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Record W4312191938 · doi:10.1088/1475-7516/2022/12/031

Diffuse x-ray and gamma-ray limits on boson stars that interact with nuclei

2022· article· en· W4312191938 on OpenAlexaff
Javier F. Acevedo, Amit Bhoonah, Joseph Bramante

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsPerimeter InstituteArthur B. McDonald-Canadian Astroparticle Physics Research InstituteQueen's University
Fundersnot available
KeywordsPhysicsDark matterAstrophysicsBosonStarsParticle physicsHidden sector

Abstract

fetched live from OpenAlex

Abstract Light bosonic dark matter can form gravitationally bound states known as boson stars. In this work, we explore a new signature of these objects interacting with the interstellar medium (ISM). We show how small effective couplings between the bosonic dark matter and the nucleon lead to a potential that accelerates ISM baryons as they transit the boson star, making the ISM within radiate at a high rate and energy. The low ISM density, however, implies the majority of Galactic boson stars will be too faint to be observable through this effect. By contrast, the diffuse photon flux, in hard x-rays and soft gamma-rays, produced by boson stars interacting with the ionized ISM phases can be sizable. We compute this diffuse flux and compare it to existing observations from HEAO-1, INTEGRAL and COMPTEL to infer limits on the fraction of these objects. This novel method places constraints on boson star dark matter while avoiding back-action effects from ambient baryons on the boson star configuration, unlike terrestrial searches where it has been noted that back-action can screen light bosonic fields. In addition, this study could be extended to other couplings and structures formed from light dark matter. For dark matter masses (10 -14 , 10 -8 ) eV and boson star masses (10 -10 , 10 -1 ) M ⊙ , we find the constraints on the fraction can go down to f * ≲ 10 -9 for dark matter in boson stars that is directly coupled to the Standard 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.473

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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 designObservational
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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

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