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Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys [arXiv:1509.03318v1]
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Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit [arXiv:1702.00403]
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Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing [arXiv:1901.05005]
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Improving Galaxy Clustering Measurements with Deep Learning: analysis of the DECaLS DR7 data [arXiv:1907.11355]
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Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems [arXiv:1912.03980]
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KiDS+VIKING-450: Improved cosmological parameter constraints from redshift calibration with self-organising maps [arXiv:2005.04207]
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Deblending galaxies with Variational Autoencoders: a joint multi-band, multi-instrument approach [arXiv:2005.12039]
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Mitigating contamination in LSS surveys: a comparison of methods [arXiv:2007.14499]
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Deep Generative Models for Galaxy Image Simulations [arXiv:2008.03833]
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Baryon Acoustic Oscillations in the projected cross-correlation function between the eBOSS DR16 quasars and photometric galaxies from the DESI Legacy Imaging Surveys [arXiv:2009.02308]
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A Generative Model of Galactic Dust Emission Using Variational Inference [arXiv:2101.11181]
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Dark Energy Survey Year 3 Results: Galaxy clustering and systematics treatment for lens galaxy samples [arXiv:2105.13540]
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Simultaneous Estimation of Large-Scale Structure and Milky Way Dust Extinction from Galaxy Surveys [arXiv:2106.08818]
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Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic Mitigation [arXiv:2106.13724]
✭ More papers listed in this repository : ML papers in cosmology.
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Finding strong lenses in CFHTLS using convolutional neural networks [arXiv:1704.02744]
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Deep Convolutional Neural Networks as strong gravitational lens detectors [arXiv:1705.07132]
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The Strong Gravitational Lens Finding Challenge [arXiv:1802.03609]
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Finding high-redshift strong lenses in DES using convolutional neural networks [arXiv:1811.03786]
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Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys [arXiv:2005.04730]
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Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach [arXiv:1712.08955]
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Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study [arXiv:1901.07047]
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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging [arXiv:1908.03610]
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Galaxy morphological classification in deep-wide surveys via unsupervised machine learning [arXiv:1909.10537]
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Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning [arXiv:2009.11932]
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Pushing automated morphological classifications to their limits with the Dark Energy Survey [arXiv:2012.07858]
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DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains [arXiv:2103.01373]
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Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning [arXiv:2105.01070]
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Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models [arXiv:2105.12149]
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Finding quadruply imaged quasars with machine learning. I. Methods [arXiv:2109.09781]
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Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects [arXiv:2009.12856]
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DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning [arXiv:2011.12437]
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Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning [arXiv:2104.12789]
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A search of the full six years of the Dark Energy Survey for outer Solar System objects [arXiv:2109.03758]
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Machine Learning for the Zwicky Transient Facility [arXiv:1902.01936]
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The Zwicky Transient Facility Alert Distribution System [arXiv:1902.02227]
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Real-bogus classification for the Zwicky Transient Facility using deep learning [arXiv:1907.11259]
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Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) [arXiv:2012.12392]
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Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks [arXiv:2106.11315]