SN 2023adsy: A normal type Ia Supernova at

Context. Supernovae (SNe) discovered in high-redshift (z > 2) galaxies by the James Webb Space Telescope (JWST) provide a unique opportunity to extend the Hubble diagram beyond z ∼ 1.5 and constrain the cosmic star formation rate in the early Universe. Aims. SN 2023adsy, a type Ia supernova discovered by JWST at z = 2.9, was found to be a peculiar event, since it is extremely red and faint, but it shows a very similar rest-frame light-curve decline rate to the majority of low-redshift SNe Ia. We examine whether the red color and faint peak magnitude could also be explained by significant reddening and extinction due to dust within the host galaxy. Methods. We used two light-curve fitting codes, SALT3-NIR and BayeSN, which have templates extended to the near-infrared, to re-fit the published NIRCam photometry, assuming a “normal” SN Ia and Milky-Way dust extinction law. We also examined NIRCam photometry of the host galaxy taken before the SN discovery by comparing its spectral energy distribution with galaxy templates. Results. The NIRCam photometry can be fit reasonably well with a slowly declining but otherwise “normal” SN Ia template that suffers significant reddening (E(B − V)host ≳ 0.5 mag). Photometry of the host galaxy suggests a blue, star-forming galaxy, where the presence of a significant amount of dust cannot be ruled out. Conclusions. A comparison of the inferred luminosity distance with the prediction of the ΛCDM cosmology on the Hubble diagram suggests no significant evolution of the SN Ia peak luminosity at z > 2 redshifts. We also show that the discovery of a single SN Ia between 2 < z < 3 within the area of the JADES survey during approximately one year is consistent with the current estimates for the SN Ia rates at such redshifts.

Publication Details

Published
2025-09-04
DOI
https://doi.org/10.1051/0004-6361/202554209/pdf
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SN 2023adsy: A normal type Ia Supernova at

Geochemistry and Geologic Mapping
article

SN 2023adsy: A normal type Ia Supernova at

article en

Abstract

Context. Supernovae (SNe) discovered in high-redshift (z > 2) galaxies by the James Webb Space Telescope (JWST) provide a unique opportunity to extend the Hubble diagram beyond z ∼ 1.5 and constrain the cosmic star formation rate in the early Universe. Aims. SN 2023adsy, a type Ia supernova discovered by JWST at z = 2.9, was found to be a peculiar event, since it is extremely red and faint, but it shows a very similar rest-frame light-curve decline rate to the majority of low-redshift SNe Ia. We examine whether the red color and faint peak magnitude could also be explained by significant reddening and extinction due to dust within the host galaxy. Methods. We used two light-curve fitting codes, SALT3-NIR and BayeSN, which have templates extended to the near-infrared, to re-fit the published NIRCam photometry, assuming a “normal” SN Ia and Milky-Way dust extinction law. We also examined NIRCam photometry of the host galaxy taken before the SN discovery by comparing its spectral energy distribution with galaxy templates. Results. The NIRCam photometry can be fit reasonably well with a slowly declining but otherwise “normal” SN Ia template that suffers significant reddening (E(B − V)host ≳ 0.5 mag). Photometry of the host galaxy suggests a blue, star-forming galaxy, where the presence of a significant amount of dust cannot be ruled out. Conclusions. A comparison of the inferred luminosity distance with the prediction of the ΛCDM cosmology on the Hubble diagram suggests no significant evolution of the SN Ia peak luminosity at z > 2 redshifts. We also show that the discovery of a single SN Ia between 2 < z < 3 within the area of the JADES survey during approximately one year is consistent with the current estimates for the SN Ia rates at such redshifts.

Openalex Percentile: Top 33%
Geochemistry and Geologic Mapping
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.