The ZTF source classification project. I. Methods and infrastructure

Jan van Roestel; Dmitry A. Duev; Ashish A. Mahabal; Michael W. Coughlin; Przemek Mróz; Kevin Burdge; Andrew Drake; Matthew J. Graham; Lynne Hillenbrand; Eric C. Bellm; Thomas Kupfer; Alexandre Delacroix; C. Fremling; V. Zach Golkhou; David Hale; Russ R. Laher; Frank J. Masci; Reed Riddle; Philippe Rosnet; Ben Rusholme; Roger Smith; Maayane T. Soumagnac; Richard Walters; Thomas A. Prince; S. R. Kulkarni

doi:10.3847/1538-3881/abe853

The ZTF source classification project. I. Methods and infrastructure

Jan van Roestel, Dmitry A. Duev, Ashish A. Mahabal, Michael W. Coughlin, Przemek Mróz, Kevin Burdge, Andrew Drake, Matthew J. Graham, Lynne Hillenbrand, Eric C. Bellm, Thomas Kupfer, Alexandre Delacroix, C. Fremling, V. Zach Golkhou, David Hale, Russ R. Laher, Frank J. Masci, Reed Riddle, Philippe Rosnet, Ben RusholmeRoger Smith, Maayane T. Soumagnac, Richard Walters, Thomas A. Prince, S. R. Kulkarni

Department of Particle Physics and Astrophysics

Weizmann Institute of Science

Research output: Contribution to journal › Article › peer-review

Abstract

The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 (5σ for 30 s exposure) in the g, r, and i filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in g and r, and fewer in i. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data become available, training samples are updated, and new classes need to be incorporated.

Original language	English
Article number	267
Journal	Astronomical Journal
Volume	161
Issue number	6
DOIs	https://doi.org/10.3847/1538-3881/abe853
State	Published - Jun 2021

All Science Journal Classification (ASJC) codes

Astronomy and Astrophysics
Space and Planetary Science

Access to Document

10.3847/1538-3881/abe853

https://resolver.caltech.edu/CaltechAUTHORS:20210505-070438281License: CC BY

Cite this

van Roestel, J., Duev, D. A., Mahabal, A. A., Coughlin, M. W., Mróz, P., Burdge, K., Drake, A., Graham, M. J., Hillenbrand, L., Bellm, E. C., Kupfer, T., Delacroix, A., Fremling, C., Zach Golkhou, V., Hale, D., Laher, R. R., Masci, F. J., Riddle, R., Rosnet, P., ... Kulkarni, S. R. (2021). The ZTF source classification project. I. Methods and infrastructure. Astronomical Journal, 161(6), Article 267. https://doi.org/10.3847/1538-3881/abe853

@article{7d2191c24875476eae597d395f305be1,

title = "The ZTF source classification project. I. Methods and infrastructure",

abstract = "The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 (5σ for 30 s exposure) in the g, r, and i filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in g and r, and fewer in i. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data become available, training samples are updated, and new classes need to be incorporated.",

author = "\{van Roestel\}, Jan and Duev, \{Dmitry A.\} and Mahabal, \{Ashish A.\} and Coughlin, \{Michael W.\} and Przemek Mr{\'o}z and Kevin Burdge and Andrew Drake and Graham, \{Matthew J.\} and Lynne Hillenbrand and Bellm, \{Eric C.\} and Thomas Kupfer and Alexandre Delacroix and C. Fremling and \{Zach Golkhou\}, V. and David Hale and Laher, \{Russ R.\} and Masci, \{Frank J.\} and Reed Riddle and Philippe Rosnet and Ben Rusholme and Roger Smith and Soumagnac, \{Maayane T.\} and Richard Walters and Prince, \{Thomas A.\} and Kulkarni, \{S. R.\}",

year = "2021",

month = jun,

doi = "10.3847/1538-3881/abe853",

language = "الإنجليزيّة",

volume = "161",

journal = "Astronomical Journal",

issn = "0004-6256",

publisher = "American Astronomical Society",

number = "6",

}

van Roestel, J, Duev, DA, Mahabal, AA, Coughlin, MW, Mróz, P, Burdge, K, Drake, A, Graham, MJ, Hillenbrand, L, Bellm, EC, Kupfer, T, Delacroix, A, Fremling, C, Zach Golkhou, V, Hale, D, Laher, RR, Masci, FJ, Riddle, R, Rosnet, P, Rusholme, B, Smith, R, Soumagnac, MT, Walters, R, Prince, TA & Kulkarni, SR 2021, 'The ZTF source classification project. I. Methods and infrastructure', Astronomical Journal, vol. 161, no. 6, 267. https://doi.org/10.3847/1538-3881/abe853

TY - JOUR

T1 - The ZTF source classification project. I. Methods and infrastructure

AU - van Roestel, Jan

AU - Duev, Dmitry A.

AU - Mahabal, Ashish A.

AU - Coughlin, Michael W.

AU - Mróz, Przemek

AU - Burdge, Kevin

AU - Drake, Andrew

AU - Graham, Matthew J.

AU - Hillenbrand, Lynne

AU - Bellm, Eric C.

AU - Kupfer, Thomas

AU - Delacroix, Alexandre

AU - Fremling, C.

AU - Zach Golkhou, V.

AU - Hale, David

AU - Laher, Russ R.

AU - Masci, Frank J.

AU - Riddle, Reed

AU - Rosnet, Philippe

AU - Rusholme, Ben

AU - Smith, Roger

AU - Soumagnac, Maayane T.

AU - Walters, Richard

AU - Prince, Thomas A.

AU - Kulkarni, S. R.

PY - 2021/6

Y1 - 2021/6

N2 - The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 (5σ for 30 s exposure) in the g, r, and i filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in g and r, and fewer in i. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data become available, training samples are updated, and new classes need to be incorporated.

AB - The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 (5σ for 30 s exposure) in the g, r, and i filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in g and r, and fewer in i. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data become available, training samples are updated, and new classes need to be incorporated.

UR - http://www.scopus.com/inward/record.url?scp=85106611816&partnerID=8YFLogxK

U2 - 10.3847/1538-3881/abe853

DO - 10.3847/1538-3881/abe853

M3 - مقالة

SN - 0004-6256

VL - 161

JO - Astronomical Journal

JF - Astronomical Journal

IS - 6

M1 - 267

ER -

The ZTF source classification project. I. Methods and infrastructure

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this