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Record W2790422624 · doi:10.1103/physrevd.98.083516

Saturated overburden scattering and the multiscatter frontier: Discovering dark matter at the Planck mass and beyond

2018· article· en· W2790422624 on OpenAlexafffund
Joseph Bramante, B. Broerman, R. F. Lang, Nirmal Raj

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsQueen's UniversityPerimeter Institute
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaOntario Ministry of Economic Development and InnovationAspen Center for PhysicsNational Science Foundation
KeywordsDark matterPlanckPhysicsOverburdenScatteringAstrophysicsElectronXenonParticle physicsNuclear physicsOpticsGeology

Abstract

fetched live from OpenAlex

We show that underground experiments like LUX/LZ, PandaX-II, XENON, and PICO could discover dark matter up to the Planck mass and beyond, with new searches for dark matter that scatters multiple times in these detectors. This opens up significant discovery potential via reanalysis of existing and future data. We also identify a new effect that substantially enhances experimental sensitivity to large dark matter scattering cross sections: while passing through atmospheric or solid overburden, there is a maximum number of scatters that dark matter undergoes, determined by the total number of scattering sites it passes, such as nuclei and electrons. For dark matter heavier than $\ensuremath{\sim}{10}^{15}\text{ }\text{ }\mathrm{GeV}$, this extends the reach of some published limits and future analyses to exclude large dark matter scattering cross sections, using detectors two kilometers underground.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.007
GPT teacher head0.327
Teacher spread0.321 · 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.

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

Citations70
Published2018
Admission routes2
Has abstractyes

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