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  1. Abstract Analysis description languages are declarative interfaces for HEP data analysis that allow users to avoid writing event loops, simplify code, and enable performance improvements to be decoupled from analysis development. One example is FuncADL, inspired by functional programming and developed using Python as a host language. FuncADL borrows concepts from database query languages to isolate the interface from the underlying physical and logical schemas. The same query can be used to select data from different sources and formats and with different execution mechanisms. FuncADL is one of the tools being developed by IRIS-HEP for highly scalable physics analysis for the LHC and HL-LHC. FuncADL is demonstrated by implementing example analysis tasks designed by HSF and IRIS-HEP. Another language example is ADL, which expresses the physics content of an analysis in a standard and unambiguous way, independent of computing frameworks. In ADL, analyses are described in human-readable text files composed of blocks with a keyword-expression structure. Two infrastructures are available to render ADL executable: CutLang, a runtime interpreter written in C++; and adl2tnm, a transpiler converting ADL into C++ or Python code. ADL/CutLang are already used in several physics studies and educational projects, and are adapted for use with LHC Open Data. 
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  2. Free, publicly-accessible full text available December 1, 2024
  3. A<sc>bstract</sc>

    A search for new physics in top quark production with additional final-state leptons is performed using data collected by the CMS experiment in proton-proton collisions at$$ \sqrt{s} $$s= 13 TeV at the LHC during 2016–2018. The data set corresponds to an integrated luminosity of 138 fb1. Using the framework of effective field theory (EFT), potential new physics effects are parametrized in terms of 26 dimension-six EFT operators. The impacts of EFT operators are incorporated through the event-level reweighting of Monte Carlo simulations, which allows for detector-level predictions. The events are divided into several categories based on lepton multiplicity, total lepton charge, jet multiplicity, and b-tagged jet multiplicity. Kinematic variables corresponding to the transverse momentum (pT) of the leading pair of leptons and/or jets as well as thepTof on-shell Z bosons are used to extract the 95% confidence intervals of the 26 Wilson coefficients corresponding to these EFT operators. No significant deviation with respect to the standard model prediction is found.

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    Free, publicly-accessible full text available December 1, 2024
  4. Free, publicly-accessible full text available November 1, 2024
  5. Abstract

    A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015–2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.

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    Free, publicly-accessible full text available November 1, 2024
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  9. Abstract

    A search for decays to invisible particles of Higgs bosons produced in association with a top-antitop quark pair or a vector boson, which both decay to a fully hadronic final state, has been performed using proton-proton collision data collected at$${\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V}}$$s=13TeVby the CMS experiment at the LHC, corresponding to an integrated luminosity of 138$$\,\text {fb}^{-1}$$fb-1. The 95% confidence level upper limit set on the branching fraction of the 125$$\,\text {Ge}\hspace{-.08em}\text {V}$$GeVHiggs boson to invisible particles,$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$B(Hinv), is 0.54 (0.39 expected), assuming standard model production cross sections. The results of this analysis are combined with previous$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$B(Hinv)searches carried out at$${\sqrt{s}=7}$$s=7, 8, and 13$$\,\text {Te}\hspace{-.08em}\text {V}$$TeVin complementary production modes. The combined upper limit at 95% confidence level on$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$B(Hinv)is 0.15 (0.08 expected).

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    Free, publicly-accessible full text available October 1, 2024
  10. Free, publicly-accessible full text available October 1, 2024