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Pushing the technical frontier: From overwhelmingly large data sets to machine learning

Published online by Cambridge University Press:  10 June 2020

Viviana Acquaviva*
Affiliation:
Physics Department, New York City College of Technology, 300 Jay Street, Brooklyn NY 11201 email: [email protected]
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Abstract

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This paper summarizes my thoughts, given in an invited review at the IAU symposium 341 “Challenges in Panchromatical Galaxy Modelling with Next Generation Facilities”, about how machine learning methods can help us solve some of the big data problems associated with current and upcoming large galaxy surveys.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Astropy Collaboration. 2013, Astron. Astrophys., 558, A33Google Scholar
Astropy Collaboration. 2018, Astron. J., 156, 123Google Scholar
de la Calleja, J. & Fuentes, O. 2004, Mon. Not. R. Astron. Soc., 349, 87CrossRefGoogle Scholar
Dieleman, S., Willett, K. W., & Dambre, J. 2015, Mon. Not. R. Astron. Soc., 450, 144110.1093/mnras/stv632CrossRefGoogle Scholar
Fussell, L. & Moews, B. 2018, ArXiv e-prints, 1811.03081Google Scholar
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. 2014, ArXiv e-prints, 1406.2661Google Scholar
Hocking, A., Geach, J. E., Sun, Y., & Davey, N. 2018, Mon. Not. R. Astron. Soc., 473, 1108CrossRefGoogle Scholar
Huertas-Company, M., Primack, J. R., Dekel, A., Koo, D. C., Lapiner, S., Ceverino, D., Simons, R. C., Snyder, G. F., Bernardi, M., Chen, Z., Domnguez-Sánchez, H., Lee, C. T., Margalef-Bentabol, B., & Tuccillo, D. 2018, Astrophys. J., 858, 114CrossRefGoogle Scholar
Katebi, R., Zhou, Y., Chornock, R., & Bunescu, R. 2019, Mon. Not. R. Astron. Soc., 486, 153910.1093/mnras/stz915CrossRefGoogle Scholar
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., & Shi, W. 2016, arXiv e-prints,arXiv:1609.04802Google Scholar
Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., Raddick, M. J., Nichol, R. C., Szalay, A., Andreescu, D., Murray, P., & Vandenberg, J. 2008, Mon. Not. R. Astron. Soc., 389, 1179CrossRefGoogle Scholar
Lipton, Z. C. 2016, arXiv e-prints,arXiv:1606.03490Google Scholar
Lundberg, S. M., Erion, G. G., & Lee, S.-I. 2018, ArXiv e-printsGoogle Scholar
Naul, B., Bloom, J. S., Pérez, F., & van der Walt, S. 2018, Nature Astronomy, 2, 151CrossRefGoogle Scholar
Norris, R. P. 2017, Publications of the Astron. Soc. of Australia, 34, e007CrossRefGoogle Scholar
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., & McFarland, J. 2017, ArXiv e-prints, 1702,arXiv:1702.07675Google Scholar
Collaboration, Planck. 2016, A&A, 594, A10Google Scholar
Schawinski, K., Zhang, C., Zhang, H., Fowler, L., & Santhanam, G. K. 2017, Mon. Not. R. Astron. Soc., 467, L110Google Scholar
Tuccillo, D., Huertas-Company, M., Decencière, E., & Velasco-Forero, S. 2017, in IAU Symposium, Vol. 325, Astroinformatics, ed. Brescia, M., Djorgovski, S. G., Feigelson, E. D., Longo, G., & Cavuoti, S., 191–196CrossRefGoogle Scholar
Tulio Ribeiro, M., Singh, S., & Guestrin, C. 2016, ArXiv e-prints, 1602.04938Google Scholar
Ustun, B., Spangher, A., & Liu, Y. 2018, ArXiv e-prints, 1809.06514Google Scholar
Vanderplas, J., Connolly, A., Ivezić, Ž., & Gray, A. 2012, in Conference on Intelligent Data Understanding (CIDU), 47–54Google Scholar
Zhang, R. & Zou, Q. 2018, Journal of Physics: Conference Series, 1061, 012012CrossRefGoogle Scholar
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. 2017, arXiv e-prints,arXiv:1703.10593Google Scholar