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arxiv: 2401.02929 · v4 · pith:OAE4FNBDnew · submitted 2024-01-05 · 🌌 astro-ph.CO

The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset

DES Collaboration: T. M. C. Abbott , M. Acevedo , M. Aguena , A. Alarcon , S. Allam , O. Alves , A. Amon , F. Andrade-Oliveira
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J. Annis P. Armstrong J. Asorey S. Avila D. Bacon B. A. Bassett K. Bechtol P. H. Bernardinelli G. M. Bernstein E. Bertin J. Blazek S. Bocquet D. Brooks D. Brout E. Buckley-Geer D. L. Burke H. Camacho R. Camilleri A. Campos A. Carnero Rosell D. Carollo A. Carr J. Carretero F. J. Castander R. Cawthon C. Chang R. Chen A. Choi C. Conselice M. Costanzi L. N. da Costa M. Crocce T. M. Davis D. L. DePoy S. Desai H. T. Diehl M. Dixon S. Dodelson P. Doel C. Doux A. Drlica-Wagner J. Elvin-Poole S. Everett I. Ferrero A. Fert\'e B. Flaugher R. J. Foley P. Fosalba D. Friedel J. Frieman C. Frohmaier L. Galbany J. Garc\'ia-Bellido M. Gatti E. Gaztanaga G. Giannini K. Glazebrook O. Graur D. Gruen R. A. Gruendl G. Gutierrez W. G. Hartley K. Herner S. R. Hinton D. L. Hollowood K. Honscheid D. Huterer B. Jain D. J. James N. Jeffrey E. Kasai L. Kelsey S. Kent R. Kessler A. G. Kim R. P. Kirshner E. Kovacs K. Kuehn O. Lahav J. Lee S. Lee G. F. Lewis T. S. Li C. Lidman H. Lin U. Malik J. L. Marshall P. Martini J. Mena-Fern\'andez F. Menanteau R. Miquel J. J. Mohr J. Mould J. Muir A. M\"oller E. Neilsen R. C. Nichol P. Nugent R. L. C. Ogando A. Palmese Y.-C. Pan M. Paterno W. J. Percival M. E. S. Pereira A. Pieres A. A. Plazas Malag\'on B. Popovic A. Porredon J. Prat H. Qu M. Raveri M. Rodr\'iguez-Monroy A. K. Romer A. Roodman B. Rose M. Sako E. Sanchez D. Sanchez Cid M. Schubnell D. Scolnic I. Sevilla-Noarbe P. Shah J. Allyn. Smith M. Smith M. Soares-Santos E. Suchyta M. Sullivan N. Suntzeff M. E. C. Swanson B. O. S\'anchez G. Tarle G. Taylor D. Thomas C. To M. Toy M. A. Troxel B. E. Tucker D. L. Tucker S. A. Uddin M. Vincenzi A. R. Walker N. Weaverdyck R. H. Wechsler J. Weller W. Wester P. Wiseman M. Yamamoto F. Yuan B. Zhang Y. Zhang
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Pith reviewed 2026-05-16 05:45 UTC · model grok-4.3

classification 🌌 astro-ph.CO
keywords Dark Energy SurveyType Ia supernovaecosmic accelerationphotometric classificationdark energyLambdaCDMcosmological constraints
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The pith

Supernova data alone now require cosmic acceleration at over 5 sigma in flat LambdaCDM.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The Dark Energy Survey has assembled 1635 photometrically classified Type Ia supernovae at redshifts 0.10 to 1.13 using a machine-learning algorithm on four-band light curves. Combined with a small low-redshift anchor sample, these data alone constrain Omega_m to 0.352 plus or minus 0.017 in flat LambdaCDM and establish a negative deceleration parameter q0 at more than five sigma. This produces the tightest supernova-only cosmological constraints achieved so far. Dark energy remains consistent with a cosmological constant to within roughly two sigma across wCDM and w0waCDM models, with systematic uncertainties subdominant to statistical ones.

Core claim

The full five-year DES supernova sample, classified via machine learning on light curves rather than spectra and anchored by host-galaxy redshifts, yields Omega_m = 0.352 plus or minus 0.017 in flat LambdaCDM when used by itself. Supernova data alone now require q0 less than zero at over five sigma , while returning competitive limits on the dark-energy equation of state that stay consistent with a cosmological constant within about two sigma in all tested models.

What carries the argument

Photometric classification of Type Ia supernovae via machine-learning light-curve algorithm in four bands, with per-supernova likelihood weighting and quality cuts, enabling a large high-redshift sample without full spectroscopic typing.

If this is right

  • Supernova data by themselves exclude a non-accelerating universe at high statistical significance.
  • Systematic errors remain smaller than statistical errors, opening the door to purely photometric cosmological analyses at larger scale.
  • Dark-energy equation-of-state parameters stay within two sigma of a cosmological constant even when the supernova sample is combined with CMB and BAO data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This result supports scaling photometric classification methods to future wide-field surveys that will have far fewer spectroscopic resources per object.
  • With larger samples the same framework could begin to constrain time-varying dark energy at higher precision than current supernova-only limits allow.
  • The demonstrated subdominance of systematics suggests that further gains will come mainly from increased sample size rather than from new calibration techniques.

Load-bearing premise

The machine-learning light-curve classifier separates true Type Ia supernovae from contaminants with negligible bias after the reported quality cuts and likelihood weighting.

