morphologies.”
Method
The PAU survey: estimating galaxy photometry with deep learning
Cabayol, L., Eriksen, M., Amara, A., Carretero, J., Casas, R., Castander, F. J., De Vicente, J., Fernández, E., García-Bellido, J., Gaztanaga, E., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1909 · Cited by 18 · see citing works
A Novel Cs 2g-Starlet Denoising Method for High Noise Astronomical Imagei
Zhang, J., Zhang, H.-L., Wang, F., & Shi, X. (2022) · Social Science Research Network · doi:10.2139/ssrn.4161550 · Cited by 16
Advancing antibiotic discovery with bacterial cytological profiling: a high-throughput solution to antimicrobial resistance
Salgado, J., Rayner, J., & Ojkic, N. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1536131 · Cited by 13 · see citing works
SILVERRUSH X: Machine Learning-aided Selection of 9318 LAEs at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and CHORUS Survey Data
Ono, Y., Itoh, R., Shibuya, T., Ouchi, M., Harikane, Y., Yamanaka, S., Inoue, A., Amagasa, T., Miura, D., Okura, M., et al. (2021) · Astrophysical Journal · doi:10.3847/1538-4357/abea15 · Cited by 15 · see citing works
“…classifications and measurements (Dieleman et al. 2015; Huertas-Company et al. 2015; Domínguez Sánchez et al. 2018; Martin et al. 2020; Hausen & Robertson 2020; Cheng et al. 2020; Ghosh et al. 2020; Tohill et al. 2020; see also, Tadaki et al. 2020), photometric redshift estimates…”
Background
Point spread function estimation for wide field small aperture telescopes with deep neural networks and calibration data
Jia, P., Wu, X., Li, Z., Li, B., Wang, W.-H., Liu, Q., Popowicz, A., & Cai, D.-M. (2020) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1461 · Cited by 15 · see citing works
Deep Learning and Chaos: A combined Approach To Image Encryption and Decryption
Nair, B. V., VismayaV, S., Muni, S. S., & Durdu, A. (2024) · arXiv.org · doi:10.48550/arxiv.2406.16792 · Cited by 14
“The image is flattened into a one-dimensional array for facilitating pixel-level analysis[29].”
Method
GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Ghosh, A., Urry, C., Rau, A., Perreault-Levasseur, L., Cranmer, M., Schawinski, K., Stark, D., Tian, C., Ofman, R., Ananna, T., et al. (2022) · Astrophysical Journal · doi:10.3847/1538-4357/ac7f9e · Cited by 13 · see citing works
“…the field of image processing and have become increasingly popular for determining galaxy morphology (e.g., Dieleman et al. 2015; Huertas-Company et al. 2015; Tuccillo et al. 2018; Hausen & Robertson 2020; Walmsley et al. 2020; Cheng et al. 2021; Vega-Ferrero et al. 2021; Tarsitano et al. 2022).”
Background
Multimodal explainable artificial intelligence-driven analysis of quantum size effects in copper nanoclusters for hydrogen storage
Kurban, M., Polat, C., Serpedin, E., & Kurban, H. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.152950 · Cited by 11 · see citing works
FitsMap: A simple, lightweight tool for displaying interactive astronomical image and catalog data
Hausen, R., & Robertson, B. (2022) · Astronomy and Computing · doi:10.1016/j.ascom.2022.100586 · Cited by 10
“FitsMap has already been deployed to visualize large simulated JWST [39] and Roman [15] datasets and machine-learning galaxy morphological analyses of large Hubble Space Telescope surveys [18].”
BackgroundMethodSelf-citation
Photometric classification of Hyper Suprime-Cam transients using machine learning
Takahashi, I., Suzuki, N., Yasuda, N., Kimura, A., Ueda, N., Tanaka, M., Tominaga, N., & Yoshida, N. (2020) · Nippon Tenmon Gakkai obun kenkyu hokoku · doi:10.1093/pasj/psaa082 · Cited by 10 · see citing works
Surveying image segmentation approaches in astronomy
Xu, D., & Zhu, Y. (2024) · Astronomy and Computing · doi:10.1016/j.ascom.2024.100838 · Cited by 9 · see citing works
QuantumShellNet: Ground-state eigenvalue prediction of materials using electronic shell structures and fermionic properties via convolutions
Polat, C., Kurban, H., & Kurban, M. (2024) · Computational materials science · doi:10.1016/j.commatsci.2024.113366 · Cited by 9 · see citing works
Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey
Ghosh, A., Urry, C., Mishra, A., Perreault-Levasseur, L., Natarajan, P., Sanders, D., Nagai, D., Tian, C., Cappelluti, N., Kartaltepe, J., et al. (2022) · Astrophysical Journal · doi:10.3847/1538-4357/acd546 · Cited by 8 · see citing works
“(e.g., Dieleman et al. 2015; Huertas-Company et al. 2015; Tuccillo et al. 2018; Ghosh et al. 2020; Hausen & Robertson 2020; Walmsley et al. 2020; Cheng et al. 2021; VegaFerrero et al. 2021; Tarsitano et al. 2022).”
