HiggsSignals-2: probing new physics with precision Higgs measurements in the LHC 13 TeV era

Abstract

The program HiggsSignals confronts the predictions of models with arbitrary Higgs sectors with the available Higgs signal rate and mass measurements, resulting in a likelihood estimate. A new version of the program, HiggsSignals-2, is presented that contains various improvements in its functionality and applicability. In particular, the new features comprise improvements in the theoretical input framework and the handling of possible complexities of beyond-the-SM Higgs sectors, as well as the incorporation of experimental results in the form of simplified template cross section (STXS) measurements. The new functionalities are explained, and a thorough discussion of the possible statistical interpretations of the HiggsSignals results is provided. The performance of HiggsSignals is illustrated for some example analyses. In this context the importance of public information on certain experimental details like efficiencies and uncertainty correlations is pointed out. HiggsSignals is continuously updated to the latest experimental results and can be obtained at https://gitlab.com/higgsbounds/higgssignals. ; Funded by SCOAP3. ; The work of S.H. was supported in part by the MEINCOP (Spain) under contract FPA2016-78022-P and under contract PID2019-110058GB-C21, in part by the Spanish Agencia Estatal de Investigación (AEI), in part by the EU Fondo Europeo de Desarrollo Regional (FEDER) through the project FPA2016-78645-P, in part by the "Spanish Red Consolider MultiDark" FPA2017-90566-REDC, and in part by the AEI through the grant IFT Centro de Excelencia Severo Ochoa SEV-2016-0597. T.S. and G.W. acknowledge support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2121 "Quantum Universe" – 390833306. The work of J.W. is supported by the Swedish Research Council, contract number 2016-05996 and was in part funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, Grant Agreement No 668679. ; Peer reviewed