Special Issue "Application of Machine-Learning Techniques in Astronomical Data Analysis"

A special issue of Galaxies (ISSN 2075-4434).

Deadline for manuscript submissions: closed (30 June 2018)

Special Issue Editor

Guest Editor
Prof. Robert Benton Metcalf

University of Bologna, Italy
Website | E-Mail

Special Issue Information

Dear Colleagues,

Galaxies is hosting a Special Issue on machine learning in analyzing astronomical images and data. For this issue, we would like to focus on how new techniques in machine learning have been changing the way data sets are searched and used. We invite researchers to submit papers related to the use of artificial neural networks and other machine learning techniques to astronomical problems such as the classification of objects, searches for gravitational lenses, searches for other rare objects, and the detection of planets. These techniques have been advancing rapidly in recent years, but often in isolation without benefit of what has been learned in other subfields of astronomy. We wish to draw attention to new methods, new benchmarks in performance and to cross pollinate between fields.

References:

  1. Gieseke, F.; Bloemen, S.; van den Bogaard, C.; Heskes, T.; Kindler, J.; Scalzo, R.A.; Valério A.R.M. Ribeiro, V.A.R.M.; van Roestel, J.; Groot, P.J.; Yuan, F. Convolutional neural networks for transient candidate vetting in large-scale surveys. Mon. Not. Roy. Astron. Soc. 2017, 472, 3101‒3114.
  2. Sullivan, D.; Iliev, I.T.; Dixon, K.L. Using artificial neural networks to constrain the halo baryon fraction during reionization. Mon. Not. Roy. Astron. Soc. 2018, 473, 38‒58.
  3. Wright, D.E.; Lintott, C.J.; Smartt, S.J.; Smith, K.W.; Fortson, L.; Trouille, L.; Allen, C.R.; Beck, M.; Bouslog, M.C.; Boyer, A. A transient search using combined human and machine classifications. Mon. Not. Roy. Astron. Soc. 2017, 472, 1315‒1323.
  4. Petrillo, C.E.; Tortora, C.; Chatterjee, S.; Vernardos, G.; Koopmans, L.V.E.; Verdoes Kleijn, G.; Napolitano, N.R.; Covone, G.; Schneider, P.; Grado, A. Finding strong gravitational lenses in the kilo degree survey with convolutional neural networks. Mon. Not. Roy. Astron. Soc. 2017, 472, 1129‒1150.
  5. Hartley, P.; Flamary, R.; Jackson, N.; Tagore, A.S.; Metcalf, R.B. Support vector machine classification of strong gravitational lenses. Mon. Not. Roy. Astron. Soc. 2017, 471, 3378‒3397.
  6. Lanusse, F.; Ma, Q.; Li, N.; Collett, T.E.; Li, C.-L.; Ravanbakhsh, S.; Mandelbaum, R.; Poczos, B. CMU deeplens: Deep learning for automatic image-based galaxy-galaxy strong lens finding. arXiv 2017, arXiv1703.02642.
  7. Hezaveh, Y.D.; Levasseur, L.P.; Marshall, P.J. Fast automated analysis of strong gravitational lenses with convolutional neural networks. Nature 2017, 548, 555‒557.

Prof. Robert Benton Metcalf
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Galaxies is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • neural networks
  • data analysis
  • gravitational lenses
  • data analysis
  • object classification
  • data mining

Published Papers (2 papers)

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Research

Open AccessArticle SpArcFiRe: Enhancing Spiral Galaxy Recognition Using Arm Analysis and Random Forests
Received: 16 June 2018 / Revised: 30 August 2018 / Accepted: 31 August 2018 / Published: 5 September 2018
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Abstract
Automated quantification of galaxy morphology is necessary because the size of upcoming sky surveys will overwhelm human volunteers. Existing classification schemes are inadequate because (a) their uncertainty increases near the boundary of classes and astronomers need more control over these uncertainties; (b) galaxy [...] Read more.
Automated quantification of galaxy morphology is necessary because the size of upcoming sky surveys will overwhelm human volunteers. Existing classification schemes are inadequate because (a) their uncertainty increases near the boundary of classes and astronomers need more control over these uncertainties; (b) galaxy morphology is continuous rather than discrete; and (c) sometimes we need to know not only the type of an object, but whether a particular image of the object exhibits visible structure. We propose that regression is better suited to these tasks than classification, and focus specifically on determining the extent to which an image of a spiral galaxy exhibits visible spiral structure. We use the human vote distributions from Galaxy Zoo 1 (GZ1) to train a random forest of decision trees to reproduce the fraction of GZ1 humans who vote for the “Spiral” class. We prefer the random forest model over other black box models like neural networks because it allows us to trace post hoc the precise reasoning behind the regression of each image. Finally, we demonstrate that using features from SpArcFiRe—a code designed to isolate and quantify arm structure in spiral galaxies—improves regression results over and above using traditional features alone, across a sample of 470,000 galaxies from the Sloan Digital Sky Survey. Full article
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Open AccessFeature PaperArticle A Catalog of Photometric Redshift and the Distribution of Broad Galaxy Morphologies
Received: 4 April 2018 / Revised: 31 May 2018 / Accepted: 8 June 2018 / Published: 11 June 2018
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Abstract
We created a catalog of photometric redshift of ∼3,000,000 SDSS galaxies annotated by their broad morphology. The photometric redshift was optimized by testing and comparing several pattern recognition algorithms and variable selection strategies, and was trained and tested on a subset of the [...] Read more.
We created a catalog of photometric redshift of ∼3,000,000 SDSS galaxies annotated by their broad morphology. The photometric redshift was optimized by testing and comparing several pattern recognition algorithms and variable selection strategies, and was trained and tested on a subset of the galaxies in the catalog that had spectra. The galaxies in the catalog have i magnitude brighter than 18 and Petrosian radius greater than 5.5. The majority of these objects are not included in previous SDSS photometric redshift catalogs such as the photoz table of SDSS DR12. Analysis of the catalog shows that the number of galaxies in the catalog that are visually spiral increases until redshift of ∼0.085, where it peaks and starts to decrease. It also shows that the number of spiral galaxies compared to elliptical galaxies drops as the redshift increases. Full article
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