Search for nonresonant production of strongly coupled dark matter in proton-proton collisions at $\sqrt{s}$ = 13 TeV
Year: 2026
Type: preprint
Abstract: A search is presented for semivisible jets produced in nonresonant processes, using proton-proton collision data at $\sqrt{s}$ = 13 TeV, collected with the CMS detector at the CERN LHC and corresponding to an integrated luminosity of 138 fb$^{-1}$. These jets, containing both visible and invisible particles, would indicate the presence of dark matter arising from a strongly coupled hidden sector. The signal model includes a bifundamental scalar that couples simultaneously to standard model quarks and dark-sector quarks, the latter forming semivisible jets. The final state therefore includes missing transverse momentum aligned with a jet, rather than recoiling against standard model particles as typically required by conventional collider dark-matter searches. Two separate machine-learning strategies are employed to identify semivisible jets: a supervised graph neural network and an unsupervised autoencoder. No significant excess corresponding to dark-sector signal hypotheses is observed. Upper limits at 95% confidence level on the signal production cross section are determined for each strategy and interpreted as exclusions in the signal model parameter space. The supervised search excludes mediator masses between 0.5 and 2.4 TeV, depending on the other signal model parameters. For signal models with semivisible jets containing higher fractions of stable dark-matter particles, the unsupervised search demonstrates increased sensitivity with 5 (11)% higher expected (observed) mediator mass exclusions than the supervised search.
Source: arXiv (Cornell University)
Author: CMS Collaboration
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: Ministerio de Ciencia, Tecnología e Innovación, CERN, Department of Science and Technology, Ministry of Science and Technology, India, Science Foundation Ireland, Deutsche Forschungsgemeinschaft, Fundação de Amparo à Pesquisa do Estado de São Paulo, National Natural Science Foundation of China, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Austrian Science Fund, Fonds De La Recherche Scientifique - FNRS, Fonds Wetenschappelijk Onderzoek, Ministerio de Economía y Competitividad, Bulgarian National Science Fund, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, Ministry of Education, India, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Centre National de la Recherche Scientifique, Shota Rustaveli National Science Foundation, Universidad Autónoma de San Luis Potosí, Benemérita Universidad Autónoma de Puebla, Pakistan Atomic Energy Commission, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, Ministry of Science and ICT, South Korea, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Institut National de Physique Nucléaire et de Physique des Particules
Awards: G196419191, G6649312515
OpenAlex ID: W7223850845