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Record W4375956503 · doi:10.1088/1475-7516/2023/08/073

Prospects for annihilating dark matter from M31 and M33 observations with the Cherenkov Telescope Array

2023· article· en· W4375956503 on OpenAlexfundno aff
Miltiadis Michailidis, Lorenzo Marafatto, D. Malyshev, Fabio Iocco, G. Zaharijaš, O. Sergijenko, M. Bernardos, Christopher Eckner, Alexey Boyarsky, Anastasia Sokolenko, A. Santangelo

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersHigh Energy PhysicsInstitut National de Physique Nucléaire et de Physique des ParticulesAgencia Estatal de InvestigaciónStaatssekretariat für Bildung, Forschung und InnovationArgonne National LaboratoryComisión Nacional de Investigación Científica y TecnológicaJapan Society for the Promotion of ScienceAustralian Research CouncilCanadian Space AgencyAgencia Nacional de Investigación y DesarrolloIstituto Nazionale di Fisica NucleareMinistério da Ciência, Tecnologia, Inovações e ComunicaçõesJunta de AndalucíaGeneralitat de CatalunyaState Committee of ScienceMinisterio de Ciencia e InnovaciónUniversitetet i OsloMax-Planck-GesellschaftCentre National de la Recherche ScientifiqueScience and Technology Facilities CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungFundação AraucáriaU.S. Department of EnergyFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroUniversity of SydneyMinistero dell’Istruzione, dell’Università e della RicercaMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheHrvatska Zaklada za ZnanostConselho Nacional de Desenvolvimento Científico e TecnológicoRegione LombardiaMinisterstvo Školství, Mládeže a TělovýchovyUniversidad de JaénLeverhulme TrustEuropean CommissionFundação de Amparo à Pesquisa do Estado de São PauloUniversity of AdelaideNational Research FoundationJavna Agencija za Raziskovalno Dejavnost RSUniversity of New South WalesBundesministerium für Bildung, Wissenschaft und ForschungDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekNatural Sciences and Engineering Research Council of CanadaWestern Sydney UniversityBundesministerium für Bildung und ForschungMinistry of Education, Culture, Sports, Science and TechnologyRoyal Swedish Academy of SciencesUniversity of OxfordInstitute for Cosmic Ray Research, University of TokyoUniversity of LeicesterIstituto Nazionale di AstrofisicaDurham UniversityVetenskapsrådetFondazione CariploSmithsonian InstitutionBarnard CollegeAgence Nationale de la RechercheSveučilište u ZagrebuComunidad de MadridGeorgia Institute of TechnologyMonash UniversityCentres de Recerca de CatalunyaUniversity of ChicagoSveučilište Josipa Jurja Strossmayera u OsijekuIowa State UniversityNational Science Foundation
KeywordsPhysicsCherenkov Telescope ArrayDark matterMilky WayCherenkov radiationAstrophysicsGalaxyFermi Gamma-ray Space TelescopeAnnihilationAstronomyLocal GroupParticle physics

Abstract

fetched live from OpenAlex

Abstract M31 and M33 are the closest spiral galaxies and the largest members (together with the Milky Way) of the Local group, which makes them interesting targets for indirect dark matter searches. In this paper we present studies of the expected sensitivity of the Cherenkov Telescope Array (CTA) to an annihilation signal from weakly interacting massive particles from M31 and M33. We show that a 100 h long observation campaign will allow CTA to probe annihilation cross-sections up to 〈 συ 〉 ≈ 5·10 -25 cm 3 s -1 for the τ + τ - annihilation channel (for M31, at a DM mass of 0.3 TeV), improving the current limits derived by HAWC by up to an order of magnitude. We present an estimate of the expected CTA sensitivity, by also taking into account the contributions of the astrophysical background and other possible sources of systematic uncertainty. We also show that CTA might be able to detect the extended emission from the bulge of M31, detected at lower energies by the Fermi /LAT.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.263

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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