Results from the Supernova Photometric Classification Challenge

被引：132

作者：

Kessler, Richard ^{[1
,2
]}

Bassett, Bruce ^{[4
,5
]}

Belov, Pavel ^{[6
]}

Bhatnagar, Vasudha ^{[7
]}

Campbell, Heather ^{[8
]}

Conley, Alex ^{[9
]}

Frieman, Joshua A. ^{[1
,2
,10
]}

Glazov, Alexandre ^{[6
]}

Gonzalez-Gaitan, Santiago ^{[11
]}

Hlozek, Renee ^{[12
]}

Jha, Saurabh ^{[13
]}

Kuhlmann, Stephen ^{[14
]}

Kunz, Martin ^{[15
]}

Lampeitl, Hubert ^{[8
]}

Mahabal, Ashish ^{[16
]}

Newling, James

Nichol, Robert C. ^{[8
]}

Parkinson, David ^{[17
]}

Philip, Ninan Sajeeth ^{[18
]}

Poznanski, Dovi ^{[19
,20
]}

Richards, Joseph W. ^{[20
,21
]}

Rodney, Steven A. ^{[22
]}

Sako, Masao ^{[23
]}

Schneider, Donald P. ^{[24
]}

Smith, Mathew ^{[3
]}

Stritzinger, Maximilian ^{[25
,26
,27
]}

Varughese, Melvin ^{[28
]}

机构：

[1] Univ Chicago, Dept Astron & Astrophys, Chicago, IL 60637 USA

[2] Univ Chicago, Kavli Inst Cosmol Phys, Chicago, IL 60637 USA

[3] Univ Cape Town, Dept Math & Appl Math, ACGC, ZA-7701 Rondebosch, South Africa

[4] S African Astron Observ, ZA-7935 Cape Town, South Africa

[5] African Inst Math Sci, ZA-7945 Muizenberg, South Africa

[6] Deutsch Elektronensynchrotron DESY, D-22607 Hamburg, Germany

[7] Univ Delhi, Dept Comp Sci, Delhi 110007, India

[8] Univ Portsmouth, Inst Cosmol & Gravitat, Portsmouth PO1 3FX, Hants, England

[9] Univ Colorado, Ctr Astrophys & Space Astron, Boulder, CO 80309 USA

[10] Fermilab Natl Accelerator Lab, Ctr Particle Astrophys, Batavia, IL 60510 USA

[11] Univ Toronto, Dept Astron & Astrophys, Toronto, ON M5S 3H4, Canada

[12] Univ Oxford, Dept Astrophys, Oxford OX1 3RH, England

[13] Rutgers State Univ, Dept Phys & Astron, Piscataway, NJ 08854 USA

[14] Argonne Natl Lab, Lemont, IL 60437 USA

[15] Univ Geneva, Dept Phys Theor, CH-1211 Geneva, Switzerland

[16] CALTECH, Pasadena, CA 91125 USA

[17] Univ Sussex, Ctr Astron, Brighton BN1 9QH, E Sussex, England

[18] St Thomas Coll, Dept Phys, Kozhencheri 689641, Kerala, India

[19] Univ Calif Berkeley, Lawrence Berkeley Lab, Computat Cosmol Ctr, Div Comp Sci, Berkeley, CA 94720 USA

[20] Univ Calif Berkeley, Dept Astron, Berkeley, CA 94720 USA

[21] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA

[22] Johns Hopkins Univ, Dept Phys & Astron, Baltimore, MD 21218 USA

[23] Univ Penn, Dept Phys & Astron, Philadelphia, PA 19104 USA

[24] Penn State Univ, Dept Astron & Astrophys, University Pk, PA 16802 USA

[25] Las Campanas Observ, Carnegie Observ, La Serena, Chile

[26] Univ Copenhagen, Dark Cosmol Ctr, Niels Bohr Inst, DK-2100 Copenhagen O, Denmark

[27] Stockholm Univ, Dept Astron, Oskar Klein Ctr, S-10691 Stockholm, Sweden

[28] Univ Cape Town, Dept Stat Sci, ZA-7701 Rondebosch, South Africa

来源：

PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC | 2010年 / 122卷 / 898期

基金：

美国国家科学基金会; 美国国家航空航天局;

关键词：

DIGITAL SKY SURVEY; HUBBLE-SPACE-TELESCOPE; IA SUPERNOVAE; OBSERVATIONAL CONSTRAINTS; DARK ENERGY; DIAGRAM; SAMPLE;

D O I：

10.1086/657607

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

We report results from the Supernova Photometric Classification Challenge (SNPhotCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rates. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function, and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia-type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN and nine entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe Ia has an efficiency of 0.96 and an SN Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPhotCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS-II, and provided the answer keys so that developers can evaluate their own analysis.

引用

页码：1415 / 1431

页数：17

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