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1. Search for Higgs boson production in association with a high-energy photon via vector-boson fusion with decay into bottom quark pairs at $$ \sqrt{s} $$ = 13 TeV with the ATLAS detector

   https://doi.org/10.1007/JHEP03(2021)268  

   Aad, G.; Abbott, B.; Abbott, D. C.; Abed Abud, A.; Abeling, K.; Abhayasinghe, D. K.; Abidi, S. H.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; et al (March 2021, Journal of High Energy Physics)

   null (Ed.)

   A bstract A search is presented for the production of the Standard Model Higgs boson in association with a high-energy photon. With a focus on the vector-boson fusion process and the dominant Higgs boson decay into b -quark pairs, the search benefits from a large reduction of multijet background compared to more inclusive searches. Results are reported from the analysis of 132 fb − 1 of pp collision data at $$ \sqrt{s} $$ s = 13 TeV collected with the ATLAS detector at the LHC. The measured Higgs boson signal yield in this final-state signature is 1 . 3 ± 1 . 0 times the Standard Model prediction. The observed significance of the Higgs boson signal above the background is 1 . 3 standard deviations, compared to an expected significance of 1 . 0 standard deviations. 

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2. Weighted Ensembles for Adaptive Active Learning

   Polyzos, K D; Lu, Q; Giannakis, G B (March 2024, IEEE transactions on signal processing)

   Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, computer vision and wireless networks to list a few. To efficiently train machine learning models under such high labeling costs, active learning (AL) judiciously selects the most informative data instances to label on-the-fly. This active sampling process can benefit from a statistical function model, that is typically captured by a Gaussian process (GP) with well-documented merits especially in the regression task. While most GP-based AL approaches rely on a single kernel function, the present contribution advocates an ensemble of GP (EGP) models with weights adapted to the labeled data collected incrementally. Building on this novel EGP model, a suite of acquisition functions emerges based on the uncertainty and disagreement rules. An adaptively weighted ensemble of EGP-based acquisition functions is advocated to further robustify performance. Extensive tests on synthetic and real datasets in the regression task showcase the merits of the proposed EGP-based approaches with respect to the single GP-based AL alternatives. 

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3. earch for a W′ boson decaying to a vector-like quark and a top or bottom quark in the all-jets final state at $$ \sqrt{\mathrm{s}} $$ = 13 TeV

   https://doi.org/10.1007/JHEP09(2022)088  

   Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Escalante Del Valle, A.; Frühwirth, R.; Jeitler, M.; Krammer, N.; et al (September 2022, Journal of High Energy Physics)

   A bstract A search is presented for a heavy W′ boson resonance decaying to a B or T vector-like quark and a t or a b quark, respectively. The analysis is performed using proton-proton collisions collected with the CMS detector at the LHC. The data correspond to an integrated luminosity of 138 fb − 1 at a center-of-mass energy of 13 TeV. Both decay channels result in a signature with a t quark, a Higgs or Z boson, and a b quark, each produced with a significant Lorentz boost. The all-hadronic decays of the Higgs or Z boson and of the t quark are selected using jet substructure techniques to reduce standard model backgrounds, resulting in a distinct three-jet W′ boson decay signature. No significant deviation in data with respect to the standard model background prediction is observed. Upper limits are set at 95% confidence level on the product of the W′ boson cross section and the final state branching fraction. A W′ boson with a mass below 3.1 TeV is excluded, given the benchmark model assumption of democratic branching fractions. In addition, limits are set based on generalizations of these assumptions. These are the most sensitive limits to date for this final state. 

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4. Randomly Projected Additive Gaussian Processes for Regression

   Delbridge, I. (January 2020, International Conference on Machine Learning)

   Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle with even moderate input dimensionality. Learning a low dimensional projection can help alleviate this curse of dimensionality, but introduces many trainable hyperparameters, which can be cumbersome, especially in the small data regime. We use additive sums of kernels for GP regression, where each kernel operates on a different random projection of its inputs. Surprisingly, we find that as the number of random projections increases, the predictive performance of this approach quickly converges to the performance of a kernel operating on the original full dimensional inputs, over a wide range of data sets, even if we are projecting into a single dimension. As a consequence, many problems can remarkably be reduced to one dimensional input spaces, without learning a transformation. We prove this convergence and its rate, and additionally propose a deterministic approach that converges more quickly than purely random projections. Moreover, we demonstrate our approach can achieve faster inference and improved predictive accuracy for high-dimensional inputs compared to kernels in the original input space. 

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5. Inclusive search for highly boosted Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at $$ \sqrt{s} $$ = 13 TeV

   https://doi.org/10.1007/JHEP12(2020)085  

   Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Bergauer, T.; Dragicevic, M.; Erö, J.; Escalante Del Valle, A.; Frühwirth, R.; Jeitler, M.; et al (December 2020, Journal of High Energy Physics)

   null (Ed.)

   A bstract A search for standard model Higgs bosons (H) produced with transverse momentum ( p T ) greater than 450 GeV and decaying to bottom quark-antiquark pairs ( $$ \mathrm{b}\overline{\mathrm{b}} $$ b b ¯ ) is performed using proton-proton collision data collected by the CMS experiment at the LHC at $$ \sqrt{s} $$ s = 13 TeV. The data sample corresponds to an integrated luminosity of 137 fb − 1 . The search is inclusive in the Higgs boson production mode. Highly Lorentz-boosted Higgs bosons decaying to $$ \mathrm{b}\overline{\mathrm{b}} $$ b b ¯ are reconstructed as single large-radius jets, and are identified using jet substructure and a dedicated b tagging technique based on a deep neural network. The method is validated with Z → $$ \mathrm{b}\overline{\mathrm{b}} $$ b b ¯ decays. For a Higgs boson mass of 125 GeV, an excess of events above the background assuming no Higgs boson production is observed with a local significance of 2.5 standard deviations ( σ ), while the expectation is 0.7. The corresponding signal strength and local significance with respect to the standard model expectation are μ H = 3 . 7 ± 1 . 2(stat) $$ {}_{-0.7}^{+0.8} $$ − 0.7 + 0.8 (syst) $$ {}_{-0.5}^{+0.8} $$ − 0.5 + 0.8 (theo) and 1 . 9 σ . Additionally, an unfolded differential cross section as a function of Higgs boson p T for the gluon fusion production mode is presented, assuming the other production modes occur at the expected rates. 

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