Searches and Measurements in Multi-Jet Production at the LHC and HL-LHC

About This Grant

One of humanity’s greatest searches has been for the fundamental constituents and forces which make up the universe. That search has led us to the Standard Model of Particle Physics, which describes three of the four fundamental forces of nature: electromagnetism, the weak nuclear force, and the strong nuclear force. The strong nuclear force, which holds protons and neutrons together into nuclei, is the focus of this project. We understand protons, neutrons, and other particles which interact through the strong force, to be composed of smaller constituents called “quarks”, and their interactions occur through the exchange of a particle called the “gluon”. At very high energy colliders like those at the Large Hadron Collider at the CERN laboratory near Geneva, these quarks and gluons can be produced and their properties studied. But because of the behavior of the strong force, these quarks and gluons are detected in our experiment as sprays of particles called “jets”. Studying jets is therefore our window into the world of quarks and gluons and things smaller than a proton. This research seeks to extend our knowledge of one of the fundamental forces of nature and to seek evidence for new particles or forces through deviations from the predictions of the Standard Model. To perform this research, new computing and analysis techniques, many based on Machine Learning and Artificial Intelligence, will be developed. New techniques for detecting and calibrating jets based on extremely high precision timing will also be developed. These are all applicable in a wide range of applications beyond experimental particle physics. As the only university in the mid-South region performing experiments at the Large Hadron Collider, this project also serves as an important tool for attracting students to study physics and to communicate basic particle physics to local high schools. This project proposes to study the production rates of collimated sprays of particles, so-called “jets”, in proton-proton collisions, using the ATLAS detector at the CERN Large Hadron Collider. Most of the time, the jets are produced in interactions due to the strong nuclear force, and the jets can be regarded as the footprints of the quarks, one of the fundamental particles of matter, and gluons, the carriers of the strong force. Measurements of multi-jet production rates are therefore sensitive to the properties of the strong force, as predicted by the theory of Quantum Chromodynamics (QCD). Ratios of multi-jet production rates, in which experimental and theoretical uncertainties (at least partially) cancel, are particularly suited to determine the “strong coupling constant”, ɑlpha_s, a parameter that specifies the strength of the strong force. QCD predicts that ɑlpha_s decreases with increasing energy, which is one of the fundamental predictions of the theory. New particle interactions, should they exist, can modify this running of ɑlpha_s, so that its precise measurement at high energies is an important method for searching for signals of new physics. New physics processes can also be discovered in multi-jet production by looking for jets originating from vertices highly displaced from the proton-proton interaction region, which would be an indication of new long-lived particles; or by looking for resonance peaks in multi-jet production, which for example could indicate pairs of new particles decaying into quarks or gluons which then form jets. The proposed research requires contributions in different areas, including experimental measurements of multi-jet production rates and their ratios, phenomenological analyses in which jet cross-sections and ɑlpha_s are extracted from the measurement results, and experimental work on detector operations and upgrades that supports the work on these measurements. Specific applications of Machine Learning and Artificial Intelligence will be developed for identifying jets and separating them from background, including the incorporation of high-precision timing information, incorporating high-precision timing in identifying jet vertices, development of Machine Learning algorithms for jet cleaning, and development of Machine Learning algorithms for data quality assessment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.