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Record W4413694310 · doi:10.1088/1475-7516/2025/08/075

Searching for dark matter interactions with ACT, SPT and DES

2025· article· en· W4413694310 on OpenAlexaff
Zilu Zhou, Neal Weiner

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsPhysicsDark matterDark energyAstrophysicsParticle physicsAstronomyCosmology

Abstract

fetched live from OpenAlex

Abstract Models of a dark radiation sector with a mass threshold (WZDR+) have proved to be an appealing alternative to ΛCDM. These models provide simple comparison models, grounded in well-understood particle physics and with limited additional parameters. In addition, they have shown relevance in easing existing cosmological tensions, specifically the H 0 tension and the S 8 tension. Recently, measurements of CMB lensing by the ACT collaboration have provided strong additional information on clustering at late times. Within ΛCDM, these results yield a high value of S 8 at odds with weak-lensing measurements. In this work, we study this in the context of WZDR+, and find a much wider range of allowed values of S 8 , and in particular much better agreement between data sets and an overall improvement of fit versus ΛCDM. We expand our analyses to include a wide set of data, including the ACT-DR6 lensing data, as well as primary CMB information from ACT-DR4 and SPT-3G, scale-dependent power spectra from DES and measurements of H 0 from SH0ES. We find that there is little to no tension in measurements of structure within the data sets, and the inferred value of S 8 is generally lower than that in ΛCDM. We find that the inclusion of DES generally favors a higher H 0 , but there is some direct tension between the high-ℓ multipole data and this result. Future data should clarify whether this is a statistical artifact, or a true incompatibility of these datasets within this model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.291
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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