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)
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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.
- machine learning
- deep learning
- neural networks
- data analysis
- gravitational lenses
- data analysis
- object classification
- data mining