Statistics, Data Mining, and Machine Learning in Astronomy

Ivezić, Željko; Connelly, Andrew; VanderPlas, Jacob T.; Gray, Alexander G.

doi:10.2307/j.ctvrxk1hs

Cited by 87 publications

(72 citation statements)

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“…The color map shows a well defined density peak, marked by the gray box. The proper motions of the sources that fall within the box are shown in the inset, where they are fit using robust parameter estimation of a bivariate Gaussian (Vanderplas et al 2012;Ivezić et al 2014); we retain sources whose proper motion fall within the 3-σ contour level of the ellipse that is centered on (µ * α , µ δ )=(-1.5, -0.9) mas yr −1 . We used the same sub-sample of data to build a density map in the transverse velocity phase space.…”

Section: Identification Of the New Co-moving Group And Selection Of Candidate Membersmentioning

confidence: 99%

“…Monoceros OB1's molecular cloud complex is located between 697 pc and 784 pc Zucker et al (2019), and has spawned the NGC 2264 cluster in the east, and a series of reflection nebula (termed Mon R1 by van den Bergh 1966) in the west (see Figure 1). The most well studied cluster in Mon OB1 is NGC 2264 (Dahm 2008, and references therein), (Young et al 2006;Teixeira et al 2006Teixeira et al , 2007Teixeira et al , 2008Forbrich et al 2010;Alencar et al 2010;Kwon et al 2011;Teixeira et al 2012;Mariñas et al 2013;Pineda & Teixeira 2013;Cody et al 2014;Rapson et al 2014;Venuti et al 2014;McGinnis et al 2015;Sousa et al 2016;Tobin et al 2015;Schneider et al 2018;Costado & Alfaro 2018;Venuti et al 2019;Sousa et al 2019;Montillaud et al 2019b,a;Buckner et al 2020;Jackson et al 2020;Pearson et al 2020 (Green et al 2019). The most prominent star-forming region is NGC 2264; it is located within the ellipse representing the approximate area surveyed with Spitzer by Rapson et al (2014).…”

Section: Introductionmentioning

confidence: 99%

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Monoceros OB4: a new association in Gaia DR2

Teixeira

Sicilia‐Aguilar

Hacar

et al. 2021

Monthly Notices of the Royal Astronomical Society: Letters

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We use Gaia DR2 data to survey the classic Monoceros OB1 region and look for the existence of a dispersed young population, co-moving with the cloud complex. An analysis of the distribution of proper motions reveals a 20-30 Myr association of young stars, about 300-400 pc away from the far side of the Mon OB1 complex, along the same general line-of-sight. We characterize the new association, Monoceros OB4, and estimate it contains between 1400 and 2500 stars, assuming a standard IMF, putting it on par in size with NGC 2264. We find from the internal proper motions that Mon OB4 is unbound and expanding. Our results seem to unveil a larger and more complex Monoceros star formation region, suggesting an elongated arrangement that seems to be at least 300 × 60 pc.

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Section: Identification Of the New Co-moving Group And Selection Of Candidate Membersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monoceros OB4: a new association in Gaia DR2

Teixeira

Sicilia‐Aguilar

Hacar

et al. 2021

Monthly Notices of the Royal Astronomical Society: Letters

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“…(see Table 9), and in the present work in Table 34, are almost the same (see Table 36). (Eisenhart, 1961;Ivezić et al, 2014). He applied normal distribution before Gauß established it in 1809.…”

Section: The Longitude Of the Node ω=Nmentioning

confidence: 99%

ON THE DETERMINATION OF THE SOLAR ROTATION ELEMENTS i, Ω AND PERIOD USING SUNSPOT OBSERVATIONS BY RUĐER BOŠKOVIĆ IN 1777

Husak

Brajša

Špoljarić³

2021

MGPB

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In September 1777, Ruđer Bošković observed sunspots for six days. Based on these measurements, he used his own methods to calculate the elements of the Sun’s rotation, the longitude of the node, the inclination of the solar equator and the period. He published a description of the methods, the method of observation and detailed instructions for calculations in the second chapter of the fifth part of the Opera in 1785. In this paper, Bošković’s original calculations and repeated calculations by his procedure are published. By analysing the input quantities, procedures, and results, the input quantities of the error, and the calculation results are discussed. The reproduction of Bošković’s calculations is successfully reproduced and we obtained very similar results. The conclusion proposes a relationship of Bošković’s research with mod ern astronomy.

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“…Modern astronomical data is measured in Terabytes, Petabytes and, soon, Exabytes. When astronomy crossed the 100 Terabyte scale near the end of the 20th century (e.g., Brunner et al, ; Szalay & Gray, ) a new data‐driven astronomy emerged: where data mining was mooted as the likely future (Bell, Hey, & Szalay, ; Ivezić, Connelly, VanderPlas, & Gray, ; Szalay & Gray, ). The expectation was an increased reliance on automated systems to locate, classify, and characterize objects.…”

Section: Introductionmentioning

confidence: 99%

“…The need for practical data mining infrastructure led to the development of tools such as DAMEWARE (DAta Mining & Exploration Web Application and REsource; Brescia et al, , ). See also Ivezić et al () and the AstroML Python module for a selection of data mining implementations, with an emphasize on large‐scale observational surveys.…”

Section: Introductionmentioning

confidence: 99%

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Fluke

Jacobs

2019

WIREs Data Min & Knowl

102

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Machine learning (automated processes that learn by example in order to classify, predict, discover, or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses, or becomes established. This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Machine Learning

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Statistics, Data Mining, and Machine Learning in Astronomy

Cited by 87 publications

References 0 publications

Monoceros OB4: a new association in Gaia DR2

Monoceros OB4: a new association in Gaia DR2

ON THE DETERMINATION OF THE SOLAR ROTATION ELEMENTS i, Ω AND PERIOD USING SUNSPOT OBSERVATIONS BY RUĐER BOŠKOVIĆ IN 1777

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

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