Analyzing N -Point Energy Correlators inside Jets with CMS Open Data
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Beamlines are required to shield against two primary sources of radiation, the primary gas bremsstrahlung (GB) and the synchrotron beam, as well as the secondary radiation resulting from the scattering of these two primary beams by the beamline components and/or air. The shielding requirements for the First Optical Enclosure (FOE) are dominated by the scattering of the primary bremsstrahlung and not the synchrotron beam.
We have begun a search for interesting signature from a coannihilation codex model.
Introduction points: LHC performed very well, delivering > 150 fb -1 ; Huge challenge to quickly process this amount of data; Understand detector & object performance; pT miss tails very important for SUSY searches; Most results on full Run 2 data set will come in Summer; SUSY searches are expanding; Big inclusive searches complemented by dedicated searches to close gaps in coverage; Machine learning for H, b, c, top,… tagging; and Improved analysis techniques
The slide presentation covers: B→K*μμ angular analysis and prospects, CP Violation in the B s →J/ψϕ decay, Angular analysis of the B→J/ψΛp decay; Spectroscopy of excited B s and B c mesons, and Conclusions.
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The High-granularity Calorimeter silicon sensors suffer from leakage currents. Measurements of the leakage current as a function of temperature and reverse bias voltage were carried out using a temperature chamber and digital source-meter. In addition to analysis of the sensors, a redesigned data acquisition software was written and utilized to simplify the setup. It was determined that the sensors do follow expected behavior for the leakage current as a function of both reverse bias voltage and temperature, but there is some slight deviation at lower temperatures.
This poster seeks to characterize the quality of silicon sensors that will potentially be used in the High Granularity Calorimeter at the Compact Muon Solenoid detector. The silicon sensors used in this analysis were provided by the companies Hammatsu and NHanced. The silicon sensors were characterized by measuring the capacitance of the MOS and diode structures on the sensor's test structures both before and after irradiation.
The automatization of failing workflow handling is discussed. Implementation of a pipeline for DAQ and ML of error logs using big data analysis tools is available at github. Development of a prototype NLP model in Keras is explored.
Long-lived particles (LLPs) appear in many well-motivated BSM models
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