ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., et al. (2022). Constraints on Higgs boson production with large transverse momentum using H -> b(b)over-bar decays in the ATLAS detector. Phys. Rev. D, 105(9), 092003–37pp.
Abstract: This paper reports constraints on Higgs boson production with transverse momentum above 1 TeV. The analyzed data from proton-proton collisions at a center-of-mass energy of 13 TeV were recorded with the ATLAS detector at the Large Hadron Collider from 2015 to 2018 and correspond to an integrated luminosity of 136 fb(-1.) Higgs bosons decaying into b (b) over bar are reconstructed as single large-radius jets recoiling against a hadronic system and are identified by the experimental signature of two b-hadron decays. The experimental techniques are validated in the same kinematic regime using the Z -> b (b) over bar process. The 95% confidence-level upper limit on the cross section for Higgs boson production with transverse momentum above 450 GeV is 115 fb, and above 1 TeV it is 9.6 fb. The Standard Model cross section predictions for a Higgs boson with a mass of 125 GeV in the same kinematic regions are 18.4 fb and 0.13 fb, respectively.
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LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2022). Observation of the Decay Lambda(0)(b) -> Lambda(+)(c) tau(-)(nu)over-bar(tau). Phys. Rev. Lett., 128(19), 191803–11pp.
Abstract: The first observation of the semileptonic b-baryon decay Lambda(0)(b) -> Lambda(+)(c) tau(-)(nu) over bar (tau) with a significance of 6.1 sigma, is reported using a data sample corresponding to 3 fb(-1) of integrated luminosity, collected by the LHCb experiment at center-of-mass energies of 7 and 8 TeV at the LHC. The tau(-) lepton is reconstructed in the hadronic decay to three charged pions. The ratio K = B(Lambda(0)(b) -> Lambda(+)(c) tau(-)(nu) over bar (tau))/B(Lambda(0)(b) -> Lambda(+)(c)pi(-)pi(+)pi(-)) is measured to be 2.46 +/- 0.27 +/- 0.40, where the first uncertainty is statistical and the second systematic. The branching fraction B(Lambda(0)(b) -> Lambda(+)(c) tau(-)(nu) over bar (tau)) (1.50 +/- 0.16 +/- 0.25 +/- 0.23)% is obtained, where the third uncertainty is from the external branching fraction of the normalization channel Lambda(0)(b) -> Lambda(+)(c)pi(-)pi(+)pi(-). The ratio of semileptonic branching fractions R(Lambda(+)(c)) B(Lambda(0)(b) -> Lambda(+)(c) tau(-)(nu) over bar (tau))/B(Lambda(0)(b) -> Lambda(+)(c)mu(-)(nu) over bar (tau)) is derived to be 0.242 +/- 0.026 +/- 0.040 +/- 0.059, where the external branching fraction uncertainty from the channel Lambda(0)(b) -> Lambda(+)(c)mu(-)(nu) over bar (tau) contributes to the last term. This result is in agreement with the standard model prediction.
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Cranmer, K. et al, & Sanz, V. (2022). Publishing statistical models: Getting the most out of particle physics experiments. SciPost Phys., 12(1), 037–55pp.
Abstract: The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases – including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits – we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.
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LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2022). Observation of the B-0 -> (D)over-bar*K-0(+) pi(-) and B-s(0) -> (D)over-bar*K-0(-)pi(+) decays. Phys. Rev. D, 105(7), 072005–22pp.
Abstract: The first observations of B-0 -> (D) over bar*(2007)K-0(+)pi(-) and B-s(0) -> (D) over bar*(2007)K-0(-)pi(+) decays are presented, and their branching fractions relative to that of the B ->( D) over bar* (2007)(0)pi(+)pi(-) decay are reported. These modes can potentially be used to investigate the spectroscopy of charm and charm-strange resonances and to determine the angle gamma of the Cabibbo-Kobayashi-Maskawa unitarity triangle. It is also important to understand them as a source of potential background in determinations of gamma from B+ -> DK+ and B-0 -> DK+pi(-) decays. The analysis is based on a sample corresponding to an integrated luminosity of 5.4 fb(-1 )of proton-proton collision data at 13 TeV center-of-mass energy recorded with the LHCb detector. The (D) over bar*(2007)(0) mesons are fully reconstructed in the (D) over bar (0)pi(0) and (D) over bar (0)gamma channels with the (D) over bar (0) -> K+pi(-) decay. A novel weighting method is used to subtract background while simultaneously applying an event-by-event efficiency correction to account for resonant structures in the decays.
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Aarrestad, T. et al, Mamuzic, J., & Ruiz de Austri, R. (2022). Benchmark data and model independent event classification for the large hadron collider. SciPost Phys., 12(1), 043–57pp.
Abstract: We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.
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