Ryan Cross
Currently, my primary research focus is on reconstruction: The process of taking the raw results from High Energy Physics (HEP) experiments, and moving towards higher-level, Physics quantities.
That is, when performing research, an analysis wants to work on fairly high level features, say "I want this particle and this other particle interacting, with energies above some limit". That is fairly far removed from the raw signals we get out of our detectors! Reconstruction is a broad name for the variety of software that is used to fill that gap. Common steps include hit creation (discretizing the raw waveforms from a detector), noise removal and other techniques to make the signal stand out more, pattern recognition and finally particle identification (to distinguish what underlying particle a group of hits came from).
I work mostly in the pattern recognition step, as part of the Pandora reconstruction framework. I have worked on this in the context of the Deep Underground Neutrino Experiment (DUNE) and MicroBooNE.
My current project is improving Pandora for the unique challenges the near detector (ND, so-called because it is nearer the beam than the far detector) at DUNE will face. To start with, it uses a native 3D technology, rather than the more common 2D outputs. This means Pandora's entire suite of algorithms has extra info they can exploit for better Physics. Then, because it will be so close to such an intense neutrino beam, the near detector will see enormous pile-up; that is over 100 interactions in the detector as once, from both interactions happening inside the detector and those that occur just before, but then enter the detector. Finally, the DUNE ND will be part of a suite of detectors, so additional work is needed to load and process various detectors at once, such that we can reconstruct things cohesively across them all.