A Method for the Automatic Separation of the Images of Galaxies and Stars from Measurements Made with the Cosmos Machine

MacGillivray, H. T.; Martín, Roland; Pratt, N. M.; Reddish, V. C.; Seddon, H. R.; Alexander, Liza; Walker, G. S.; Williams, Peter

doi:10.1093/mnras/176.2.265

Cited by 27 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the tasks of star-galaxy sorting and image selection are often still performed wholly by eye (the entries in the VCC, for example, were selected by visual inspection alone) many different automated techniques have been developed for discriminating between images of stars and galaxies ever since those of Oemler (1974), Godwin (1976) and MacGillivray et al (1976). These methods all involve isolating images that approximate well to the point spread function and are therefore probably stellar, from those with shallower profiles that are probably not stellar; though it should be noted that none of these automated procedures can be relied upon to yield a pure galaxy sample.…”

Section: Star-galaxy Sortingmentioning

confidence: 99%

The "Virgo photometry catalogue”; a catalogue of 1180 galaxies in the direction of the Virgo Cluster's core

Young

Currie

1998

Astron. Astrophys. Suppl. Ser.

View full text Add to dashboard Cite

Abstract. We present a new catalogue of galaxies in the direction of the Virgo Cluster's core: the Virgo Photometry Catalogue (VPC)1 . This catalogue contains 1180 galaxies (including background objects) within a 23 square-degree area of the sky centred on R.A. 1950.0 = 12 h 26 m and dec. 1950.0 = 13• 08 . The VPC galaxy sample comprises of non-stellar objects brighter than B J25 = 19.0; the completeness limits being B J25 ≈ 18.5 for the northern half of the survey area and B J25 ≈ 18.0 for the southern half.Independently-calibrated photographic surface photometry is presented for over 1000 galaxies in the U , B J and R C bands. Parameters listed for catalogued galaxies include: equatorial coordinates, morphological types, surface-brightness profile parameters (which preserve the majority of the original surface photometry information), U , B J & R C isophotal magnitudes, B J and [transformed] B total magnitudes, (U − B J ) and (B J − R C ) equal-area and total colours, apparent angular radii, ellipticities, position angles, heliocentric radial velocities and alternative designations. All total magnitudes and total colours are extrapolated according to a new system denoted t in order to distinguish it from the T system already in use.The VPC is based primarily on four (one U , two B J and one R C ) UK-Schmidt plates, all of which were digitised using the Royal Observatory Edinburgh's (ROE) COSMOS measuring machine. All magnitudes, colours and surface-brightness parameters are derived from numerical integrations of segmented plate-scan data, except for (in 109 cases) saturated or (in 51 cases) inextricablySend offprint requests to: C.K. Young by e-mailing c.young1@physics.oxford.ac.uk 1 Appendices B, C and E, which contain the surface photometry, the main catalogue and the summary catalogue respectively, are only available in electronic form. They can be obtained from Le Centre de Données astronomiques de Strasbourg (CDS) via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsweb.ustrasbg.fr/Abstract.html merged images; our segmentation software being able to cope with the vast majority of image mergers.

show abstract

Section: Star-galaxy Sortingmentioning

confidence: 99%

The "Virgo photometry catalogue”; a catalogue of 1180 galaxies in the direction of the Virgo Cluster's core

Young

Currie

1998

Astron. Astrophys. Suppl. Ser.

View full text Add to dashboard Cite

show abstract

“…In the 1970s and 1980s, when digitized images became widespread in astronomy, many authors undertook projects to create automated methods to separate stars from galaxies. The first efforts relied on purely parametric methods, such as the pioneering works of Macgillivray et al (1976), Heydon-Dumbleton et al (1989) and Maddox et al (1990). Macgillivray et al (1976) used a plot of transmission vs. log (area) 1 , fitting a discriminant function to separate stars and galaxies.…”

Section: Introductionmentioning

confidence: 99%

“…The first efforts relied on purely parametric methods, such as the pioneering works of Macgillivray et al (1976), Heydon-Dumbleton et al (1989) and Maddox et al (1990). Macgillivray et al (1976) used a plot of transmission vs. log (area) 1 , fitting a discriminant function to separate stars and galaxies. Their star/galaxy separation had a completeness (i.e, the fraction of all galaxies classified as such) of 95% and a contamination (fraction of non-galaxy objects classified as galaxies) of 5-10%.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Classifiers for Star/Galaxy Separation

