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Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference [article]

Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David Schlegel, Jon McAuliffe, Prabhat
2016 arXiv   pre-print
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.  ...  In this paper, we report on a scalable, parallel version of Celeste, suitable for learning catalogs from modern large-scale astronomical datasets.  ...  ACKNOWLEDGMENTS This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S.  ... 
arXiv:1611.03404v1 fatcat:yqrvmlv3gvdevciz72gc5rtake

Cataloging the Visible Universe through Bayesian Inference at Petascale [article]

Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, Prabhat
2018 arXiv   pre-print
We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.  ...  Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe.  ...  Andrew Gelman of Columbia University for petascale hierarchical modeling, Aramco Oil due to Ali Dogru, and the Gordon and Betty Moore Foundation.  ... 
arXiv:1801.10277v1 fatcat:eig2kwxqhrfwfkwnnzk4be45qe

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Pablo Huijse, Pablo A. Estevez, Pavlos Protopapas, Jose C. Principe, Pablo Zegers
2014 IEEE Computational Intelligence Magazine  
In this article we present an overview of machine learning and computational intelligence applications to TDA.  ...  The LSST will stream data at rates of 2 Terabytes per hour, effectively capturing an unprecedented movie of the sky.  ...  The joined catalog is "filled" by estimating the probability distributions and dependencies between the features through the Bayesian network. [33] . C.  ... 
doi:10.1109/mci.2014.2326100 fatcat:735lk6ah2rbopbaxjnnouxtsuy

Probabilistic Record Linkage in Astronomy: Directional Cross-Identification and Beyond

Tamás Budavári, Thomas J. Loredo
2015 Annual Review of Statistics and Its Application  
The association of an object's independent detections is, however, a difficult problem scientifically, computationally, and statistically, raising varied challenges across diverse astronomical applications  ...  Stars and galaxies look different through the eyes of different instruments, and their independent measurements have to be carefully combined to provide a complete, sound picture of the multicolor and  ...  NSF provided partial funding for the development of cross-identification tools via NSF grant AST-0122449, Virtual Astronomical Observatory grant VAO 2010 06, and as part of the Data Infrastructure Building  ... 
doi:10.1146/annurev-statistics-010814-020231 fatcat:qm2dzizlungfvibwxr5zavrza4

Finding Galaxies in the Shadows of Quasars with Gaussian Processes

Roman Garnett, Shirley Ho, Jeff G. Schneider
2015 International Conference on Machine Learning  
We use nearly 50 000 QSO spectra to learn a tailored Gaussian process model for quasar emission spectra, which we apply to the DLA detection problem via Bayesian model selection.  ...  The detection of DLAs in large-scale spectroscopic surveys such as SDSS-III is critical to address outstanding cosmological questions, such as the nature of galaxy formation.  ...  Acknowledgments Part of this research was supported under DOE grant number DESC0011114.  ... 
dblp:conf/icml/GarnettHS15 fatcat:qnz6rkdurzc7hooveuhor7kkdi

Machine Learning and Cosmology [article]

Cora Dvorkin, Siddharth Mishra-Sharma, Brian Nord, V. Ashley Villar, Camille Avestruz, Keith Bechtol, Aleksandra Ćiprijanović, Andrew J. Connolly, Lehman H. Garrison, Gautham Narayan, Francisco Villaescusa-Navarro
2022 arXiv   pre-print
impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities.  ...  Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped.  ...  density in our Universe, whose presence thus far has been inferred through gravitational interactions), and ordinary visible matter.  ... 
arXiv:2203.08056v1 fatcat:5y7yf57gmvbs5etm355cku73oi

Time-domain Deep-learning Filtering of Structured Atmospheric Noise for Ground-based Millimeter Astronomy

Alejandra Rocha-Solache, Iván Rodríguez-Montoya, David Sánchez-Argüelles, Itziar Aretxaga
2022 Astrophysical Journal Supplement Series  
We develop a scintillation model and employ an empirical method to generate a vast catalog of atmospheric-noise realizations and train the network with representative data.  ...  We propose an architecture composed of long short-term memory cells and an incremental training strategy inspired by transfer and curriculum learning.  ...  The authors thankfully acknowledge the computer resources provided by the Laboratorio Nacional de Supercómputo del Sureste de México, CONACYT network of national laboratories.  ... 
doi:10.3847/1538-4365/ac5259 fatcat:c5viaeu5rrfsjpckrl7g77bud4

AstroVaDEr: Astronomical Variational Deep Embedder for Unsupervised Morphological Classification of Galaxies and Synthetic Image Generation [article]

Ashley Spindler, James E. Geach, Michael J. Smith
2020 arXiv   pre-print
By utilising variational inference, we are able to use the learned GMM as a statistical prior on the latent space to facilitate random sampling and generation of synthetic images.  ...  An unsupervised clustering model is found which separates galaxies based on learned morphological features such as axis ratio, surface brightness profile, orientation and the presence of companions.  ...  This research has made use of the University of Hertfordshire high-performance computing facility (https://uhhpc.herts.ac.uk).  ... 
arXiv:2009.08470v2 fatcat:zx5h27xhz5dl3kpdqja2xx6qya

Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks – sifting the GOTO candidate stream [article]

T. L. Killestein, J. Lyman, D. Steeghs, K. Ackley, M. J. Dyer, K. Ulaczyk, R. Cutter, Y.-L. Mong, D. K. Galloway, V. Dhillon, P. O'Brien, G. Ramsay (+36 others)
2021 arXiv   pre-print
through the vast quantities of incoming data generated.  ...  This data-driven approach is uniquely scalable to the upcoming challenges and data needs of next-generation transient surveys.  ...  Leicester; Armagh Observatory & Planetarium; the National Astronomical Research Institute of Thailand (NARIT); the University of Turku; the University of Manchester; the University of Portsmouth; the Instituto  ... 
arXiv:2102.09892v1 fatcat:hwdg5xootrdjnljvp73w5yz33a

From Data to Software to Science with the Rubin Observatory LSST [article]

Katelyn Breivik, Andrew J. Connolly, K. E. Saavik Ford, Mario Jurić, Rachel Mandelbaum, Adam A. Miller, Dara Norman, Knut Olsen, William O'Mullane, Adrian Price-Whelan, Timothy Sacco, J. L. Sokoloski (+88 others)
2022 arXiv   pre-print
Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy  ...  It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection  ...  The exception to this may be with the processing of the large number of light curves for this sample.  ... 
arXiv:2208.02781v1 fatcat:nt2uwu72mnextiixwhdrkft2zi

Petabytes to Science [article]

Amanda E. Bauer, Eric C. Bellm, Adam S. Bolton, Surajit Chaudhuri, A.J. Connolly, Kelle L. Cruz, Vandana Desai, Alex Drlica-Wagner, Frossie Economou, Niall Gaffney, J. Kavelaars, J. Kinney, Ting S. Li (+13 others)
2019 arXiv   pre-print
The aim of the this workshop was to discuss important trends and technologies which may support astronomy.  ...  This document was coauthored during the workshop and edited in the weeks after. It comprises the discussions and highlights many recommendations which came out of the workshop.  ...  Citizen Science, NASA's Universe of Learning, and the EPO program of LSST.  ... 
arXiv:1905.05116v2 fatcat:lzi3j7hzk5crfbedgraa5ojwbi

DATA MINING AND MACHINE LEARNING IN ASTRONOMY

NICHOLAS M. BALL, ROBERT J. BRUNNER
2010 International Journal of Modern Physics D  
Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results.  ...  We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable  ...  Acknowledgments The authors acknowledge support from NASA through grants NN6066H156 and NNG06GF89G, from Microsoft Research, and from the University of Illinois.  ... 
doi:10.1142/s0218271810017160 fatcat:qd442usdmfgalbomkkiyvwzsfu

GWOPS: A VO-technology Driven Tool to Search for the Electromagnetic Counterpart of Gravitational Wave Event

Yunfei Xu, Dong Xu, Chenzhou Cui, Dongwei Fan, Zipei Zhu, Bangyao Yu, Changhua Li, Jun Han, Linying Mi, Shanshan Li, Boliang He, Yihan Tao (+2 others)
2020 Publications of the Astronomical Society of the Pacific  
It consists of three parts: a pipeline to select host candidates of GW and sort their priorities for follow-up observation, an identification module to find the transient from follow-up observation data  ...  Due to the limitation of the accuracy of the GW observation facility at this stage, we can only get a rough sky-localization region for the GW event, and the typical area of the region is between 200 and  ...  Dong Xu acknowledges the supports by the One-Hundred-Talent Program of the Chinese Academy of Sciences (CAS) and by the Strategic Priority Research Program Multiwavelength Gravitational Wave Universe of  ... 
doi:10.1088/1538-3873/aba69f fatcat:tgmhimflb5bjzeiybj2ogilggu

Deep neural networks to enable real-time multimessenger astrophysics

Daniel George, E. A. Huerta
2018 Physical Review D  
We showcase the application of this method for the detection and parameter estimation of gravitational waves from binary black hole mergers.  ...  To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries  ...  Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.  ... 
doi:10.1103/physrevd.97.044039 fatcat:dcsm6dnauna4xfax5v3d4bbgi4

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning [article]

Mike Walmsley, Anna M. M. Scaife, Chris Lintott, Michelle Lochner, Verlon Etsebeth, Tobias Géron, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen L. Masters, Kameswara Bharadwaj Mantha, Brooke D. Simmons
2021 arXiv   pre-print
Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch.  ...  We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained  ...  University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A&M University.  ... 
arXiv:2110.12735v1 fatcat:cnrg5rgbzrhypohvwkvcgwab7e
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