Darkjets
Project description
For experiments at the Large Hadron Collider (LHC) at CERN, proton-proton collisions occur up to 30 million times per second. One cannot record all information related to each of these collisions, since the size of each “event” can surpass 1 MB. Experiment therefore select only a subset of these collision events, record them to storage and then analyze them afterwards.
Novel techniques are needed in order to make the most of data that is not selected and would otherwise be discarded. The DARKJETS project delivers such a technique for the ATLAS experiment, called Trigger-object Level Analysis (TLA). In this technique, higher-level insight is obtained from a fast data analysis done in milliseconds, so that only a small subset of the information can be stored for each event. This greatly reduces the event size and allows for a much larger dataset to be recorded for e.g. searches for new physics phenomena. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No GA679305)”
Year
Team
Lund University, Faculty of Sciences:
- Caterina Doglioni, Senior Lecturer, specialist in data selection and data analysis, particle physics
- William Kalderon (now at Brookhaven National Lab) and Jannik Geisen, Postdocs, specialist in data selection and data analysis, particle physics
- Oxana Smirnova, Senior Lecturer, specialist in scientific computing and data processing
- Florido Paganelli, Researcher, computer scientist, system expert
- Eva Hansen, Eric Corrigan, PhD students
Core deliverables
- Novel technique for the ATLAS detector to record more data than traditional techniques in searches for new particle
- Commissioning of FPGA-based board for event selection in the upcoming LHC Run
- Scientific and technical peer-reviewed publications
Total budget
Collaborations
- Lund University
- Ohio State University
- Heidelberg University
- University of Oregon
- University of Geneva
- CERN