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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., Castillo, F. L., et al. (2021). A search for the dimuon decay of the Standard Model Higgs boson with the ATLAS detector. Phys. Lett. B, 812, 135980–24pp.
Abstract: A search for the dimuon decay of the Standard Model (SM) Higgs boson is performed using data corresponding to an integrated luminosity of 139 fb(-1) collected with the ATLAS detector in Run 2 pp collisions at root s = 13 TeV at the Large Hadron Collider. The observed (expected) significance over the background-only hypothesis for a Higgs boson with a mass of 125.09 GeV is 2.0 sigma (1.7 sigma). The observed upper limit on the cross section times branching ratio for pp -> H -> μμis 2.2 times the SM prediction at 95% confidence level, while the expected limit on a H -> μμsignal assuming the absence (presence) of a SM signal is 1.1(2.0). The best-fit value of the signal strength parameter, defined as the ratio of the observed signal yield to the one expected in the SM, is μ= 1.2 +/- 0.6.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2020). A search for the Z gamma decay mode of the Higgs boson in pp collisions at root s=13 TeV with the ATLAS detector. Phys. Lett. B, 809, 135754–21pp.
Abstract: A search for the Z gamma decay of the Higgs boson, with Z boson decays into pairs of electrons or muons is presented. The analysis uses proton-proton collision data at root s = 13 TeV corresponding to an integrated luminosity of 139 fb(-1) recorded by the ATLAS detector at the Large Hadron Collider. The observed data are consistent with the expected background with a p-value of 1.3%. An upper limit at 95% confidence level on the production cross-section times the branching ratio for pp -> H -> Z gamma is set at 3.6 times the Standard Model prediction while 2.6 times is expected in the presence of the Standard Model Higgs boson. The best-fit value for the signal yield normalised to the Standard Model prediction is 2.0(-0.9)(+1.0) where the statistical component of the uncertainty is dominant.
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LHCb Collaboration(Aaij, R. et al), Garcia Martin, L. M., Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., et al. (2019). A search for Xi(++)(cc) -> D(+)pK(-)pi(+) decays. J. High Energy Phys., 10(10), 124–21pp.
Abstract: A search for the Xi(++)(cc) baryon through the Xi(++)(cc) -> D(+)pK(-)pi(+) decay is performed with a data sample corresponding to an integrated luminosity of 1.7 fb(-1) recorded by the LHCb experiment in pp collisions at a centre-of-mass energy of 13 TeV. No significant signal is observed in the mass range from the kinematic threshold of the decay to 3800 MeV/c(2). An upper limit is set on the ratio of branching fractions R = B(Xi(++)(cc) -> D(+)pK(-)pi(+))/B(Xi(++)(cc) -> A(c)(+) K- pi(+)pi(+)) with R < 1.7 (2.1) x 10(-2) at the 90% (95%) confidence level at the known mass of the Xi(++)(cc) state.
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n_TOF Collaboration(Alcayne, V. et al), Balibrea-Correa, J., Domingo-Pardo, C., Lerendegui-Marco, J., Babiano-Suarez, V., & Ladarescu, I. (2024). A Segmented Total Energy Detector (sTED) optimized for (n,γ) cross-section measurements at n_TOF EAR2. Radiat. Phys. Chem., 217, 11pp.
Abstract: The neutron time-of-flight facility nTOF at CERN is a spallation source dedicated to measurements of neutroninduced reaction cross-sections of interest in nuclear technologies, astrophysics, and other applications. Since 2014, Experimental ARea 2 (EAR2) is operational and delivers a neutron fluence of similar to 4 center dot 10(7) neutrons per nominal proton pulse, which is similar to 50 times higher than the one of Experimental ARea 1 (EAR1) of similar to 8 center dot 10(5) neutrons per pulse. The high neutron flux at EAR2 results in high counting rates in the detectors that challenged the previously existing capture detection systems. For this reason, a Segmented Total Energy Detector (sTED) has been developed to overcome the limitations in the detector's response, by reducing the active volume per module and by using a photo-multiplier (PMT) optimized for high counting rates. This paper presents the main characteristics of the sTED, including energy and time resolution, response to gamma-rays, and provides as well details of the use of the Pulse Height Weighting Technique (PHWT) with this detector. The sTED has been validated to perform neutron-capture cross-section measurements in EAR2 in the neutron energy range from thermal up to at least 400 keV. The detector has already been successfully used in several measurements at nTOF EAR2.
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Herrero-Garcia, J., Patrick, R., & Scaffidi, A. (2022). A semi-supervised approach to dark matter searches in direct detection data with machine learning. J. Cosmol. Astropart. Phys., 02, 039–19pp.
Abstract: The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.
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