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A transient search using combined human and machine classifications

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A transient search using combined human and machine classifications. / Wright, Darryl E.; Lintott, Chris J.; Smartt, Stephen J. et al.
In: Monthly Notices of the Royal Astronomical Society, Vol. 472, No. 2, 01.12.2017, p. 1315-1323.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wright, DE, Lintott, CJ, Smartt, SJ, Smith, KW, Fortson, L, Trouille, L, Allen, CR, Beck, M, Bouslog, MC, Boyer, A, Chambers, KC, Flewelling, H, Granger, W, Magnier, EA, Mcmaster, A, Miller, GRM, O'donnell, JE, Simmons, B, Spiers, H, Tonry, JL, Veldthuis, M, Wainscoat, RJ, Waters, C, Willman, M, Wolfenbarger, Z & Young, DR 2017, 'A transient search using combined human and machine classifications', Monthly Notices of the Royal Astronomical Society, vol. 472, no. 2, pp. 1315-1323. https://doi.org/10.1093/mnras/stx1812

APA

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., Chambers, K. C., Flewelling, H., Granger, W., Magnier, E. A., Mcmaster, A., Miller, G. R. M., O'donnell, J. E., Simmons, B., Spiers, H., ... Young, D. R. (2017). A transient search using combined human and machine classifications. Monthly Notices of the Royal Astronomical Society, 472(2), 1315-1323. https://doi.org/10.1093/mnras/stx1812

Vancouver

Wright DE, Lintott CJ, Smartt SJ, Smith KW, Fortson L, Trouille L et al. A transient search using combined human and machine classifications. Monthly Notices of the Royal Astronomical Society. 2017 Dec 1;472(2):1315-1323. doi: 10.1093/mnras/stx1812

Author

Wright, Darryl E. ; Lintott, Chris J. ; Smartt, Stephen J. et al. / A transient search using combined human and machine classifications. In: Monthly Notices of the Royal Astronomical Society. 2017 ; Vol. 472, No. 2. pp. 1315-1323.

Bibtex

@article{2f8be962dffd4224b64389b5c6487914,
title = "A transient search using combined human and machine classifications",
abstract = "Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys.",
author = "Wright, {Darryl E.} and Lintott, {Chris J.} and Smartt, {Stephen J.} and Smith, {Ken W.} and Lucy Fortson and Laura Trouille and Allen, {Campbell R.} and Melanie Beck and Bouslog, {Mark C.} and Amy Boyer and Chambers, {K. C.} and Heather Flewelling and Will Granger and Magnier, {Eugene A.} and Adam Mcmaster and Miller, {Grant R. M.} and O'donnell, {James E.} and Brooke Simmons and Helen Spiers and Tonry, {John L.} and Marten Veldthuis and Wainscoat, {Richard J.} and Chris Waters and Mark Willman and Zach Wolfenbarger and Young, {Dave R.}",
year = "2017",
month = dec,
day = "1",
doi = "10.1093/mnras/stx1812",
language = "English",
volume = "472",
pages = "1315--1323",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "OXFORD UNIV PRESS",
number = "2",

}

RIS

TY - JOUR

T1 - A transient search using combined human and machine classifications

AU - Wright, Darryl E.

AU - Lintott, Chris J.

AU - Smartt, Stephen J.

AU - Smith, Ken W.

AU - Fortson, Lucy

AU - Trouille, Laura

AU - Allen, Campbell R.

AU - Beck, Melanie

AU - Bouslog, Mark C.

AU - Boyer, Amy

AU - Chambers, K. C.

AU - Flewelling, Heather

AU - Granger, Will

AU - Magnier, Eugene A.

AU - Mcmaster, Adam

AU - Miller, Grant R. M.

AU - O'donnell, James E.

AU - Simmons, Brooke

AU - Spiers, Helen

AU - Tonry, John L.

AU - Veldthuis, Marten

AU - Wainscoat, Richard J.

AU - Waters, Chris

AU - Willman, Mark

AU - Wolfenbarger, Zach

AU - Young, Dave R.

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys.

AB - Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys.

U2 - 10.1093/mnras/stx1812

DO - 10.1093/mnras/stx1812

M3 - Journal article

VL - 472

SP - 1315

EP - 1323

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 2

ER -