Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit
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TLDR
The ability to better recover detailed features from low-signal-to-noise and low angular resolution imaging data significantly increases the ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope and the Hubble and James Webb space telescopes.Abstract:
Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon-Nyquist sampling theorem. Here we train a generative adversarial network (GAN) on a sample of $4,550$ images of nearby galaxies at $0.01<z<0.02$ from the Sloan Digital Sky Survey and conduct $10\times$ cross validation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance which far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low-signal-to-noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.read more
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Recent advances and applications of machine learning in solid-state materials science
TL;DR: A comprehensive overview and analysis of the most recent research in machine learning principles, algorithms, descriptors, and databases in materials science, and proposes solutions and future research paths for various challenges in computational materials science.
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CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
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Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters
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References
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Euclid Definition Study Report
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TL;DR: Euclid as discussed by the authors is a space-based survey mission from the European Space Agency designed to understand the origin of the universe's accelerating expansion, using cosmological probes to investigate the nature of dark energy, dark matter and gravity by tracking their observational signatures.
Posted Content
LSST Science Book, Version 2.0
Paul A. Abell,J. Allison,Scott F. Anderson,John Andrew,J.Roger P. Angel,Lee Armus,David Arnett,S. Asztalos,Tim Axelrod,Stephen Bailey,David R. Ballantyne,J. Bankert,Wayne A. Barkhouse,Jeffrey D. Barr,L. Felipe Barrientos,Aaron J. Barth,James G. Bartlett,Andrew C. Becker,Jacek Becla,Timothy C. Beers,Joseph P. Bernstein,Rahul Biswas,Michael R. Blanton,Joshua S. Bloom,John J. Bochanski,P. Boeshaar,Kirk D. Borne,Marusa Bradac,W. N. Brandt,Carrie Bridge,Michael E. Brown,Robert J. Brunner,James S. Bullock,Adam J. Burgasser,J. H. Burge,D. L. Burke,Phillip Cargile,Srinivasan Chandrasekharan,George Chartas,Steven R. Chesley,You-Hua Chu,D. Cinabro,Mark Claire,Charles F. Claver,Douglas Clowe,A. J. Connolly,Kem H. Cook,Jeff Cooke,Asantha Cooray,Kevin R. Covey,Christopher S. Culliton,Roelof S. de Jong,Willem H. de Vries,Victor P. Debattista,Francisco Delgado,Ian P. Dell'Antonio,Saurav Dhital,Rosanne Di Stefano,Mark Dickinson,Benjamin Dilday,S. G. Djorgovski,Gregory Dobler,Ciro Donalek,G. P. Dubois-Felsmann,Josef Durech,Ardis Eliasdottir,Michael Eracleous,Laurent Eyer,Emilio E. Falco,Xiaohui Fan,Christopher D. Fassnacht,Henry C. Ferguson,Yanga R. Fernandez,Brian D. Fields,Douglas P. Finkbeiner,Eduardo E. Figueroa,Derek B. Fox,Harold Francke,James Frank,Josh Frieman,S. Fromenteau,Muhammad Furqan,Gaspar Galaz,Avishay Gal-Yam,Peter M. Garnavich,Eric Gawiser,John C. Geary,Perry M. Gee,Robert R. Gibson,Kirk Gilmore,E. Grace,Richard F. Green,William J. Gressler,Carl J. Grillmair,Salman Habib,J. S. Haggerty,Mario Hamuy,Alan W. Harris,Suzanne L. Hawley,Alan Heavens,Leslie