|
LHCb Collaboration(Aaij, R. et al), Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., et al. (2021). First measurement of the CP-violating phase in B-s(0) -> J/Psi (-> e(+) e(-))phi decays. Eur. Phys. J. C, 81(11), 1026–18pp.
Abstract: A flavour-tagged time-dependent angular analysis of B-s(0) -> J/Psi phi decays is presented where the J/Psi meson is reconstructed through its decay to an e(+)e(-) pair. The analysis uses a sample of pp collision data recorded with the LHCb experiment at centre-of-mass energies of 7 and 8TeV, corresponding to an integrated luminosity of 3 fb(-1). The CP-violating phase and lifetime parameters of the B-s(0) s system are measured to be phi(s) = 0.00 +/- 0.28 +/- 0.07 rad, Delta Gamma(s) = 0.115 +/- 0.045 +/- 0.011 ps(-1) and Delta Gamma(s) = 0.608 +/- 0.018 +/- 0.012 ps(-1) where the first uncertainty is statistical and the second systematic. This is the first time that CP-violating parameters are measured in the B-s(0) -> J/Psi phi decay with an e+e- pair in the final state. The results are consistent with previous measurements in other channels and with the Standard Model predictions.
|
|
|
ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo, F. L., et al. (2021). Search for R-parity-violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using root s=13 TeV proton-proton collision data. Eur. Phys. J. C, 81(11), 1023–39pp.
Abstract: A search for R-parity-violating supersymmetry in final states characterized by high jet multiplicity, at least one isolated light lepton and either zero or at least three b-tagged jets is presented. The search uses 139 fb(-1) of root s = 13 TeV proton-proton collision data collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature gluino production, top-squark production, or electroweakino production. The dominant sources of background are estimated using a data-driven model, based on observables at medium jet multiplicity, to predict the b-tagged jet multiplicity distribution at the higher jet multiplicities used in the search. Machine-learning techniques are used to reach sensitivity to electroweakino production, extending the data-driven background estimation to the shape of the machine-learning discriminant. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence level are extracted, reaching as high as 2.4 TeV in gluino mass, 1.35 TeV in top-squark mass, and 320 (365) GeV in higgsino (wino) mass.
|
|
|
Garofalo, M., Romero-Lopez, F., Rusetsky, A., & Urbach, C. (2021). Testing a new method for scattering in finite volume in the phi(4) theory. Eur. Phys. J. C, 81(11), 1034–5pp.
Abstract: We test an alternative proposal by Bruno and Hansen (J High Energy Phys 2021(6), https://doi.org/10.1007/JHEP06(2021)043, 2021) to extract the scattering length from lattice simulations in a finite volume. For this, we use a scalar phi(4) theory with two mass nondegenerate particles and explore various strategies to implement this new method. We find that the results are comparable to those obtained from the Luscher method, with somewhat smaller statistical uncertainties at larger volumes.
|
|
|
Agrawal, P. et al, Hernandez, P., & Lopez-Pavon, J. (2021). Feebly-interacting particles: FIPs 2020 workshop report. Eur. Phys. J. C, 81(11), 1015–137pp.
Abstract: With the establishment and maturation of the experimental programs searching for new physics with sizeable couplings at the LHC, there is an increasing interest in the broader particle and astrophysics community for exploring the physics of light and feebly-interacting particles as a paradigm complementary to a New Physics sector at the TeV scale and beyond. FIPs 2020 has been the first workshop fully dedicated to the physics of feebly-interacting particles and was held virtually from 31 August to 4 September 2020. The workshop has gathered together experts from collider, beam dump, fixed target experiments, as well as from astrophysics, axions/ALPs searches, current/future neutrino experiments, and dark matter direct detection communities to discuss progress in experimental searches and underlying theory models for FIPs physics, and to enhance the cross-fertilisation across different fields. FIPs 2020 has been complemented by the topical workshop “Physics Beyond Colliders meets theory”, held at CERN from 7 June to 9 June 2020. This document presents the summary of the talks presented at the workshops and the outcome of the subsequent discussions held immediately after. It aims to provide a clear picture of this blooming field and proposes a few recommendations for the next round of experimental results.
|
|
|
Arganda, E., Marcano, X., Martin Lozano, V., Medina, A. D., Perez, A. D., Szewc, M., et al. (2022). A method for approximating optimal statistical significances with machine-learned likelihoods. Eur. Phys. J. C, 82(11), 993–14pp.
Abstract: Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the signal-plus-background hypothesis over the background-only one. We present here a simple method that combines the power of current machine-learning techniques to face high-dimensional data with the likelihood-based inference tests used in traditional analyses, which allows us to estimate the sensitivity for both discovery and exclusion limits through a single parameter of interest, the signal strength. Based on supervised learning techniques, it can perform well also with high-dimensional data, when traditional techniques cannot. We apply the method to a toy model first, so we can explore its potential, and then to a LHC study of new physics particles in dijet final states. Considering as the optimal statistical significance the one we would obtain if the true generative functions were known, we show that our method provides a better approximation than the usual naive counting experimental results.
|
|