Background
The nature of giant clumps in high-z discs: a deep-learning comparison of simulations and observations
Ginzburg, O., M. Huertas-Company, Dekel, A., Mandelker, N., Snyder, G., Ceverino, D., & Primack, J. (2020) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3778 · Cited by 8 · see citing works
Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation
Variawa, M. Z., van Zyl, T. L., & Woolway, M. (2022) · IEEE Access · doi:10.1109/access.2022.3150881 · Cited by 7 · see citing works
“Hausen and Robertson [36], presented a deep learning framework, Morpheus, to classify images of galaxies.”
BackgroundMethod
Revisiting Galaxy Evolution in Morphology in the Cosmic Evolution Survey Field (COSMOS-ReGEM). I. Merging Galaxies
Ren, J., Li, N., Liu, F.-S., Cui, Q., Fu, M., & Zheng, X.-Z. (2023) · Astrophysical Journal · doi:10.3847/1538-4357/acfeee · Cited by 6 · see citing works
Deep Learning assessment of galaxy morphology in S-PLUS Data Release 1
Bom, C., Cortesi, A., Lucatelli, G., Dias, L., Schubert, P., Schwarz, G. B. O., Cardoso, N. M., Lima, E. V., de Oliveira, C. D., Sodré, L., et al. (2021) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1981 · Cited by 6 · see citing works
Study of Star Clusters in the M83 Galaxy with a Convolutional Neural Network
Bialopetravičius, J., & Narbutis, D. (2020) · Astronomical Journal · doi:10.3847/1538-3881/abbf53 · Cited by 6 · see citing works
Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning
Kao, W.-B., Zhang, Y.-X., & Wu, X.-B. (2024) · Nippon Tenmon Gakkai obun kenkyu hokoku · doi:10.1093/pasj/psae037 · Cited by 5
“Hausen & Robertson (2020) pioneered the use of convolutional neural networks (CNNs) and deep learning techniques to detect and categorize astronomical objects into various classes, including elliptical galaxies, disk galaxies, irregular galaxies, point sources, and background objects.”
Background
Deep Learning Segmentation of Spiral Arms and Bars
Walmsley, M., & Spindler, A. (2023) · arXiv.org · doi:10.48550/arxiv.2312.02908 · Cited by 5
“Hausen and Robertson [26] , [11]), masks by sparcfire [14](spiral only), and masks by our model.”
Background
Bellybutton: accessible and customizable deep-learning image segmentation
Dillavou, S., Hanlan, J. M., Chieco, A., Xiao, H., Fulco, S., Turner, K. T., & Durian, D. (2023) · Scientific Reports · doi:10.1038/s41598-024-63906-y · Cited by 5 · see citing works
“The majority focused on biological research, specifically the tracking of cells from microscopy data [10–17], but similar works tackle goals ranging from identifying and tracking 2D materials like graphene [18] to segmenting other medical or biological imaging data [19–22], images of flora and fauna [23], scanning ele…”
Background
Domain Adaptors for Hyperspectral Images
Perez, G., & Maji, S. (2022) · International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956407 · Cited by 5 · see citing works
“Deep networks can be also be used in a straightforward manner when training a network from scratch and have been successfully applied to different tasks [2], [15], [24], [34], [36], [53].”
Background
PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework
Sun, R., Jia, P., Sun, Y., Yang, Z.-M., Liu, Q., & Wei, H.-Y. (2021) · Astronomical Journal · doi:10.3847/1538-3881/ad01b5 · Cited by 5 · see citing works
“The resulting information regarding these celestial objects is then processed through photometry, astrometry, morphology classification, or segmentation algorithms (Khramtsov et al. 2019; Boucaud et al. 2020; Hausen & Robertson 2020; Dom´ınguez S´anchez et al. 2022; Casetti-Dinescu et al. 2023).”
