A portrait of the Higgs boson by the CMS experiment ten years after the discovery

A Correction to this paper has been published (18 October 2023) : https://doi.org/10.1038/s41586-023-06164-8.Data availability: Tabulated results are provided in the HEPData record for this analysis. Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, re-use and open acess policy.Code availability: The CMS core software is publicly available on GitHub (https://github.com/cms-sw/cmssw).In July 2012, the ATLAS and CMS collaborations at the CERN Large Hadron Collider announced the observation of a Higgs boson at a mass of around 125 gigaelectronvolts. Ten years later, and with the data corresponding to the production of a 30-times larger number of Higgs bosons, we have learnt much more about the properties of the Higgs boson. The CMS experiment has observed the Higgs boson in numerous fermionic and bosonic decay channels, established its spin–parity quantum numbers, determined its mass and measured its production cross-sections in various modes. Here the CMS Collaboration reports the most up-to-date combination of results on the properties of the Higgs boson, including the most stringent limit on the cross-section for the production of a pair of Higgs bosons, on the basis of data from proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts. Within the uncertainties, all these observations are compatible with the predictions of the standard model of elementary particle physics. Much evidence points to the fact that the standard model is a low-energy approximation of a more comprehensive theory. Several of the standard model issues originate in the sector of Higgs boson physics. An order of magnitude larger number of Higgs bosons, expected to be examined over the next 15 years, will help deepen our understanding of this crucial sector.BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Stavros Niarchos Foundation (Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under project number 400140256 - GRK2497; the Hungarian Academy of Sciences, the New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundação para a Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”, and the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B05F650021 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA)

The CMS Statistical Analysis and Combination Tool: Combine

Metrics: https://link.springer.com/article/10.1007/s41781-024-00121-4/metricsThis paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide.CERN (European Organization for Nuclear Research)STFC (United Kingdom)Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundatio

Measurement of the differential tt¯ production cross section as a function of the jet mass and extraction of the top quark mass in hadronic decays of boosted top quarks

Data Availability: This manuscript has no associated data or the data will not be deposited. [Authors’ comment: Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS policy as stated in https://cms-docdb.cern.ch/cgibin/PublicDocDB/RetrieveFile?docid=6032 &filename=CMSDataPolicyV1.2.pdf &version=2.]A measurement of the jet mass distribution in hadronic decays of Lorentz-boosted top quarks is presented. The measurement is performed in the lepton + jets channel of top quark pair production (tt¯ ) events, where the lepton is an electron or muon. The products of the hadronic top quark decay are reconstructed using a single large-radius jet with transverse momentum greater than 400GeV . The data were collected with the CMS detector at the LHC in proton-proton collisions and correspond to an integrated luminosity of 138fb−1 . The differential tt¯ production cross section as a function of the jet mass is unfolded to the particle level and is used to extract the top quark mass. The jet mass scale is calibrated using the hadronic W boson decay within the large-radius jet. The uncertainties in the modelling of the final state radiation are reduced by studying angular correlations in the jet substructure. These developments lead to a significant increase in precision, and a top quark mass of 173.06±0.84GeV.SCOAP

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

A preprint version of the article is available at: arXiv:2402.15366v2 [physics.ins-det], https://arxiv.org/abs/2402.15366 . Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/MLG-23-001 (CMS Public Pages). Report numbers: CMS-MLG-23-001, CERN-EP-2023-303.Data Availability: No datasets were generated or analyzed during the current study.Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.SCOAP3. Open access funding provided by CERN (European Organization for Nuclear Research

FEUP peer mentoring: Promoting integration through support and experience sharing

Admission to higher education is a milestone in the lives of young people. This can be accompanied by several changes in the student’s life such as a new place of residence, a new group of friends, and a new type of education. This entry into higher education can provide a new series of experiences, challenges, and newfound independence. However, it might also expose problems and difficulties, possibly hampering the student's personal and academic development. In order to ease the integration into higher education, the Faculty of Engineering of the University of Porto (FEUP) has developed a Peer Mentoring Programme promoted by students already attending different FEUP courses (mentors) which intends to support the first-year students (mentees) in this phase of their life, coordinated by some teachers from each course. This social and academic integration program is supported by 4 core ideas: Integration, Support, Experience, and Sharing. This work provides insight into the way in which this program is organized at FEUP, highlighting the students’ participation (mentees and mentors), the main contributions that each of them values, their degree of satisfaction and involvement, activities that were developed, and some testimonies. </jats:p