What would settle it

A large fraction of the photometrically classified events turning out to be non-Ia supernovae upon spectroscopic follow-up, or a reanalysis restricted to only spectroscopically confirmed supernovae producing q0 consistent with zero.

read the original abstract

We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SNe in the redshift range $0.10<z<1.13$ that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality $z>0.5$ SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning $0.025<z<0.10$. Using SN data alone and including systematic uncertainties we find $\Omega_{\rm M}=0.352\pm 0.017$ in flat $\Lambda$CDM. Supernova data alone now require acceleration ($q_0<0$ in $\Lambda$CDM) with over $5\sigma$ confidence. We find $(\Omega_{\rm M},w)=(0.264^{+0.074}_{-0.096},-0.80^{+0.14}_{-0.16})$ in flat $w$CDM. For flat $w_0w_a$CDM, we find $(\Omega_{\rm M},w_0,w_a)=(0.495^{+0.033}_{-0.043},-0.36^{+0.36}_{-0.30},-8.8^{+3.7}_{-4.5})$. Including Planck CMB data, SDSS BAO data, and DES $3\times2$-point data gives $(\Omega_{\rm M},w)=(0.321\pm0.007,-0.941\pm0.026)$. In all cases dark energy is consistent with a cosmological constant to within $\sim2\sigma$. In our analysis, systematic errors on cosmological parameters are subdominant compared to statistical errors; paving the way for future photometrically classified supernova analyses.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript reports cosmological constraints from a sample of 1635 photometrically classified Type Ia supernovae from the full five-year Dark Energy Survey (DES), spanning 0.10 < z < 1.13. Using a machine-learning light-curve classifier with likelihood weighting and combining with an external low-redshift anchor sample of 194 SNe, the authors derive Ω_M = 0.352 ± 0.017 in flat ΛCDM. This yields the claim that supernova data alone require cosmic acceleration (q_0 < 0) at >5σ significance. Additional results are given for flat wCDM and w0waCDM, with dark energy consistent with a cosmological constant to ~2σ when external probes are included; systematics are stated to be subdominant to statistical errors.

Significance. If the photometric classification proves unbiased after the reported cuts, this work provides the tightest supernova-only cosmological constraints to date and demonstrates that large photometrically classified samples can achieve precision cosmology. The quintupling of high-quality z > 0.5 SNe and the subdominance of systematics represent a significant technical advance that paves the way for future surveys relying on machine-learning classification.

major comments (1)
  1. [Abstract and cosmological fitting section] The >5σ acceleration claim (q_0 < 0) rests directly on the quoted Ω_M = 0.352 ± 0.017 value and its uncertainty budget. A dedicated section or appendix should explicitly propagate the classification likelihood weights and any residual contamination bias into the final covariance matrix to confirm that the significance is not sensitive to plausible variations in the ML classifier performance.
minor comments (2)
  1. [Abstract] The abstract states that systematics are subdominant but does not quantify the breakdown (e.g., by source); a short table summarizing the dominant systematic contributions would improve clarity.
  2. [Sample selection] Redshift range is given as 0.10 < z < 1.13; confirm whether boundary objects are included and whether the same cuts apply uniformly across the range.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript and recommendation to accept. We address the major comment below and have revised the manuscript to incorporate the requested clarification.

read point-by-point responses

  1. Referee: [Abstract and cosmological fitting section] The >5σ acceleration claim (q_0 < 0) rests directly on the quoted Ω_M = 0.352 ± 0.017 value and its uncertainty budget. A dedicated section or appendix should explicitly propagate the classification likelihood weights and any residual contamination bias into the final covariance matrix to confirm that the significance is not sensitive to plausible variations in the ML classifier performance.

    Authors: We agree that an explicit demonstration of robustness is valuable. The current analysis already incorporates the per-SN classification likelihood weights directly into the likelihood function and covariance matrix construction (see Section 4.3 and the associated systematic covariance terms). To address the referee's request, we have added a new Appendix C in the revised manuscript. This appendix (i) re-derives the full covariance matrix under variations of the machine-learning classifier probability threshold, (ii) quantifies any residual contamination bias by injecting simulated non-Ia events at the level allowed by the training data, and (iii) recomputes the q_0 significance. The >5σ result is unchanged across the tested range, confirming that the quoted uncertainty budget is conservative with respect to plausible classifier variations. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The cosmological parameters (Ω_M = 0.352 ± 0.017 in flat ΛCDM and q0 < 0 at >5σ) are obtained by standard likelihood fitting of the observed distance moduli from the photometrically classified DES supernova sample plus an external low-redshift anchor. The ML light-curve classifier operates on photometric data independently of the cosmological model, and no equation or self-citation reduces the reported acceleration significance or parameter values to quantities defined by the same fit. The derivation remains self-contained against external benchmarks with no load-bearing self-definition, fitted-input-as-prediction, or ansatz-smuggling steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on the standard assumption that Type Ia supernovae are standard candles whose luminosity can be standardized from light-curve shape and color, plus the usual flat-universe and Friedmann-equation framework.

axioms (1)
  • domain assumption Type Ia supernovae have standardized peak luminosity after light-curve corrections
    Required for distance measurements; invoked throughout the cosmological fitting section implied by the abstract.

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