Virtual Observatories, Data Mining, and Astroinformatics

Borne, K. D.

doi:10.1007/978-94-007-5618-2_9

Cited by 15 publications

(20 citation statements)

References 58 publications

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“…Sky and ground-based surveys can produce hundreds of terabytes (TB) and up to 100 (or more) petabytes (PB), both in the image data archive and in the object catalogues (Borne 2013). For instance, the ESA space mission Gaia (Perryman et al 2001), launched in December 2013, is expected to produce a final archive of about 1 PB (10 15 bytes) through five years of exploration.…”

Section: Introductionmentioning

confidence: 99%

A new method for the inversion of atmospheric parameters of A/Am stars

Gebran

Farah

Paletou

et al. 2016

A&A

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Context. We present an automated procedure that simultaneously derives the effective temperature T eff , surface gravity log g, metallicity [Fe/H], and equatorial projected rotational velocity v sin i for "normal" A and Am stars. The procedure is based on the principal component analysis (PCA) inversion method, which we published in a recent paper . Aims. A sample of 322 high-resolution spectra of F0-B9 stars, retrieved from the Polarbase, SOPHIE, and ELODIE databases, were used to test this technique with real data. We selected the spectral region from 4400−5000 Å as it contains many metallic lines and the Balmer Hβ line. Methods. Using three data sets at resolving powers of R = 42 000, 65 000 and 76 000, about ∼6.6 × 10 6 synthetic spectra were calculated to build a large learning database. The online power iteration algorithm was applied to these learning data sets to estimate the principal components (PC). The projection of spectra onto the few PCs offered an efficient comparison metric in a low-dimensional space. The spectra of the well-known A0-and A1-type stars, Vega and Sirius A, were used as control spectra in the three databases. Spectra of other well-known A-type stars were also employed to characterize the accuracy of the inversion technique. Results. We inverted all of the observational spectra and derived the atmospheric parameters. After removal of a few outliers, the PCA-inversion method appeared to be very efficient in determining T eff , [Fe/H], and v sin i for A/Am stars. The derived parameters agree very well with previous determinations. Using a statistical approach, deviations of around 150 K, 0.35 dex, 0.15 dex, and 2 km s −1 were found for T eff , log g, [Fe/H], and v sin i with respect to literature values for A-type stars. Conclusions. The PCA inversion proves to be a very fast, practical, and reliable tool for estimating stellar parameters of FGK and A stars and for deriving effective temperatures of M stars.

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Section: Introductionmentioning

confidence: 99%

A new method for the inversion of atmospheric parameters of A/Am stars

Gebran

Farah

Paletou

et al. 2016

A&A

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“…The LSST will be dedicated exclusively to the survey program, thus follow-up observations (light curves, spectroscopy, multiple wavelengths), which are scientifically essential, must be done by other facilities around the world [10]. With this goal the LSST will generate millions of event alerts during each night for 10 years.…”

Section: Machine Learningmentioning

confidence: 99%

“…The development of fully-automated and robust methods for the rapid classification of what is known, and the characterization of emergent behavior in these massive astronomical databases are the main tasks of these new fields. We believe that computational intelligence, machine learning and statistics will play major roles in the development of these methods [12], [10].…”

Section: Introductionmentioning

confidence: 99%

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

Huijse

Estévez

Protopapas

et al. 2014

IEEE Comput. Intell. Mag.

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Time-domain astronomy (TDA) is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new astronomical sky surveys. For example, the Large Synoptic Survey Telescope (LSST), which will begin operations in northern Chile in 2022, will generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky. The LSST will stream data at rates of 2 Terabytes per hour, effectively capturing an unprecedented movie of the sky.The LSST is expected not only to improve our understanding of time-varying astrophysical objects, but also to reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with a change of paradigm to data-driven astronomy, the fields of astroinformatics and astrostatistics have been created recently. The new data-oriented paradigms for astronomy combine statistics, data mining, knowledge discovery, machine learning and computational intelligence, in order to provide the automated and robust methods needed for the rapid detection and classification of known astrophysical objects as well as the unsupervised characterization of novel phenomena. In this article we present an overview of machine learning and computational intelligence applications to TDA. Future big data challenges and new lines of research in TDA, focusing on the LSST, are identified and discussed from the viewpoint of computational intelligence/machine learning. Interdisciplinary collaboration will be required to cope with the challenges posed by the deluge of astronomical data coming from the LSST.

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“…Modern astronomy has been becoming dependent on data obtained from autonomous digital sky surveys, allowing data-driven and statistical analysis that would not have been possible with manually controlled telescopes (Borne, 2013;Djorgovski et al, 2013;Edwards and Gaber, 2014). As the scales of these databases and the breadth of astronomical pipelines continue to grow, it is expected that efficient and reliable methods for analyzing these data will become crucial for astronomy research.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach to machine learning annotation of large galaxy image databases

Kuminski

Shamir

2018

Astronomy and Computing

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Modern astronomy relies on massive databases collected by robotic telescopes and digital sky surveys, acquiring data in a much faster pace than what manual analysis can support. Among other data, these sky surveys collect information about millions and sometimes billions of extra-galactic objects. Since the very large number of objects makes manual observation impractical, automatic methods that can analyze and annotate extra-galactic objects are required to fully utilize the discovery power of these databases. Machine learning methods for annotation of celestial objects can be separated broadly into methods that use the photometric information collected by digital sky surveys, and methods that analyze the image of the object. Here we describe a hybrid method that combines photometry and image data to annotate galaxies by their morphology, and a method that uses that information to identify objects that are visually similar to a query object (query-by-example). The results are compared to using just photometric information from SDSS, and to using just the morphological descriptors extracted directly from the images. The comparison shows that for automatic classification the image data provide marginal addition to the information provided by the photometry data. For query-by-example, however, the analysis of the image data provides more information that improves the automatic detection substantially. The source code and binaries of the method can be downloaded through the Astrophysics Source Code Library.

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Virtual Observatories, Data Mining, and Astroinformatics

Cited by 15 publications

References 58 publications

A new method for the inversion of atmospheric parameters of A/Am stars

A new method for the inversion of atmospheric parameters of A/Am stars

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

A hybrid approach to machine learning annotation of large galaxy image databases

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