Pith. sign in

REVIEW 5 cited by

The Dark Energy Survey Supernova Program: Light curves and 5-Year data release

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2406.05046 v1 pith:3A26MHJU submitted 2024-06-07 astro-ph.CO

The Dark Energy Survey Supernova Program: Light curves and 5-Year data release

classification astro-ph.CO
keywords photometrysurveycurveslightdiffimgreleasesamplesingle
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
abstract

We present $griz$ photometric light curves for the full 5 years of the Dark Energy Survey Supernova program (DES-SN), obtained with both forced Point Spread Function (PSF) photometry on Difference Images (DIFFIMG) performed during survey operations, and Scene Modelling Photometry (SMP) on search images processed after the survey. This release contains $31,636$ DIFFIMG and $19,706$ high-quality SMP light curves, the latter of which contains $1635$ photometrically-classified supernovae that pass cosmology quality cuts. This sample spans the largest redshift ($z$) range ever covered by a single SN survey ($0.1<z<1.13$) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the DES-SN3YR spectroscopically-confirmed SN Ia sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-time DIFFIMG forced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found at https://github.com/des-science/DES-SN5YR. Finally, we discuss implications for future transient surveys, such as the forthcoming Vera Rubin Observatory Legacy Survey of Space and Time (LSST).

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations

    astro-ph.CO 2026-07 conditional novelty 6.0

    A simulation-based inference pipeline (Stjörnumál) fits SN Ia dust and intrinsic scatter models to DES 5-year data, enabling fast Bayesian model comparison across seven SN Ia population models.

  2. Constraints on Dynamical Dark Energy from Multiple Probes in the Full Dark Energy Survey

    astro-ph.CO 2026-05 unverdicted novelty 6.0

    Full DES data from SN+BAO+3x2pt yields w0=-0.84, wa=-0.44 with 2.2σ deviation from ΛCDM; adding DESI+CMB reaches 3.0σ while 3x2pt improves figure of merit by ~10%.

  3. Probing Dark Energy Microphysics with kSZ Tomography

    astro-ph.CO 2025-11 conditional novelty 5.0

    Fisher-matrix forecasts for LSST- and CMB-S4-like surveys show kSZ tomography tightens constraints on dark energy parameters w0 and wa by 15% and 32% while assessing detectability of perturbations for different sound speeds.

  4. New Insights into Dark Energy from DESI DR2 with CMB and SNIa

    astro-ph.CO 2025-07 unverdicted novelty 4.0

    Systematic dataset swaps show DESY5 low-redshift SNIa, Planck CMB plus lensing, and DESI-DR2 BAO as the dominant sources of w0waCDM tension with ΛCDM, while other combinations remain consistent.

  5. Cosmic Strings as Dynamical Dark Energy: Novel Constraints

    astro-ph.CO 2025-05 conditional novelty 4.0

    Cosmic string networks are constrained to less than ~1% of the energy density using CMB+BAO+SN data, with some models preferring mildly negative densities but no Bayesian evidence favoring them over LambdaCDM.