Method
Gri Tonlamalı Görüntülerdeki Yüksek Yoğunluklu Tuz ve Biber Gürültüsünü Kaldırmak için Farklı Uyarlamalı Modifiye Riesz Ortalama Filtresi
Memis, S., & Erkan, U. (2021) · European Journal of Science and Technology · doi:10.31590/ejosat.873312 · Cited by 5 · see citing works
Segmentation of spectroscopic images of the low solar atmosphere by the self-organizing map technique
Schillirò, F., & Romano, P. (2021) · doi:10.1093/mnras/stab507 · Cited by 5
A deep learning experiment for semantic segmentation of overlapping characters in palimpsests
Perino, M., Ginolfi, M., Felici, A. C., & Rosellini, M. (2023) · Proceedings of the 2022 IMEKO TC4 International Conference on Metrology for Archaeology and Cultural Heritage · doi:10.48550/arxiv.2311.01130 · Cited by 4
A Simulation-Augmented Benchmarking Framework for Automatic RSO Streak Detection in Single-Frame Space Images
Chen, Z., Yang, Y., Bettens, A., Eun, Y., & Wu, X. (2023) · arXiv.org · doi:10.48550/arxiv.2305.00412 · Cited by 4
“For example, researchers used the UNet [3, 22] which generates DCNN outputs with the same resolution of the input image to detect small objects like point sources and spheroids [23].”
Method
Segmentation and Tracking of Eruptive Solar Phenomena With Convolutional Neural Networks
Stepanyuk, O., & Kozarev, K. (2025) · Journal of Geophysical Research · doi:10.1029/2025jh000728 · Cited by 3 · see citing works
“…neural networks (CNN) intuitively seem to be the first choice technology for tracking of CME-related phenomena (Stuardi et al. (2024); Panes et al. (2021); Hausen & Robertson (2020); Zhao et al. (2025)) still, application of CNNs for image segmentation in heliophysics has been limited.”
Adaptive weight-modified Riesz mean filter for high-density salt-and-pepper noise removal
Islam, M. J. (2025) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-025-00266-1 · Cited by 3 · see citing works
CDN-Net: Faint Celestial Target Detection Based on Densely Nested Hierarchical Network
Chen, G., Xue, B., Li, X.-Y., Cao, J., & Yin, J.-H. (2025) · Astronomical Journal · doi:10.3847/1538-3881/adae9f · Cited by 3 · see citing works
Identifying Diffuse Spatial Structures in High-energy Photon Lists
Fan, M., Wang, J., Kashyap, V., Lee, T. C. M., van Dyk, D. V., & Zezas, A. (2022) · Astronomical Journal · doi:10.3847/1538-3881/aca478 · Cited by 3
“…akin to seeded region growing (cf. SrcExtractor, Bertin & Arnouts 1996; NoiseChisel, Akhlaghi & Ichikawa 2015) and Machine Learning (e.g., Morpheus, Hausen & Robertson 2020; Mask R-CNN, Farias et al. 2020; galmask, Gondhalekar et al. 2022) have been used for the identification of features in…”
Method
galmask: A Python Package for Unsupervised Galaxy Masking
Gondhalekar, Y., de Souza, R., & Chies-Santos, A. (2022) · Research Notes of the AAS · doi:10.3847/2515-5172/ac780b · Cited by 3
“Examples include Morpheus (Hausen & Robertson 2020), a deep learning approach for pixel-level classification using semantic segmentation that requires training the model with user-specified segmentation maps, and galclean (de Albernaz Ferreira & Ferrari 2018), which was designed to remove bright…”
Method
Astronomical Pointlike Source Detection via Deep Feature Matching
Ma, L., Xin, J.-R., Du, J., Zhao, J., Wang, X., & Zhang, Y. (2024) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ad9244 · Cited by 2
Detecting Moving Objects With Machine Learning
Fraser, W. (2024) · arXiv.org · doi:10.48550/arxiv.2405.06148 · Cited by 2
“We direct the reader to Hausen and Robertson (2020) for a short discussion on overfitting avoidance through augmentation.”
Background
Discovering Galaxy Features via Dataset Distillation
Guan, H.-W., Zhao, X., Wang, Z.-S., Li, Z., & Kempe, J. (2023) · arXiv.org · doi:10.48550/arxiv.2311.17967 · Cited by 2
“CNN-based galaxy morphology classification has now been applied across multiple different surveys, including SDSS [8, 26, 10], CANDELS [15, 12, 10], and Dark Energy Survey [3, 2] with predicted features included in official surveys such as the new catalogue in SDSS-IV DR17 [9].”
