Speaker
Description
DIPZ is a machine learning algorithm aiming to re-purpose the Deep Impact Parameter Sets (DIPS) jet-flavour taggers to instead regress the jet’s origin vertex position along the beam-line axis. Deployed at the ATLAS High Level Trigger (HLT), the DIPZ labels of each jet in an event are then used in an HLT jet algorithm to construct an event-wide likelihood-based discriminant variable (MLPL), which is used to select events compatible with targeted multi-jet signature selection. This is an HLT algorithm that takes superROI tracking information at the pre-selection step as inputs (prior to full-scan tracking) and performs fast rejection of jets from pile-up. The main goal for Run 3 is to reduce input to full scan tracking in an attempt to reduce the CPU consumption in the HLT while maintaining acceptable event rates and not compromising on signal efficiency for multi-jet signatures. This approach is particularly promising for the HL-LHC era, where the ability to efficiently reject jets from an overwhelming number of pile-up vertices will be crucial for maintaining manageable trigger rates and sustaining high efficiency for targeted signals relevant to the ATLAS physics programme.
| Would you like to be considered for an oral presentation? | Yes |
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