Selective loss of PARG restores PARylation and counteracts PARP inhibitor-mediated synthetic lethality

Inhibitors of poly(ADP-ribose) (PAR) polymerase (PARPi) have recently entered the clinic for the treatment of homologous recombination (HR)-deficient cancers. Despite the success of this approach, drug resistance is a clinical hurdle, and we poorly understand how cancer cells escape the deadly effects of PARPi without restoring the HR pathway. By combining genetic screens with multi-omics analysis of matched PARPi-sensitive and -resistant Brca2-mutated mouse mammary tumors, we identified loss of PAR glycohydrolase (PARG) as a major resistance mechanism. We also found the presence of PARG-negative clones in a subset of human serous ovarian and triple-negative breast cancers. PARG depletion restores PAR formation and partially rescues PARP1 signaling. Importantly, PARG inactivation exposes vulnerabilities that can be exploited therapeutically

Mucociliary transport, differential white blood cells, and cyto-genotoxicity in peripheral erythrocytes in fish from a polluted urban pond

The present study evaluated the water quality of a polluted pond through the analysis of in vitro mucociliary transport, hematological parameters, and biomarkers of cyto-genotoxicity in the Nile tilapia (Oreochromis niloticus). Blood and mucus samples were collected from ten specimens from the polluted pond and from ten specimens from a control area. The fish were anesthetized with 3% benzocaine, mucus was collected directly from the gills, and blood was drawn from the caudal artery. Blood smears were stained using the May-Grünwald Giemsa process for the differential leukocyte counts and to determine the frequency of leukocytes, thrombocytes, erythroblasts, micronuclei, and nuclear abnormalities. The results revealed low transportability in vitro, a high percentage of monocytes and eosinophils, and increased frequency of leukocytes and nuclear abnormalities in fish from the polluted pond. However, the frequency of thrombocytes and erythroblasts and the percentage of lymphocytes and neutrophils were significantly lower. It is possible to conclude that changes in fish are due to poor water quality and that these non-destructive biomarkers can be used for the biomonitoring of aquatic environments vulnerable to contamination

Benthic Estuarine Assemblages of the Southeastern Brazil Marine Ecoregion (SBME)

We assess the current knowledge of the benthic assemblages in the Southeastern Brazil Marine Ecoregion (SBME), which extends for approximately 1200 km of coastline and includes seven major estuarine systems from Guanabara Bay in Rio de Janeiro to Babitonga Bay (or Sao Francisco do Sul) in Santa Catarina. The high ecosystem diversity of SBME putatively accounts for the high levels of endemism of the regional marine invertebrate fauna. However, until more taxonomical and biogeographical evidence is available, the SBME should be treated as a working biogeographical hypothesis rather than a cohesive unit identified by endemic fauna. As a consequence of urban, agricultural, and industrial development, the coastal areas from the SBME have been the most altered in the country over the last 500 years. Some of the largest cities and busiest harbors of the country are in or near the regional estuarine areas. The rapid environmental changes over the last several decades do not allow for the assessment if current similarities and dissimilarities in the benthic assemblages express pristine conditions or if they are already the result of major human interventions, especially in the case of the Guanabara, Sepetiba, and Santos estuaries.Univ Fed Parana, Ctr Estudos Mar, Pontal Do Sul, Parana, BrazilUniv Fed Sao Paulo, Inst Mar, Santos, SP, BrazilUniv Catolica Norte, Millennium Nucleus Ecol & Sustainable Management, Fac Ciencias Mar, Dept Biol Marina, Coquimbo, ChileUniv Estadual Paulista, Inst Biociencias, Campus Litoral Paulista, Sao Vicente, SP, BrazilUniv Fed Fluminense, Dept Biol Marinha, Campus Valonguinho, Niteroi, RJ, BrazilUniv Estadual Paulista, Inst Biociencias, Campus Litoral Paulista, Sao Vicente, SP, Brazi