High-mass Final States Research Articles

Machine learning approach for the search of resonances with topological features at the Large Hadron Collider

The observation of resonances is unequivocal evidence of new physics beyond the Standard Model at the Large Hadron Collider (LHC). So far, inclusive and model-dependent searches have not provided evidence of new resonances, indicating these could be driven by subtle topologies. Here, we use machine learning techniques based on weak supervision to perform searches. Weak supervision based on mixed samples can be used to search for resonances with little or no prior knowledge of the production mechanism. Also, it offers the advantage that sidebands or control regions can be used to effectively model backgrounds with minimal reliance on simulations. However, weak supervision alone is found to be highly inefficient in identifying corners of the multi-dimensional space of interest. Instead, we propose an approach to search for new resonances that involves a classification procedure that is signature and topology based. A combination of weak supervision with Deep Neural Network algorithms is applied following this classification. The performance of this strategy is evaluated on the production of SM Higgs boson decaying to a pair of photons inclusively and in exclusive regions of phase space tailored for specific production modes at the LHC. After verifying the ability of the methodology to extract different SM Higgs boson signal mechanisms, a search for new phenomena in high-mass final states is set up for the LHC.

Search for new phenomena in high-mass final states with a photon and a jet from pp collisions at sqrt{s} = 13\xa0TeV with the ATLAS detector

A search is performed for new phenomena in events having a photon with high transverse momentum and a jet collected in 36.7~text {fb}^{-1} of proton–proton collisions at a centre-of-mass energy of sqrt{s} = 13 TeV recorded with the ATLAS detector at the Large Hadron Collider. The invariant mass distribution of the leading photon and jet is examined to look for the resonant production of new particles or the presence of new high-mass states beyond the Standard Model. No significant deviation from the background-only hypothesis is observed and cross-section limits for generic Gaussian-shaped resonances are extracted. Excited quarks hypothesized in quark compositeness models and high-mass states predicted in quantum black hole models with extra dimensions are also examined in the analysis. The observed data exclude, at 95% confidence level, the mass range below 5.3 TeV for excited quarks and 7.1 TeV (4.4 TeV) for quantum black holes in the Arkani-Hamed–Dimopoulos–Dvali (Randall–Sundrum) model with six (one) extra dimensions.

Search for TeV-scale gravity signatures in high-mass final states with leptons and jets with the ATLAS detector at s=13 TeV

A search for physics beyond the Standard Model, in final states with at least one high transverse momentum charged lepton (electron or muon) and two additional high transverse momentum leptons or jets, is performed using 3.2 fb$^{-1}$ of proton--proton collision data recorded by the ATLAS detector at the Large Hadron Collider in 2015 at $\sqrt{s}=13$ TeV. The upper end of the distribution of the scalar sum of the transverse momenta of leptons and jets is sensitive to the production of high-mass objects. No excess of events beyond Standard Model predictions is observed. Exclusion limits are set for models of microscopic black holes with two to six extra dimensions.

New Physics with the ATLAS detector: experimental prospects

During 2010 the ATLAS detector has collected 45 pb−1 of proton-proton collisions at TeV. These data have been used for a wide range of searches such as high-mass final states and contact interactions. Early inclusive SUSY searches have been also performed for a wide range of final states. The most recent results of searches of physics beyond the Standard Model with the ATLAS detector are presented. Prospects for physics searches with ∼ 1 fb−1 of data will be discussed together with the most relevant performance results.