High-Luminosity LHC

In his doctoral thesis, Todd details a method for data analysis in a way that minimizes a source of bias in some particle physics experiments. By analyzing information from two distant detectors simultaneously rather than sequentially, he incorporated the lack of precision knowledge in both detectors. A University of Cincinnati graduate, Todd used data from Fermilab’s MINOS and MINOS+ experiments, and his analysis can be applied in other neutrino research as well.

The year 2018 will be remembered as a very eventful year for CMS as a whole and especially for the Fermilab group. Thanks to excellent accelerator performance, the LHC delivered much more proton-proton collision data than anticipated, making the LHC Run 2 a very successful data-taking period. Being at the very core of the detector operations and computing, the Fermilab group was key in ensuring that a large and high quality data set was collected for searches and precision measurements.

The building boom

These international projects, selected during the process to plan the future of U.S. particle physics, are all set to come online within the next 10 years.

Machine learning will become an even more important tool when scientists upgrade to the High-Luminosity Large Hadron Collider.