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Detection of quasars in the time domain

Published online by Cambridge University Press:  30 May 2017

Matthew J. Graham
Affiliation:
California Institute of Technology, Pasadena CA, USA email: mjg@caltech.edu, george@caltech.edu, ajd@caltech.edu, ashish@caltech.edu National Optical Astronomy Observatory, Tucson AZ, USA
S. G. Djorgovski
Affiliation:
California Institute of Technology, Pasadena CA, USA email: mjg@caltech.edu, george@caltech.edu, ajd@caltech.edu, ashish@caltech.edu
Daniel J. Stern
Affiliation:
JPL, Pasadena CA, USA email: daniel.k.stern@jpl.nasa.gov
Andrew Drake
Affiliation:
California Institute of Technology, Pasadena CA, USA email: mjg@caltech.edu, george@caltech.edu, ajd@caltech.edu, ashish@caltech.edu
Ashish Mahabal
Affiliation:
California Institute of Technology, Pasadena CA, USA email: mjg@caltech.edu, george@caltech.edu, ajd@caltech.edu, ashish@caltech.edu
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Abstract

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The time domain is the emerging forefront of astronomical research with new facilities and instruments providing unprecedented amounts of data on the temporal behavior of astrophysical populations. Dealing with the size and complexity of this requires new techniques and methodologies. Quasars are an ideal work set for developing and applying these: they vary in a detectable but not easily quantifiable manner whose physical origins are poorly understood. In this paper, we will review how quasars are identified by their variability and how these techniques can be improved, what physical insights into their variability can be gained from studying extreme examples of variability, and what approaches can be taken to increase the number of quasars known. These will demonstrate how astroinformatics is essential to discovering and understanding this important population.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2017 

References

Aretxaga, I., Cid Fernandes, R., & Terlevich, R., 1997, MNRAS, 286, 271 Google Scholar
Artymowicz, P. & Lubow, S. H., 1996, ApJ, 467, 77 Google Scholar
Bauer, A., et al., 2009, ApJ, 696, 1241 Google Scholar
Chornock, R., et al., 2014, ApJ, 780, 44 CrossRefGoogle Scholar
Cid Fernandes, R., Terlevich, R., & Aretxaga, I. 1997, MNRAS, 289, 318 Google Scholar
Drake, A. J., et al., 2009, ApJ, 696, 870 Google Scholar
Drake, A. J., et al., 2011, ApJ, 735, 106 Google Scholar
Drake, A. J., et al., 2013, ApJ, 763, 32 CrossRefGoogle Scholar
Gezari, S., et al., 2012, Nature, 485, 217 Google Scholar
Graham, M. J., et al., 2013, MNRAS Google Scholar
Graham, M. J., et al., 2014, MNRAS Google Scholar
Graham, M. J., et al., 2015a, Nature Google Scholar
Graham, M. J., et al., 2015b, MNRAS Google Scholar
Graham, M. J., 2017, MNRAS, submittedGoogle Scholar
Guillochon, J. & Ramirez-Ruiz, E., 2015, ApJ, 809, 166 Google Scholar
Hawkins, M. R. S., 1993, Nature, 366, 242 CrossRefGoogle Scholar
Hawkins, M. R. S., 2010, MNRAS, 405, 1940 Google Scholar
Hayasaki, K., Mineshige, S., & Ho, L. C., 2008, ApJ, 682, 1134 Google Scholar
Kasliwal, V. P., Vogeley, M. S. & Richards, G. T., 2016, arXiv:1607.04299Google Scholar
Kawaguchi, T., et al., 1998, ApJ, 504, 671 Google Scholar
Kelly, B. C., Bechtold, J., & Siemiginowska, A., 2009, ApJ, 698, 895 Google Scholar
Kelly, B. C., et al., 2013, ApJ, 779, 187 Google Scholar
Kelly, B. C., et al., 2014, ApJ, 788, 33 CrossRefGoogle Scholar
Klebanov, L. B., 2016, arXiv:1611.05410Google Scholar
Komossa, S., et al., 2015, A&A, 574, 121 Google Scholar
Kozlowski, S., 2016a, MNRAS, 458, 2787 Google Scholar
Kozlowski, S., 2016b, arXiv:1611.08248Google Scholar
Krone-Martins, A., Ishida, E. E. O., & de Souza, R. S., 2014, MNRAS, 443, L34 Google Scholar
LaMassa, S. M., et al., 2015, ApJ, 800, 144 Google Scholar
Lawrence, A., et al., 2016, MNRAS Google Scholar
MacLeod, C., et al., 2010, AJ, 721, 1014 CrossRefGoogle Scholar
MacLeod, C., et al., 2016, ApJ, 457, 389 Google Scholar
Matthews, T. & Sandage, A., 1963, ApJ, 46, 138 Google Scholar
Meusinger, H., et al., 2010, A&A, 512, A1 Google Scholar
Nguyen, K., Bogdanovic, T., 2016, ApJ, 828, 68 Google Scholar
Mondal, D. & Percival, D. B., 2011, in Statistical Challenges in Modern Astronomy V, eds. Ferguson, E. D., Babu, G. J., Springer, New York, pp. 403 Google Scholar
Ruan, J. J., et al., 2016, ApJ, 826, 188 Google Scholar
Runnoe, J. C., et al., 2016, MNRAS, 455, 1691 Google Scholar
Schmidt, K. B., et al., 2010, ApJ, 714, 1194 CrossRefGoogle Scholar
Schmidt, K. B., et al., 2012, ApJ, 744, 147 Google Scholar
Stern, D., et al., 2016, ApJ, submittedGoogle Scholar
Torricelli-Ciamponi, G., et al., 2000, A&A, 358, 57 Google Scholar
Ulrich, M.-H., Maraschi, L., & Urry, C. M., 1997, ARA&A, 35, 445 Google Scholar
Vanden Berk, D. E., et al., 2004, ApJ, 601, 692 Google Scholar
van der Maarten, L. J. P., & Hinton, G., 2008, Jour. Machine Learning Research, 9, 2579 Google Scholar
Vaughan, S., et al., 2016, MNRAS, 461, 3145 Google Scholar