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At least 145 records · Page 8

MUSiC: a model-unspecific search for new physics in proton–proton collisions at $\sqrt{s} = 13\,\text {TeV} $

Results of the Model Unspecific Search in CMS (MUSiC), using proton–proton collision data recorded at the LHC at a centre-of-mass energy of 13TeV, corresponding to an integrated luminosity of 35.9 fb -1 , are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Data-directed search for new physics based on symmetries of the SM

We propose exploiting symmetries (exact or approximate) of the Standard Model (SM) to search for physics Beyond the Standard Model (BSM) using the data-directed paradigm (DDP). Symmetries are very powerful because they provide two samples that can be compared without requiring simulation. Focusing on the data, exclusive selections which exhibit significant asymmetry can be identified efficiently and marked for further study. Using a simple and generic test statistic which compares two matrices already provides good sensitivity, only slightly worse than that of the profile likelihood ratio test statistic which relies on the exact knowledge of the signal shape. This can be exploited for rapidly scanning large portions of the measured data, in an attempt to identify regions of interest. We also demonstrate that weakly supervised Neural Networks could be used for this purpose as well.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A search for new physics in central exclusive production using the missing mass technique with the CMS detector and the CMS-TOTEM precision proton spectrometer

A generic search is presented for the associated production of a Z boson or a photon with an additional unspecified massive particle X, pp → pp + Z/γ + X, in proton-tagged events from proton–proton collisions at $\sqrt{s}$ = 13 TeV, recorded in 2017 with the CMS detector and the CMS-TOTEM precision proton spectrometer. The missing mass spectrum is analysed in the 600–1600 GeV range and a fit is performed to search for possible deviations from the background expectation. No significant excess in data with respect to the background predictions has been observed. Model-independent upper limits on the visible pro duction cross section of pp → pp + Z/γ + X are set

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Exploring New Physics on Cosmological Scales

In recent work with her group, the PI developed a general formalism to compute from first principles the projected mass density (convergence) power spectrum of the substructure in galactic halos under different populations of dark matter sub halos. She constructed a halo model-based formalism, computing the 1-subhalo and the 2-subhalo terms from first principles for the first time. She found that the asymptotic slope of the substructure power spectrum at large wave number reflects the internal density profile of the sub halos, and proposed this as a key observable to discern between different dark matter scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗

Searches for New Physics in Neutrino Oscillations at the MINOS+, NOvA, and DUNE Experiments

Neutrinos are some of the most mysterious fundamental particles in Standard Model: they are orders of magnitude lighter than the next lightest massive particle (the electron), and they rarely interact with other particles. In the last 20 years, we have firmly established that neutrinos change flavors as they travel by observing the disappearance of muon neutrinos produced by accelerators and interactions of cosmic rays with the atmosphere; the appearance of electron neutrinos in muon neutrino beams; and the disappearance of electron antineutrinos produced by nuclear reactors. Almost all experiments are consistent with the existence of only three neutrino flavor states which mix with three neutrino mass states. However, there are a few experiments which have seen evidence for oscillations at frequencies incompatible with the three known states, which would suggest the existence of a fourth state, the sterile neutrino. This project supported searches for physics beyond the Standard Model, especially sterile neutrinos, in neutrino oscillations at the MINOS/MINOS+ and NOvA experiments. These efforts were enabled through the use of Deep Learning approaches which have revolutionized selection and reconstruction methods in High Energy Physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