Conveners
Lightening talks
- Maria Dadarlat
As gravitational-wave (GW) detectors become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with GWs does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up...
At the LHC, the FPGA-based real-time data filter system that rapidly decides which collision events to record, known as the level-1 trigger, requires small models because of the low latency budget and other computing resource constraints. To enhance the sensitivity to unknown new physics, we want to put generic anomaly detection algorithms into the trigger. Past research suggests that graph...
Many high-energy physics analyses rely on various machine learning models for both event reconstruction and signal/background discrimination. One of the major sources of systematic uncertainty in these analyses is due to residual mismodelling in the detailed simulation samples used to train these algorithms. In this work, we will discuss a novel approach to correcting for these systematic...
The High Granularity Calorimeter (HGCAL) is part of the High Luminosity upgrade of the CMS detector at the Large Hadron Collider (HL-LHC). For the trigger primitive generation of the 6 million channels in this detector, data compression at the front end may be accomplished by using deep-learning techniques using an on-ASICs network. The Endcap Trigger Concentrator (ECON-T) ASIC foresees an...
The Large Hadron Collider has recently started Run 3, which means protons are being collided at an astonishing energy of 13.6 TeV. Within the LHC is the CMS detector. Moreover, the Level one trigger is an integral part of the CMS detector and in many ways is considered the first responder of the High Level Trigger system. Its job is to make important initial cuts to the massive amounts of...
The transformer become widely use for the Natural language processing (NLP) task. Besides the NLP tasks, the transformer could be used to analyze any other time series signal processing data, such as images prediction, sensor detection, or glitch detection.
However, most of studies on transformer were implemented on GPU. The implementation of transformer on the FPGA was quite undiscovered. In...