Method
CMR Exploration. II. Filament Identification with Machine Learning
Xu, D., Kong, S., Kaul, A., Arce, H., & Ossenkopf-Okada, V. (2023) · Astrophysical Journal · doi:10.3847/1538-4357/acefce · Cited by 2
Artificial Intelligence for Scientific Discovery at High-Performance Computing Scales
Lee, K. L. K., & Kumar, N. (2023) · Computer · doi:10.1109/mc.2023.3241692 · Cited by 4 · see citing works
Galactic Component Mapping of Galaxy UGC 2885 by Machine Learning Classification
Kwik, R. J., Wang, J., Barmby, P., & Holwerda, B. (2022) · Advances in Space Research · doi:10.1016/j.asr.2022.04.032 · Cited by 2
Event Detection and Reconstruction Using Neural Networks in TES Devices: a Case Study for Athena/X-IFU
Vega-Ferrero, J., Ceballos, M., Cobo, B., Carrera, F., García, P., & Puyol-Gruart, J. (2022) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ac5159 · Cited by 2 · see citing works
“The list of ML applications to the fields of Astronomy and Astrophysics is already vast: object detection and segmentation (Hausen & Robertson 2020), galaxy morphological classification (HuertasCompany et al. 2015; Domínguez Sánchez et al. 2018; VegaFerrero et al. 2021), galaxy mergers and tidal…”
Method
Domain Adaptor Networks for Hyperspectral Image RecognitionPreprint
Perez, G., & Maji, S. (2021) · arXiv.org · doi:10.48550/arxiv.2108.01555 · Cited by 2 · see citing works
“Deep networks can be also be used in a straightforward manner when training a network from scratch and have been successfully applied to different tasks [2], [14], [23], [32], [34], [51].”
Background
EAA: Adapting the Segment Anything Model for Perception-driven Astronomical Source Extraction
Wang, H., Zhang, T.-S., Chen, S.-Y., Zhang, G.-Y., Shen, Z., Yang, X., Wheatley, P., Fernández, J. F., Apergis, I., Burleigh, M., et al. (2026) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ae7b24 · Cited by 1 · see citing works
Semi-supervised Source Detection in Astronomical Images: New Benchmark and Strong Baseline
Feng, L., Cao, Z.-H., Luo, A., Guo, Y.-H., Yao, S.-L., Guo, Y.-X., Jia, Q., & Liu, Y. (2026) · Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 · doi:10.1145/3770855.3818857 · Cited by 1
“, OTSU [45], SEx-tractor [1]), differential photometry [24], watershed algorithms [6], and edge detectors [21].”
High precision classification method for black tea: Deep learning combined with two-dimensional correlation spectroscopy
Liu, L., Wang, Y., Wang, B., Xu, X., & Xu, J. (2026) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2025.105580 · Cited by 1 · see citing works
The Next Generation Virgo Cluster Survey (NGVS). XL. The Morphological Classification of Virgo Cluster Galaxies
Kurzner, M. M., Côté, P., Ferrarese, L., Wang, K.-X., Peng, E., Wilkinson, S., Roediger, J., Spengler, C., Brown, T., Liu, C.-Z., et al. (2025) · Astronomical Journal · doi:10.3847/1538-3881/ae1616 · Cited by 1 · see citing works
Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Ma, C.-R., Sun, Z.-C., Jing, T., Cai, Z., Ting, Y.-S., Huang, S., & Li, M.-Y. (2025) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ae1f10 · Cited by 1 · see citing works
“The performance of ML models is often limited by the representativeness and quality of the training data, which are usually from simulations or human annotations (e.g., Hart et al. 2016; Hausen & Robertson 2020; Villaescusa-Navarro et al. 2021; Wang et al. 2022; Ono et al. 2024).”
Data-Efficient Hydrogen Adsorption Prediction in Copper Nanoclusters: A Computer Vision-Based Transfer Learning Approach
Polat, C., Kurban, H., Serpedin, E., & Kurban, M. (2025) · IEEE International Conference on Compatibility, Power Electronics and Power Engineering · doi:10.1109/cpe-powereng63314.2025.11027214 · Cited by 1 · see citing works
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide Survey
Tian, C., Urry, C., Ghosh, A., Nagai, D., Ananna, T., Powell, M., Auge, C., Mishra, A., Sanders, D., Cappelluti, N., et al. (2025) · Astrophysical Journal · doi:10.3847/1538-4357/adaec0 · Cited by 1 · see citing works
Compressed Sensing Approximate Message Passing Method for Astronomical Image Denoising
Zhang, J., Lu, M., Huang, W., Li, D., Wang, X., & Chen, C. (2024) · Cybersecurity and Cyberforensics Conference · doi:10.23919/ccc63176.2024.10662102 · Cited by 1 · see citing works
“Astronomical images from deep space exploration can be used to study the large-scale structure and distribution of the universe, discover new astronomical phenomena, and discover unknown celestial features [1,2].”
