Automated Transient Identification in the Dark Energy Survey

Goldstein, D.; D’Andrea, C. B.; Fischer, J. A.; Foley, R. J.; Gupta, R.; Keßler, R.; Kim, A. G.; Nichol, R. C.; Nugent, P.; Papadopoulos, A.; Šako, M.; Smith, M.; Sullivan, M.; Thomas, R. C.; Wester, W. C.; Wolf, R. C.; Abdalla, F. B.; Banerji, M.; Benoit-Lévy, A.; Bertin, E.; Brooks, D.; Rosell, A. Carnero; Castander, F. J.; Costa, L. N. da; Covarrubias, R.; DePoy, D. L.; Desai, S.; Diehl, H. T.; Doel, P.; Eifler, T. F.; Neto, A. Fausti; Finley, D. A.; Flaugher, B.; Fosalba, P.; Frieman, Joshua A.; Gerdes, D. W.; Gruen, D.; Gruendl, R. A.; James, D. J.; Kuêhn, K.; Kuropatkin, N.; Lahav, O.; Li, T. S.; Maia, M. A. G.; Makler, M.; March, M.; Marshall, J. L.; Martini, Paul; Merritt, K. W.; Miquel, R.; Nord, B.; Ogando, R. L. C.; Plazas, A. A.; Romer, A. K.; Roodman, A.; Sánchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarlè, G.; Thaler, J. J.; Walker, A. R.

doi:10.1088/0004-6256/150/3/82

Cited by 138 publications

(128 citation statements)

References 52 publications

(51 reference statements)

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“…2. Criterion 2: the candidate must pass our automated scanning program (Goldstein et al 2015) with a machinelearning score 0.7 in all detections. This criterion rejects non-astrophysical artifacts.…”

Section: Candidate Selectionmentioning

confidence: 99%

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. I. Discovery of the Optical Counterpart Using the Dark Energy Camera

Soares-Santos¹,

Holz²,

Annis³

et al. 2017

ApJL

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We present the Dark Energy Camera (DECam) discovery of the optical counterpart of the first binary neutron star merger detected through gravitational-wave emission, GW170817. Our observations commenced 10.5 hr post-merger, as soon as the localization region became accessible from Chile. We imaged 70 deg 2 in the i and z bands, covering 93% of the initial integrated localization probability, to a depth necessary to identify likely optical counterparts (e.g., a kilonova). At 11.4 hr post-merger we detected a bright optical transient located , in the luminosity range expected for a kilonova. We identified 1500 potential transient candidates. Applying simple selection criteria aimed at rejecting background events such as supernovae, we find the transient associated with NGC 4993 as the only remaining plausible counterpart, and reject chance coincidence at the 99.5% confidence level. We therefore conclude that the optical counterpart we have identified near NGC 4993 is associated with GW170817. This discovery ushers in the era of multi-messenger astronomy with gravitational waves and demonstrates the power of DECam to identify the optical counterparts of gravitationalwave sources.

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Section: Candidate Selectionmentioning

confidence: 99%

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. I. Discovery of the Optical Counterpart Using the Dark Energy Camera

Soares-Santos¹,

Holz²,

Annis³

et al. 2017

ApJL

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489

290

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“…Subtraction artifacts are rejected using a machine-learning technique described in Goldstein et al (2015). This typically yields ∼10 good-quality transient detections on each 9 18 ¢´¢ area covered by a single CCD.…”

Section: Optical Data and Analysismentioning

confidence: 99%

Discovery and Physical Characterization of a Large Scattered Disk Object at 92 au

Gerdes¹,

Šako²,

Hamilton³

et al. 2017

ApJL

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We report the observation and physical characterization of the possible dwarf planet 2014 UZ 224 ("DeeDee"), a dynamically detached trans-Neptunian object discovered at 92 AU. This object is currently the second-most distant known trans-Neptunian object with reported orbital elements, surpassed in distance only by the dwarf planet Eris. The object was discovered with an r-band magnitude of 23.0 in data collected by the Dark Energy Survey between 2014 and 2016. Its 1140year orbit has (a, e, i) = (109 AU, 0.65, 26.8 • ). It will reach its perihelion distance of 38 AU in the year 2142. Integrations of its orbit show it to be dynamically stable on Gyr timescales, with only weak interactions with Neptune. We have performed followup observations with ALMA, using 3 hours of on-source integration time to measure the object's thermal emission in the Rayleigh-Jeans tail. The signal is detected at 7σ significance, from which we determine a V -band albedo of 13.1 +3.3 −2.4 (stat) +2.0 −1.4 (sys) percent and a diameter of 635 +57 −61 (stat) +32 −39 (sys) km, assuming a spherical body with uniform surface properties.

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“…Our goal is to create a binary ML classifier that uses features of the data extracted from the results of the matching algorithm to quantify the probability of a correct host match for every SN. We use a Random Forest (RF; Breiman 2001) classifier since this method is fast, easy to implement, and was successfully used by Goldstein et al (2015) to train a binary classifier to separate artifacts from true transients in DES SN differenced images. RF is also capable of providing probabilities for class membership, which in effect tells us the likelihood that an SN-host-matched pair is correctly matched (i.e., belongs to class "correct match").…”

Section: Improvements Using MLmentioning

confidence: 99%

Host Galaxy Identification for Supernova Surveys

Gupta

Kuhlmann

Kovacs

et al. 2016

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Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of The Astronomical Journal, 152:154 (20pp), 2016 December doi:10.3847/0004-6256/152/6/154 © 2016. The American Astronomical Society. All rights reserved.1 the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey.

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Automated Transient Identification in the Dark Energy Survey

Cited by 138 publications

References 52 publications

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. I. Discovery of the Optical Counterpart Using the Dark Energy Camera

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. I. Discovery of the Optical Counterpart Using the Dark Energy Camera

Discovery and Physical Characterization of a Large Scattered Disk Object at 92 au

Host Galaxy Identification for Supernova Surveys

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