Vasconcellos

Carvalho

Gal

et al. 2011

The Astronomical Journal

View full text Add to dashboard Cite

We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS DR7). Each algorithm is defined by a set of parameters which, when varied, produce different final classification trees. We extensively explore the parameter space of each algorithm, using the set of 884, 126 SDSS objects with spectroscopic data as the training set. The efficiency of star-galaxy separation is measured using the completeness function. We find that the Functional Tree algorithm (FT) yields the best results as measured by the mean completeness in two magnitude intervals: 14 ≤ r ≤ 21 (85.2%) and r ≥ 19 (82.1%). We compare the performance of the tree generated with the optimal FT configuration to the classifications provided by the SDSS parametric classifier, 2DPHOT and Ball et al. (2006). We find that our FT classifier is comparable or better in completeness over the full magnitude range 15 ≤ r ≤ 21, with much lower contamination than all but the Ball et al. classifier. At the faintest magnitudes (r > 19), our classifier is the only one able to maintain high completeness (>80%) while still achieving low contamination (∼ 2.5%). Finally, we apply our FT classifier to separate stars from galaxies in the full set of 69, 545, 326 SDSS photometric objects in the magnitude range 14 ≤ r ≤ 21.

show abstract

“…Kron 1980; Yasuda et al 2001; Leauthaud et al 2007; Irwin et al, in preparation) or fitted to the data (e.g. MacGillivray et al 1976; Heydon‐Dumbleton, Collins & MacGillivray 1989). Such cut‐based methods of star–galaxy separation have a number of benefits: they are clearly defined; they are easy to repeat or simulate and they correctly classify the majority of sources.…”

Section: Star–galaxy Classification Methodsmentioning

confidence: 99%

A Bayesian approach to star-galaxy classification

Henrion

Mortlock

Hand

2011

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

Star-galaxy classification is one of the most fundamental data-processing tasks in survey astronomy, and a critical starting point for the scientific exploitation of survey data. For bright sources this classification can be done with almost complete reliability, but for the numerous sources close to a survey's detection limit each image encodes only limited morphological information. In this regime, from which many of the new scientific discoveries are likely to come, it is vital to utilise all the available information about a source, both from multiple measurements and also prior knowledge about the star and galaxy populations. It is also more useful and realistic to provide classification probabilities than decisive classifications. All these desiderata can be met by adopting a Bayesian approach to star-galaxy classification, and we develop a very general formalism for doing so. An immediate implication of applying Bayes's theorem to this problem is that it is formally impossible to combine morphological measurements in different bands without using colour information as well; however we develop several approximations that disregard colour information as much as possible. The resultant scheme is applied to data from the UKIRT Infrared Deep Sky Survey (UKIDSS), and tested by comparing the results to deep Sloan Digital Sky Survey (SDSS) Stripe 82 measurements of the same sources. The Bayesian classification probabilities obtained from the UKIDSS data agree well with the deep SDSS classifications both overall (a mismatch rate of 0.022, compared to 0.044 for the UKIDSS pipeline classifier) and close to the UKIDSS detection limit (a mismatch rate of 0.068 compared to 0.075 for the UKIDSS pipeline classifier). The Bayesian formalism developed here can be applied to improve the reliability of any star-galaxy classification schemes based on the measured values of morphology statistics alone.Comment: Accepted 22 November 2010, 19 pages, 17 figure

show abstract

A Method for the Automatic Separation of the Images of Galaxies and Stars from Measurements Made with the Cosmos Machine

Cited by 27 publications

References 0 publications

The "Virgo photometry catalogue”; a catalogue of 1180 galaxies in the direction of the Virgo Cluster's core

The "Virgo photometry catalogue”; a catalogue of 1180 galaxies in the direction of the Virgo Cluster's core

Decision Tree Classifiers for Star/Galaxy Separation

A Bayesian approach to star-galaxy classification

Contact Info

Product

Resources

About