2012 Conference on Intelligent Data Understanding 2012
DOI: 10.1109/cidu.2012.6382200
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Introduction to astroML: Machine learning for astrophysics

Abstract: Abstract-Astronomy and astrophysics are witnessing dramatic increases in data volume as detectors, telescopes and computers become ever more powerful. During the last decade, sky surveys across the electromagnetic spectrum have collected hundreds of terabytes of astronomical data for hundreds of millions of sources. Over the next decade, the data volume will enter the petabyte domain, and provide accurate measurements for billions of sources. Astronomy and physics students are not traditionally trained to hand… Show more

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Cited by 192 publications
(126 citation statements)
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References 26 publications
(33 reference statements)
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“…The Lomb-Scargle routine in Numerical Recipes 2 , modified to return false alarm probability (FAP) values for all peaks, is valuable for the periodograms where there are comparatively clear-cut peaks, however for many of the spectroscopic results it is unable to return any clear periods. For these cases software was written in Python using alternative Lomb-Scargle routines provided by SCIPY library (Jones et al 2001), the ASTROML library, (Vanderplas et al 2012) and the GATSPY library, (VanderPlas & Ivezić 2015), comparing the results. We did this because in some cases the results widely differed and it gave an assessment of the stability of the calculations.…”
Section: Periodicity Of Proxima Centauri From Photometric Measurementsmentioning
confidence: 99%
“…The Lomb-Scargle routine in Numerical Recipes 2 , modified to return false alarm probability (FAP) values for all peaks, is valuable for the periodograms where there are comparatively clear-cut peaks, however for many of the spectroscopic results it is unable to return any clear periods. For these cases software was written in Python using alternative Lomb-Scargle routines provided by SCIPY library (Jones et al 2001), the ASTROML library, (Vanderplas et al 2012) and the GATSPY library, (VanderPlas & Ivezić 2015), comparing the results. We did this because in some cases the results widely differed and it gave an assessment of the stability of the calculations.…”
Section: Periodicity Of Proxima Centauri From Photometric Measurementsmentioning
confidence: 99%
“…to avoid saturation. We checked the light curves of all 55 of these objects for any signs of variability using a Lomb-Scargle periodogram via the astroML python package (Vanderplas et al 2012) and derived V magnitudes, based on the median value of those data, for objects with 3 or more epochs of data (see Fig. 3 for an example).…”
Section: Photometry From the Catalina Surveymentioning
confidence: 99%
“…Because of the relatively large 1 Eyer & Bartholdi (1999) claimed that for most practical cases, lower periodicities (higher frequencies) can be detected even for strongly (but randomly) under-sampled observations. 2 Calculated by using the astroML python module (Vanderplas et al 2012) and its bootstrapping package. 3 http://exoplanets.org/code/ uncertainties in the RV and incomplete coverage of the RV curve, we also decided to fix the eccentricity of the orbit to the slightly non-circular value determined by Lillo-Box et al (2014b), e = 0.066 +0.013 −0.017 .…”
Section: Resultsmentioning
confidence: 99%