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The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying quantities such as heterogeneous material properties and turbulent flows. Deep generative…

With its exquisite sensitivity, wavelength coverage, and spatial and spectral resolution, the James Webb Space Telescope is poised to revolutionise our view of the distant, high-redshift ($z>5$) Universe. While Webb's spectroscopic…

We present a proof-of-concept analysis of photometric redshifts with Bayesian priors on physical properties of galaxies. This concept is particularly suited for upcoming/on-going large imaging surveys, in which only several broad-band…

Astrophysics of Galaxies · Physics 2015-06-23 Masayuki Tanaka

We develop a framework for using clustering-based redshift inference (cluster-$z$) to measure the evolving galaxy luminosity function (GLF) and galaxy stellar mass function (GSMF) using WISE W1 ($3.4\mu m$) mid-infrared photometry and…

Astrophysics of Galaxies · Physics 2023-04-26 Geray S. Karademir , Edward N. Taylor , Chris Blake , Michelle E. Cluver , Thomas H. Jarrett , Dian P. Triani

Measuring the statistics of galaxy peculiar velocities using redshift-space distortions is an excellent way of probing the history of structure formation. Because galaxies are expected to act as test particles within the flow of matter,…

Astrophysics · Physics 2010-04-22 Yong-Seon Song , Will J. Percival

We develop new methods to study the properties of galaxy redshift surveys and radial peculiar velocity surveys, both individually and combined. We derive the Fisher information matrix for redshift surveys, including redshift distortions and…

Astrophysics · Physics 2009-11-10 Daniel Burkey , Andy Taylor

This paper addresses the challenges of detecting anomalies in cellular networks in an interpretable way and proposes a new approach using variational autoencoders (VAEs) that learn interpretable representations of the latent space for each…

Machine Learning · Computer Science 2023-06-29 Amandeep Singh , Michael Weber , Markus Lange-Hegermann

Density estimation, compression and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models,…

Machine Learning · Statistics 2021-07-07 Ioannis Gatopoulos , Jakub M. Tomczak

Determining the distribution of redshifts of galaxies observed by wide-field photometric experiments like the Dark Energy Survey is an essential component to mapping the matter density field with gravitational lensing. In this work we…

Cosmology and Nongalactic Astrophysics · Physics 2021-06-30 J. Myles , A. Alarcon , A. Amon , C. Sánchez , S. Everett , J. DeRose , J. McCullough , D. Gruen , G. M. Bernstein , M. A. Troxel , S. Dodelson , A. Campos , N. MacCrann , B. Yin , M. Raveri , A. Amara , M. R. Becker , A. Choi , J. Cordero , K. Eckert , M. Gatti , G. Giannini , J. Gschwend , R. A. Gruendl , I. Harrison , W. G. Hartley , E. M. Huff , N. Kuropatkin , H. Lin , D. Masters , R. Miquel , J. Prat , A. Roodman , E. S. Rykoff , I. Sevilla-Noarbe , E. Sheldon , R. H. Wechsler , B. Yanny , T. M. C. Abbott , M. Aguena , S. Allam , J. Annis , D. Bacon , E. Bertin , S. Bhargava , S. L. Bridle , D. Brooks , D. L. Burke , A. Carnero Rosell , M. Carrasco Kind , J. Carretero , F. J. Castander , C. Conselice , M. Costanzi , M. Crocce , L. N. da Costa , M. E. S. Pereira , S. Desai , H. T. Diehl , T. F. Eifler , J. Elvin-Poole , A. E. Evrard , I. Ferrero , A. Ferté , B. Flaugher , P. Fosalba , J. Frieman , J. García-Bellido , E. Gaztanaga , T. Giannantonio , S. R. Hinton , D. L. Hollowood , K. Honscheid , B. Hoyle , D. Huterer , D. J. James , E. Krause , K. Kuehn , O. Lahav , M. Lima , M. A. G. Maia , J. L. Marshall , P. Martini , P. Melchior , F. Menanteau , J. J. Mohr , R. Morgan , J. Muir , R. L. C. Ogando , A. Palmese , F. Paz-Chinchón , A. A. Plazas , M. Rodriguez-Monroy , S. Samuroff , E. Sanchez , V. Scarpine , L. F. Secco , S. Serrano , M. Smith , M. Soares-Santos , E. Suchyta , M. E. C. Swanson , G. Tarle , D. Thomas , C. To , T. N. Varga , J. Weller , W. Wester

With the increasing number of deep multi-wavelength galaxy surveys, the spectral energy distribution (SED) of galaxies has become an invaluable tool for studying the formation of their structures and their evolution. In this context,…

Instrumentation and Methods for Astrophysics · Physics 2017-07-12 Joana Frontera-Pons , Florent Sureau , Jerome Bobin , Emeric Le Floc'h

Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most…

Astrophysics of Galaxies · Physics 2023-01-05 Euclid Collaboration , L. Bisigello , C. J. Conselice , M. Baes , M. Bolzonella , M. Brescia , S. Cavuoti , O. Cucciati , A. Humphrey , L. K. Hunt , C. Maraston , L. Pozzetti , C. Tortora , S. E. van Mierlo , N. Aghanim , N. Auricchio , M. Baldi , R. Bender , C. Bodendorf , D. Bonino , E. Branchini , J. Brinchmann , S. Camera , V. Capobianco , C. Carbone , J. Carretero , F. J. Castander , M. Castellano , A. Cimatti , G. Congedo , L. Conversi , Y. Copin , L. Corcione , F. Courbin , M. Cropper , A. Da Silva , H. Degaudenzi , M. Douspis , F. Dubath , C. A. J. Duncan , X. Dupac , S. Dusini , S. Farrens , S. Ferriol , M. Frailis , E. Franceschi , P. Franzetti , M. Fumana , B. Garilli , W. Gillard , B. Gillis , C. Giocoli , A. Grazian , F. Grupp , L. Guzzo , S. V. H. Haugan , W. Holmes , F. Hormuth , A. Hornstrup , K. Jahnke , M. Kümmel , S. Kermiche , A. Kiessling , M. Kilbinger , R. Kohley , M. Kunz , H. Kurki-Suonio , S. Ligori , P. B. Lilje , I. Lloro , E. Maiorano , O. Mansutti , O. Marggraf , K. Markovic , F. Marulli , R. Massey , S. Maurogordato , E. Medinaceli , M. Meneghetti , E. Merlin , G. Meylan , M. Moresco , L. Moscardini , E. Munari , S. M. Niemi , C. Padilla , S. Paltani , F. Pasian , K. Pedersen , V. Pettorino , G. Polenta , M. Poncet , L. Popa , F. Raison , A. Renzi , J. Rhodes , G. Riccio , H. -W. Rix , E. Romelli , M. Roncarelli , C. Rosset , E. Rossetti , R. Saglia , D. Sapone , B. Sartoris , P. Schneider , M. Scodeggio , A. Secroun , G. Seidel , C. Sirignano , G. Sirri , L. Stanco , P. Tallada-Crespí , D. Tavagnacco , A. N. Taylor , I. Tereno , R. Toledo-Moreo , F. Torradeflot , I. Tutusaus , E. A. Valentijn , L. Valenziano , T. Vassallo , Y. Wang , A. Zacchei , G. Zamorani , J. Zoubian , S. Andreon , S. Bardelli A. Boucaud , C. Colodro-Conde , D. Di Ferdinando , J. Graciá-Carpio , V. Lindholm , D. Maino , S. Mei , V. Scottez , F. Sureau , M. Tenti , E. Zucca , A. S. Borlaff , M. Ballardini , A. Biviano , E. Bozzo , C. Burigana , R. Cabanac , A. Cappi , C. S. Carvalho , S. Casas , G. Castignani , A. Cooray , J. Coupon , H. M. Courtois , J. Cuby , S. Davini , G. De Lucia , G. Desprez , H. Dole , J. A. Escartin , S. Escoffier , M. Farina , S. Fotopoulou , K. Ganga , J. Garcia-Bellido , K. George , F. Giacomini , G. Gozaliasl , H. Hildebrandt , I. Hook , M. Huertas-Company , V. Kansal , E. Keihanen , C. C. Kirkpatrick , A. Loureiro , J. F. Macías-Pérez , M. Magliocchetti , G. Mainetti , S. Marcin , M. Martinelli , N. Martinet , R. B. Metcalf , P. Monaco , G. Morgante , S. Nadathur , A. A. Nucita , L. Patrizii , A. Peel , D. Potter , A. Pourtsidou , M. Pöntinen , P. Reimberg , A. G. Sánchez , Z. Sakr , M. Schirmer , E. Sefusatti , M. Sereno , J. Stadel , R. Teyssier , C. Valieri , J. Valiviita , M. Viel

Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning…

Cosmology and Nongalactic Astrophysics · Physics 2021-04-21 Ben Thorne , Lloyd Knox , Karthik Prabhu

Although the optical colour-magnitude diagram of galaxies allows one to select red sequence objects, neither can it be used for galaxy classification without additional observational data such as spectra or high-resolution images, nor to…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-27 Igor Chilingarian , Ivan Zolotukhin

Upcoming deep imaging surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will be confronted with challenges that come with increased depth. One of the leading systematic errors in deep surveys is the blending of…

Cosmology and Nongalactic Astrophysics · Physics 2022-12-27 Erfan Nourbakhsh , J. Anthony Tyson , Samuel J. Schmidt , The LSST Dark Energy Science Collaboration

Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying…

Machine Learning · Computer Science 2019-10-08 Bin Dai , Yu Wang , John Aston , Gang Hua , David Wipf

While we already seem to have a general scenario of the evolution of different types of galaxies, a complete and satisfactory understanding of the processes that led to the formation of all the variety of today's galaxy types is still…

Astrophysics of Galaxies · Physics 2022-11-23 M. Siudek , K. Lisiecki , M. Mezcua , K. Małek , A. Pollo , J. Krywult , A. Karska , Junais

Surveys with James Webb Space Telescope (JWST) have discovered candidate galaxies in the first 400 Myr of cosmic time. Preliminary indications have suggested these candidate galaxies may be more massive and abundant than previously thought.…

The LIGO-Virgo-KAGRA catalog has been analyzed with an abundance of different population models due to theoretical uncertainty in the formation of gravitational-wave sources. To expedite model exploration, we introduce an efficient and…

Instrumentation and Methods for Astrophysics · Physics 2025-06-30 Matthew Mould , Noah E. Wolfe , Salvatore Vitale

Data-driven fault diagnostics of safety-critical systems often faces the challenge of a complete lack of labeled data associated with faulty system conditions (i.e., fault types) at training time. Since an unknown number and nature of fault…

Machine Learning · Computer Science 2020-10-01 Manuel Arias Chao , Bryan T. Adey , Olga Fink