Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-23T09:03:07.928Z Has data issue: false hasContentIssue false
  • English
  • Français

Deep-Learning the Time Domain

Published online by Cambridge University Press:  29 August 2019

A. Mahabal ,
K. Sheth ,
F. Gieseke ,
A. Drake ,
G. Djorgovski  and
M. J. Graham
A. Mahabal
Affiliation:
Department of Astronomy, California Institute of Technology, Pasadena, CA, USA email: aam@astro.caltech.edu Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA, USA
K. Sheth
Affiliation:
Indian Institute of Technology Gandhinagar, India,
F. Gieseke
Affiliation:
Department of Computer Science, University of Copenhagen, Denmark
A. Drake
Affiliation:
Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA, USA
G. Djorgovski
Affiliation:
Department of Astronomy, California Institute of Technology, Pasadena, CA, USA email: aam@astro.caltech.edu Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA, USA
M. J. Graham
Affiliation:
Department of Astronomy, California Institute of Technology, Pasadena, CA, USA email: aam@astro.caltech.edu Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA, USA
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

“Deep learning” is finding more and more applications everywhere, and astronomy is not an exception. This talk described the application of convolutional neural networks to time-domain astronomy, specifically to light-curves of sources. The work that is discussed is based on a published paper to which reference can be made for more detail. The talk finished with a note cautioning new practitioners about the pitfalls lurking in out-of-the-box use of deep-learning techniques.

Keywords

Surveysmethods: data analysisTechniques: image processing
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium S339: Southern Horizons in Time-Domain Astronomy , November 2017 , pp. 165 - 171
Copyright
© International Astronomical Union 2019 

References

Abbott, B.P. et al. 2017, Phys. Rev. Lett., 118, 221101 CrossRefGoogle Scholar
Abraham, S., Aniyan, A. K., Kembhavi, A. K., Philip, N. S., & Vaghmare, K. 2018, MNRAS, 477, 894 CrossRefGoogle Scholar
Cabrera-Vives, G., Reyes, I., Förster, F., Estvez, P., & Maureira, J.-C. 2016, IJCNN, p. 251 Google Scholar
Charnock, T., & Moss, A. 2017, ApJ, 837, L28 CrossRefGoogle Scholar
Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441 CrossRefGoogle Scholar
Djorgovski, S. G., Graham, M. J., Donalek, C., et al. 2016, Future Gener. Comput. Syst., 59, 95 CrossRefGoogle Scholar
Djorgovski, S. G., Donalek, C., Mahabal, A., et al. 2011, in: Srivastava, A. N., Chawla, N. V., & Shehan Perera, A. (eds.), Proc., Conference on Intelligent Data Understanding (CIDU 2011), p. 174 Google Scholar
Donalek, C., Arun Kumar, A., Djorgovski, S. G., et al. 2013, in: IEEE International Conference on Big Data, 35 Google Scholar
Drake, A. J., Djorgovski, S. G., Mahabal, A., et al. 2009, ApJ, 696, 870 CrossRefGoogle Scholar
Drake, A. J., Graham, M. J., Djorgovski, S. G., et al. 2014, ApJS, 213, 9 CrossRefGoogle Scholar
Drake, A. J., Djorgovski, S. G., Catelan, M., et al. 2017, MNRAS, 469, 3688 CrossRefGoogle Scholar
Dubath, P., Rimoldini, L., Süveges, M., et al. 2011, MRAS, 414, 2602 CrossRefGoogle Scholar
George, D., Shen, H., & Huerta, E. A. 2017, arXiv:1706.07446 Google Scholar
Graham, M. J., Djorgovski, S. G., Drake, A. J., et al. 2014, MNRAS, 439, 703 10.1093/mnras/stt2499CrossRefGoogle Scholar
Hastie, T., Tibshirani, R., & Friedman, J. 2009, The Elements of Statistical Learning, 2nd ed. (Springer, New York)CrossRefGoogle Scholar
Hoyle, B. 2016, Astron. Comput., 16, 34 10.1016/j.ascom.2016.03.006CrossRefGoogle Scholar
Law, N. M., Kulkarni, S. R., Dekany, R. G., et al. 2009, PASP, 121, 1395 CrossRefGoogle Scholar
LeCun, Y., Bengio, Y., & Hinton, G. 2015, Nature, 521, 436 CrossRefGoogle Scholar
Mahabal, A. A., Djorgovski, S. G., Drake, A. J., et al. 2011, BASI, 39, 387 Google Scholar
Mahabal, A. A., Donalek, C., Djorgovski, S. G., et al. 2012, in: Griffin, E., Hanisch, R. & Seaman, R. (eds.), New Horizons in Time-Domain Astronomy, Proc. IAUS 285 (CUP: Cambridge, UK), p. 355 Google Scholar
Mahabal, A., Sheth, K., Gieseke, F., et al. 2017, in: IEEE Symposium Series (SSCI), p. 2757 Google Scholar
Morii, M., Ikeda, S., Tominaga, N., et al. 2016, PASJ,, 68, 104 CrossRefGoogle Scholar
Mukund, N., Abraham, S., Kandhasamy, S., Mitra, S., & Philip, N. S. 2017, Phys. Rev. D, 95, 104059 CrossRefGoogle Scholar
Murphy, K. P. 2012, Machine Learning: A Probabilistic Perspective (The MIT Press)Google Scholar
Netrapalli, P., Niranjan, U. N., Sanghavi, S., Anandkumar, A., & Jain, P. 2014, CoRR, arXiv:1410.7660 Google Scholar
Richards, J. W., Starr, D. L., Butler, N. R., et al. 2011, ApJ, 733, 10 CrossRefGoogle Scholar
Sedaghat, N, Mahabal, A. 2017, MNRAS, 476, 5365 CrossRefGoogle Scholar
Zevin, M., et al. 2017, Class. Quantum Grav., 34, No. 6Google Scholar