Research
My research is in experimental particle physics, primarily with the ATLAS experiment at CERN's Large Hadron Collider (LHC). I study the fundamental particles and interactions of nature using high-energy proton-proton collisions. My work ranges from precision measurements and Higgs-boson physics to particle reconstruction, machine learning and detector development.
Machine learning and precision measurements
A major focus of my current research is developing new ways of making precision measurements from the complex data recorded at the LHC. Traditional measurements typically reduce each collision event to one or two quantities and publish the resulting distributions as histograms. Much of the information in the original collision is therefore lost.
Together with students and collaborators, I am developing machine-learning methods that allow measurements to retain much more of this information. In particular, we use OmniFold and related techniques to perform high-dimensional and unbinned measurements. We recently applied this approach to ATLAS Z+jets data, using machine learning to unfold complete sets of reconstructed charged particles rather than a small number of predefined observables. An important part of our work is developing methods to validate these measurements and quantify their experimental and modelling uncertainties.
Higgs boson and electroweak physics
I have worked on Higgs-boson physics since before its discovery at the LHC in 2012. My current interests include precision measurements of the Higgs boson's interactions and searches for rare or unexpected Higgs decays.
Recent projects at Carleton include the ATLAS measurement of Higgs-boson decays to pairs of muons (H → μμ), measurements probing the relative strengths and signs of the Higgs boson's interactions with W and Z bosons, and searches for lepton-flavour-violating decays such as H → eμ. I also work on measurements of electroweak Z+jets production in the vector-boson-fusion topology, including differential cross-section and polarization measurements.
Jets and particle reconstruction
Quarks and gluons produced in high-energy collisions form collimated sprays of particles known as jets. Reconstructing and measuring the energy of these jets accurately is essential for a large fraction of the ATLAS physics program.
I have worked extensively on jet reconstruction and calibration, calorimeter performance, and particle-flow reconstruction. Current work includes improving jet calibration using machine learning and understanding the performance of jets in challenging regions of the ATLAS detector. I am also interested in how modern machine-learning techniques can improve the reconstruction and calibration of individual particles and complete collision events.
Detectors and future experiments
I contribute to the Carleton program constructing the new ATLAS Inner Tracker (ITk), an all-silicon tracking detector being built for the High-Luminosity LHC. The upgraded detector will allow ATLAS to operate in the much more intense collision environment expected during the next phase of the LHC program.
I am also interested in detector technologies for future particle-physics experiments. One emerging direction is the use of machine learning directly in detector readout electronics, allowing information from highly granular detectors to be processed in real time before the data are recorded.
Research group
My research group includes graduate and undergraduate students and postdoctoral researchers working on particle-physics analysis, machine learning, scientific computing and detector development. See the People page for current group members and projects.