Background
Comparing Generalisation Using Crowd-Sourced vs Expert Labels for Galaxies Classification
Variawa, M. Z., van Zyl, T. L., & Woolway, M. (2020) · 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi51676.2020.9311606 · Cited by 1
“Hausen and Robertson (2020) [7], present a deep learning network to classify astronomical images.”
Background
Photometric source detection and classification algorithm based on YOLOv11 and HybridViT embedded with Astrostem
Lu, Y., Tu, L.-P., Miao, J.-W., Mao, Y., Wang, Y.-J., Ding, Y.-Y., Liu, Y.-D., & Jia, L.-Y. (2026) · Nippon Tenmon Gakkai obun kenkyu hokoku · doi:10.1093/pasj/psag144
A Deep Learning Approach For Multi-Wavelength Source Deblending And Classification
Rajesh, N., Sreeram, S., & R., R. V. (2026) · 2026 International Conference on Smart Communication and Sustainable Technologies (ICSCST) · doi:10.1109/icscst70031.2026.11709867
“Hausen and Robertson developed Morpheus [1], a U-Net variant that performs source detection and star/galaxy classification at the pixel level on HST images.”
Detection and Classification of Astronomical Sources with Astro-RetinaNet in Crowded Stellar Fields
Yan, Y.-B., Liu, C., Li, J.-D., & Wang, F. (2026) · Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ae4a24
“ML-based approaches, such as Morpheus (Hausen & Robertson 2020) and Blend2mask2flux (Boucaud et al. 2019), utilize U-Net architectures, a U-shaped convolutional neural network design (Ronneberger et al. 2015) for pixel-level segmentation and simultaneous detection-deblending tasks.”
Background Intensity Estimation for Cassini–ISS Image Using Deep-learning-based Diffusion Model
Chen, Y., Zhang, Q.-F., Zhou, T., & Tang, K. (2026) · Astronomical Journal · doi:10.3847/1538-3881/ae3001
TranLogs: Lossless Failure Recovery Empowered by Training Logs
Liu, X., & Zeng, L. (2024) · IEEE International Conference on Networking, Architecture and Storage (NAS) · doi:10.1109/nas63802.2024.10781359
“…problems is continuously tapped, methods based on deep learning ideas have been widely used in high energy physics[1][2][3], astronomical sciences[4][5], environmental sciences[6][7], computer vision[8], natural language processing[9], smart cities[10], automated driving[11][12],robot[13],…”
Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
Bolin, B., & Coughlin, M. (2024) · arXiv.org · doi:10.48550/arxiv.2409.15261
“Applications of NNs adjacent to the recognition of comets include the Morpheus network, a network designed to identify extragalactic sources in Hubble Space Telescope image data (Hausen and Robertson, 2020).”
Background
Machine-learning Application to Fermi-LAT Data: Sharpening All-sky Map and Emphasizing Variable Sources
Sato, S., Kataoka, J., Ito, S., Kotoku, J., Taki, M., Oyama, A., Toyoda, T., Nakamura, Y., & Yamamoto, M. (2021) · Astrophysical Journal · doi:10.3847/1538-4357/abf48f

Authors: Hausen, R., & Robertson, B. E.
Affiliations: University of California, Santa Cruz (US)
ORCID: Hausen, Ryan
Robertson, Brant E.
Venue: The Astrophysical Journal Supplement Series
Publisher: American Astronomical Society
Published: 2020-05-01
Volume: 248
Issue: 1
Pages: 20
ISSN: 0067-0049 (ISSN-L)
1538-4365
DOI: 10.3847/1538-4365/ab8868
OpenAlex: W2953419262
Cited by: 110
References: 130
Citations by year: 2026: 7 · 2025: 16 · 2024: 15 · 2023: 23 · 2022: 19 · 2021: 20 · 2020: 7 · 2019: 3
FWCI: 7.80
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: iopscience.iop.org/page/copyright (vor, 11 d)
iopscience.iop.org/info/page/text-and-data-mining (tdm, 11 d)
Locations: The Astrophysical Journal Supplement Series (publishedVersion)
arXiv (Cornell University) (acceptedVersion)
arXiv (Cornell University)
Funders: NASA (NNG16PJ25C)
NSF (1828315)
Institute for Advanced Study (Maureen and John Hendricks Visiting Professorship)
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 › Instrumentation › Astronomy and Astrophysical Research
Keywords: Morpheus, morphological classification, astronomical sources, source detection, semantic segmentation, pixel classification, Hubble Space Telescope, CANDELS, GOODS-S, 3D-HST, deep learning

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Morpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data

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