The Dark Energy Survey 5-yr photometrically classified type Ia supernovae without host-galaxy redshifts

ABSTRACT Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new approaches to classifying SNe and obtaining their redshifts without spectra if they wish to reach their full potential. We present here a novel approach that uses only photometry to identify SNe Ia in the 5-yr Dark Energy Survey (DES) data set using the SuperNNova classifier. Our approach, which does not rely on any information from the SN host-galaxy, recovers SNe Ia that might otherwise be lost due to a lack of an identifiable host. We select $2{,}298$ high-quality SNe Ia from the DES 5-yr data set an almost complete sample of detected SNe Ia. More than 700 of these have no spectroscopic host redshift and are potentially new SNIa compared to the DES-SN5YR cosmology analysis. To analyse these SNe Ia, we derive their redshifts and properties using only their light curves with a modified version of the SALT2 light-curve fitter. Compared to other DES SN Ia samples with spectroscopic redshifts, our new sample has in average higher redshift, bluer and broader light curves, and fainter host-galaxies. Future surveys such as LSST will also face an additional challenge, the scarcity of spectroscopic resources for follow-up. When applying our novel method to DES data, we reduce the need for follow-up by a factor of four and three for host-galaxy and live SN, respectively, compared to earlier approaches. Our novel method thus leads to better optimization of spectroscopic resources for follow-up.

Authors

Institutions

Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2024-08-13
DOI
https://doi.org/10.1093/mnras/stae1953
Citations
15
Primary Topic
Gamma-ray bursts and supernovae
Type
article
Field-Weighted Citation Impact
4.97

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Dark Energy Survey 5-yr photometrically classified type Ia supernovae without host-galaxy redshifts

A. Pieres, A. Roodman, B. Sánchez, O. Lahav et al.
15 citations
Monthly Notices of the Royal Astronomical Society
Gamma-ray bursts and supernovae
4.97
article

The Dark Energy Survey 5-yr photometrically classified type Ia supernovae without host-galaxy redshifts

A. Pieres, A. Roodman, B. Sánchez, O. Lahav, I. Ferrero, S. R. Hinton, J. Myles, D. Brooks, A. Möller, L. Galbany, J. García-Bellido, J. L. Marshall, L. N. da Costa, M. R. Smith, G. Giannini, F. J. Castander, Gregory Tarlé, E. Suchyta, A. Palmese, Brad E. Tucker, O. Alves, P. Wiseman, C. Tucker, N. Weaverdyck, D. J. James, Gaston Gutierrez, T. M. C. Abbott, Eusebio Sanchez, J. Mena-Fernández, M. Šako, A. Carnero Rosell, ENRIQUE GAZTAÑAGA, K. Honscheid, K. Kuehn, R. A. Gruendl, F. Menanteau, M. Vincenzi, S. Lee, T. M. Davis, R. L. C. Ogando, M. E. C. Swanson, R. Miquel, M. E. S. Pereira, Christopher E. Lidman, H. T. Diehl, F. Andrade-Oliveira, M. Aguena, D. N. Friedel, I. Sevilla-Noarbe, E. Bertin, M. Sullivan, R. C. Nichol, A A Plazas Malagón, D L Hollowood, S Desai, D. Sanchez Cid, S Allam, A.R Walker, D. Bacon, R Kessler, S Everett, J Lee
article en
15 citations

Abstract

ABSTRACT Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new approaches to classifying SNe and obtaining their redshifts without spectra if they wish to reach their full potential. We present here a novel approach that uses only photometry to identify SNe Ia in the 5-yr Dark Energy Survey (DES) data set using the SuperNNova classifier. Our approach, which does not rely on any information from the SN host-galaxy, recovers SNe Ia that might otherwise be lost due to a lack of an identifiable host. We select $2{,}298$ high-quality SNe Ia from the DES 5-yr data set an almost complete sample of detected SNe Ia. More than 700 of these have no spectroscopic host redshift and are potentially new SNIa compared to the DES-SN5YR cosmology analysis. To analyse these SNe Ia, we derive their redshifts and properties using only their light curves with a modified version of the SALT2 light-curve fitter. Compared to other DES SN Ia samples with spectroscopic redshifts, our new sample has in average higher redshift, bluer and broader light curves, and fainter host-galaxies. Future surveys such as LSST will also face an additional challenge, the scarcity of spectroscopic resources for follow-up. When applying our novel method to DES data, we reduce the need for follow-up by a factor of four and three for host-galaxy and live SN, respectively, compared to earlier approaches. Our novel method thus leads to better optimization of spectroscopic resources for follow-up.

Monthly Notices of the Royal Astronomical SocietyVol. 533(2)
Australian National University (AU), Institució Catalana de Recerca i Estudis Avançats (ES), Oak Ridge National Laboratory (US), Centre National de la Recherche Scientifique (FR), Fermi National Accelerator Laboratory (US), Institute of Space Sciences (ES), Jet Propulsion Laboratory (US), Lawrence Berkeley National Laboratory (US), University of Illinois Urbana-Champaign (US), Universidad de La Laguna (ES), Universität Hamburg (DE), The University of Queensland (AU), Duke University (US), University of Oslo (NO), Institute for High Energy Physics (ES), Princeton University (US), Aix-Marseille Université (FR), University of Michigan (US), Lowell Observatory (US), Mitchell Institute (US), Institut d'Astrophysique de Paris (FR), SLAC National Accelerator Laboratory (US), Australian Astronomical Observatory (AU), University of Surrey (GB), Sorbonne Université (FR), University of Chicago (US), Valongo Observatory (BR), Institut d'Estudis Espacials de Catalunya (ES), Kavli Institute for Particle Astrophysics and Cosmology (US), Center for Astrophysics Harvard & Smithsonian (US), National Observatory (BR), ARC Centre of Excellence for Gravitational Wave Discovery (AU), Instituto de Astrofísica de Canarias (ES), National Center for Supercomputing Applications (US), Cerro Tololo Inter-American Observatory (CL), Laboratoire de Physique Subatomique et de Cosmologie (FR), Laboratório Interinstitucional de e-Astronomia (BR), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), University of Southampton (GB), University College London (GB), The Ohio State University (US), Swinburne University of Technology (AU), University of Portsmouth (GB), Universidad Autónoma de Madrid (ES), Indian Institute of Technology Hyderabad (IN), Lancaster University (GB), Carnegie Mellon University (US), University of Pennsylvania (US), Texas A&M University (US), Stanford University (US), Macquarie University (AU)
National Science Foundation, U.S. Department of Energy, University of Pennsylvania, Ohio State University, University of Chicago, University of Portsmouth, National Centre for Supercomputing Applications, Centres de Recerca de Catalunya, University College London, European Commission, University of Sussex, Deutsche Forschungsgemeinschaft, Generalitat de Catalunya, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, Ministerio de Ciencia e Innovación, Institut de Física d'Altes Energies, University of Illinois at Urbana-Champaign, Science and Technology Facilities Council, European Regional Development Fund, Agencia Estatal de Investigación, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab, Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory
Climate action
Openalex Percentile: Top 5%
Gamma-ray bursts and supernovae
4.97
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.