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Heavy Thermal Dark Matter from a New Collision Mechanism

2021· article· en· W3132089181 on OpenAlexfundno aff
Eric David Kramer, Eric Kuflik, Noam Levi, Nadav Joseph Outmezguine, Joshua T. Ruderman

Bibliographic record

VenuePhysical Review Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersMilner FoundationIsraeli Centers for Research ExcellenceAzrieli FoundationIsrael Science FoundationAspen Center for PhysicsUnited States-Israel Binational Science FoundationNational Science Foundation
KeywordsPhysicsDark matterParticle physicsUnitarityCosmologyDark fluidStandard Model (mathematical formulation)AstrophysicsDark energy

Abstract

fetched live from OpenAlex

We propose a new thermal freeze-out mechanism that results in dark matter masses exceeding the unitarity bound by many orders of magnitude, without violating perturbative unitarity or modifying the standard cosmology. The process determining the relic abundance is $\ensuremath{\chi}{\ensuremath{\zeta}}^{\ifmmode\dagger\else\textdagger\fi{}}\ensuremath{\rightarrow}\ensuremath{\zeta}\ensuremath{\zeta}$, where $\ensuremath{\chi}$ is the dark matter candidate. For ${m}_{\ensuremath{\zeta}}<{m}_{\ensuremath{\chi}}<3{m}_{\ensuremath{\zeta}}$, $\ensuremath{\chi}$ is cosmologically long-lived and scatters against the exponentially more abundant $\ensuremath{\zeta}$. Therefore, such a process allows for exponentially heavier dark matter for the same interaction strength as a particle undergoing ordinary $2\ensuremath{\rightarrow}2$ freeze-out, or equivalently, exponentially weaker interactions for the same mass. We demonstrate this mechanism in a leptophilic dark matter model, which allows for dark matter masses up to ${10}^{9}\text{ }\text{ }\mathrm{GeV}$.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

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)0.0010.002

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 head0.011
GPT teacher head0.250
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations49
Published2021
Admission routes1
Has abstractyes

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