Hebb,Todd J. Henry,Edward Hileman,Eric J. Hilton,Keri Hoadley,Jay B. Holberg,M. J. Holman,Steve B. Howell,Leopoldo Infante,Zeljko Ivezic,Suzanne Jacoby,Bhuvnesh Jain,Jedicke,M. James Jee,J. Garrett Jernigan,Saurabh Jha,Kathryn V. Johnston,R. Lynne Jones,Mario Juric,Mikko Kaasalainen,Styliani,Kafka,Steven M. Kahn,Nathan A. Kaib,Jason S. Kalirai,J. Kantor,Mansi M. Kasliwal,Charles R. Keeton,Richard Kessler,Zoran Knezevic,Adam F. Kowalski,V. Krabbendam,K. Simon Krughoff,Shrinivas R. Kulkarni,Stephen Kuhlman,Mark Lacy,Sébastien Lépine,Ming Liang,A. Y. Lien,Paulina Lira,Knox S. Long,S. Lorenz,Jennifer M. Lotz,Robert H. Lupton,Julie Lutz,Lucas M. Macri,Ashish Mahabal,Rachel Mandelbaum,Phil Marshall,Morgan May,Peregrine M. McGehee,Brian Meadows,Alan Meert,Andrea Milani,Christopher J. Miller,M. L. Miller,David J. Mills,Dante Minniti,David G. Monet,Anjum S. Mukadam,Ehud Nakar,Douglas R. Neill,Jeffrey A. Newman,Sergei Nikolaev,Martin Nordby,Paul O'Connor,Masamune Oguri,John Oliver,Scot S. Olivier,Julia K. Olsen,Knut Olsen,Edward W. Olszewski,Hakeem M. Oluseyi,Nelson Padilla,Alex Parker,Joshua Pepper,John R. Peterson,Catherine Petry,Philip A. Pinto,James Pizagno,Bogdan Popescu,Andrej Prsa,Veljko Radcka,M. Jordan Raddick,Andrew P. Rasmussen,Arne Rau,Jeonghee Rho,James E. Rhoads,Gordon T. Richards,Stephen T. Ridgway,Brant Robertson,Rok Roškar,Abhijit Saha,Ata Sarajedini,Evan Scannapieco,T. Schalk,Rafe Schindler,Samuel Schmidt,Sarah J. Schmidt,Donald P. Schneider,German Schumacher,Ryan Scranton,Jacques Sebag,Lynn G. Seppala,Ohad Shemmer,Joshua D. Simon,M. Sivertz,Howard A. Smith,J. Allyn Smith,Nathan Smith,Anna H. Spitz,A. Stanford,Keivan G. Stassun,Jay Strader,Michael A. Strauss,Christopher W. Stubbs,Donald W. Sweeney,Alexander S. Szalay,Paula Szkody,Masahiro Takada,Paul Thorman,David Trilling,Virginia Trimble,Anthony Tyson,Richard Van Berg,Daniel E. Vanden Berk,Jake Vanderplas,Licia Verde,Bojan Vrsnak,Lucianne M. Walkowicz,Benjamin D. Wandelt,Sheng Wang,Yun Wang,Michael Warner,Risa H. Wechsler,Andrew A. West,Oliver Wiecha,Benjamin F. Williams,Beth Willman,David Wittman,Sidney C. Wolff,W. Michael Wood-Vasey,P. Wozniak,Patrick A. Young,Andrew R. Zentner,Hu Zhan +245 more
TL;DR: The Large Synoptic Survey Telescope (LSST) as discussed by the authors will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 degrees.
Proceedings ArticleDOI
Chaotic communications in the presence of noise
TL;DR: By modulating data on the chaotic signal used to synchronize two nonlinear systems, this work has created a Low Probability of Intercept (LPI) communications system and derived the equations which govern the system.
LSST Science Book, Version 2.0
Paul A. Abell,Houston Nasa,J. Allison,Alabama A-M,Scott F. Anderson,John Andrew,Tucson Noao,J.Roger P. Angel,U Arizona,Lee Armus,David Arnett,S. J. Asztalos,Berkeley Lbl,Tim Axelrod,Lsst Corp.,Stephen Bailey,David R. Ballantyne,Georgia Tech,J. Bankert,U Purdue,Wayne A. Barkhouse,U North Dakota,Jeffrey D. Barr,L. Felipe Barrientos,Catolica Chile U.,Aaron J. Barth,Irvine Uc,James G. Bartlett,Paris Diderot U.,Andrew C. Becker,Jacek Becla,Timothy C. Beers,Joseph P. Bernstein,Urbana Illinois U.,U New York,Berkeley Uc,Davis Uc,U Vanderbilt,Jpl Caltech,U Ohio,Livermore Llnl,Florida Inst. Tech.,Astrophys. Inst. Potsdam +42 more
Journal ArticleDOI
Importance of input data normalization for the application of neural networks to complex industrial problems
J. Sola,J. Sevilla +1 more
TL;DR: It is shown how data normalization affects the performance error of parameter estimators trained to predict the value of several variables of a PWR nuclear power plant.