Large Databases in Astronomy

Szalay, Alexander S.; Gray, Jim; Kunszt, Péter Z.; Thakar, Anirudha; Slutz, Don

doi:10.1007/10849171_7

Cited by 15 publications

(13 citation statements)

References 7 publications

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“…We have written a unique and powerful database server that allows users to make sophisticated queries efficiently. The general strategy of compiling a large astronomical database is described in Szalay et al (2001), and our method of dividing the celestial sphere into a hierarchical triangular mesh, which enables efficient access based on position, is described in Kunszt et al (2000). The database server software is described in Thakar et al (2000).…”

Section: Catalog Archive Servermentioning

confidence: 99%

Sloan Digital Sky Survey: Early Data Release

Stoughton¹,

Lupton²,

Bernardi³

et al. 2002

The Astronomical Journal

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2,115

1,463

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The Sloan Digital Sky Survey (SDSS) is an imaging and spectroscopic survey that will eventually cover approximately one-quarter of the celestial sphere and collect spectra of %10 6 galaxies, 100,000 quasars, 30,000 stars, and 30,000 serendipity targets. In 2001 June, the SDSS released to the general astronomical community its early data release, roughly 462 deg 2 of imaging data including almost 14 million detected objects and 54,008 follow-up spectra. The imaging data were collected in drift-scan mode in five bandpasses (u, g, r, i, and z); our 95% completeness limits for stars are 22.0, 22.2, 22.2, 21.3, and 20.5, respectively. The photometric calibration is reproducible to 5%, 3%, 3%, 3%, and 5%, respectively. The spectra are flux-and wavelength-calibrated, with 4096 pixels from 3800 to 9200 Å at R % 1800. We present the means by which these data are distributed to the astronomical community, descriptions of the hardware used to obtain the data, the software used for processing the data, the measured quantities for each observed object, and an overview of the properties of this data set.

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Section: Catalog Archive Servermentioning

confidence: 99%

Sloan Digital Sky Survey: Early Data Release

Stoughton¹,

Lupton²,

Bernardi³

et al. 2002

The Astronomical Journal

Self Cite

2,115

1,463

View full text Add to dashboard Cite

show abstract

“…Initial experiments support our expectations that these procedures indeed converge very rapidly, only in a few iterations, and are insensitive to the matching limit once the Bayes factor is less than unity. For varying unknown priors one can use some sky tesselation schemes, such as HEALPix (Górski et al 2005), Igloo (Crittenden 2000) or HTM (Szalay et al 2005), and estimate a piecewise constant prior (uniform in the cells) using the same methodology. Naturally other more sophisticated models can also be used in the same spirit, e.g., specific functional forms or smoothing to limit the gradient, as well as tapered windows when required.…”

Section: Self-consistent Estimationmentioning

confidence: 99%

Probabilistic Cross‐Identification of Astronomical Sources

Budavàri

Szalay

2008

ApJ

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131

188

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We present a general probabilistic formalism for cross-identifying astronomical point sources in multiple observations. Our Bayesian approach, symmetric in all observations, is the foundation of a unified framework for object matching, where not only spatial information, but also physical properties, such as colors, redshift, and luminosity, can be considered in a natural way. We provide a practical recipe to implement an efficient recursive algorithm to evaluate the Bayes factor over a set of catalogs with known circular errors in positions. This new methodology is crucial for studies leveraging the synergy of today's multiwavelength observations and to enter the time domain science of the upcoming survey telescopes.

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“…The Sloan Digital Sky Survey (SDSS, see York et al 2000) has observed over 10 000 deg 2 of the sky, performing multi-band photometry and spectroscopy (Szalay et al 1999). In 2009, before the start of the Baryon Oscillation Spectroscopic Survey (BOSS, Schlegel et al 2009), in the third stage of the project (SDSS-III), the spectrograph operating the observations has been upgraded.…”

Section: Data Selection and Predictive Samplementioning

confidence: 99%

Galaxy Spectra neural Networks (GaSNets). I. Searching for strong lens candidates in eBOSS spectra using Deep Learning

Zhong,

Li,

Napolitano

2022

Preprint

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With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we introduce a family of deep learning tools to classify features in one-dimensional spectra. As the first application of these Galaxy Spectra neural Networks (GaSNets), we focus on tools specialized at identifying emission lines from strongly lensed star-forming galaxies in the eBOSS spectra. We first discuss the training and testing of these networks and define a threshold probability, P L , of 95% for the high quality event detection. Then, using a previous set of spectroscopically selected strong lenses from eBOSS, confirmed with HST, we estimate a completeness of ∼80% as the fraction of lenses recovered above the adopted P L . We finally apply the GaSNets to ∼ 1.3M spectra to collect a first list of ∼430 new high quality candidates identified with deep learning applied to spectroscopy and visually graded as highly probable real events. A preliminary check against ground-based observations tentatively shows that this sample has a confirmation rate of 38%, in line with previous samples selected with standard (no deep learning) classification tools and follow-up by Hubble Space Telescope. This first test shows that machine learning can be efficiently extended to feature recognition in the wavelength space, which will be crucial for future surveys like 4MOST, DESI, Euclid, and the Chinese Space Station Telescope (CSST).

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Large Databases in Astronomy

Cited by 15 publications

References 7 publications

Sloan Digital Sky Survey: Early Data Release

Sloan Digital Sky Survey: Early Data Release

Probabilistic Cross‐Identification of Astronomical Sources

Galaxy Spectra neural Networks (GaSNets). I. Searching for strong lens candidates in eBOSS spectra using Deep Learning

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