Application of Machine-Learning Techniques in Astronomical Data Analysis

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

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