Conveners
Fast ML
- Melissa Quinnan (Univ. of California San Diego (US))
Fast ML
- Jannicke Pearkes (University of Colorado Boulder (US))
We describe a pile-up suppression algorithm for the ATLAS Global Trigger, using a convolutional neural network (CNN) architecture. The CNN operates on cell towers and exploits both shower topology and $E_T$ to correct for the contribution of pile-up. The algorithm is optimised for firmware deployment and demonstrates high throughput and low resource usage. The small size of the input and...
Machine learning has opened new possibilities for detecting anomalous signatures in high-energy physics data. While most approaches have focused on offline use, there is growing interest in applying these tools directly at the trigger level to enhance discovery potential. In this work, we present a novel framework for autonomous triggering that not only detects anomalous patterns in real time...
At ATLAS and CMS, the rate of proton collisions far exceeds the rate at which data can be recorded. A real-time event selection process, or trigger, is needed to ensure that the data recorded contains the highest possible discovery potential. In the absence of hoped-for anomalies such as SUSY, there is increasing motivation to develop dedicated anomaly detection triggers. A common approach is...
We present an FPGA implementation of a Normalizing Flow (NF) for Anomaly Detection (AD) of new physics in realistic high-rate trigger systems of large HEP experiments. To the best of our knowledge, this marks the first operation of such an algorithm on FPGA, demonstrating anomaly detection performance and latency comparable to existing FPGA-based ML solutions.
We train a continuous NF model...
In high-energy physics, the increasing luminosity and detector granularity at the Large Hadron Collider are driving the need for more efficient data processing solutions. Machine Learning has emerged as a promising tool for reconstructing charged particle tracks, due to its potentially linear computational scaling with detector hits. The recent implementation of a graph neural network-based...
We present a method to suppress pileup and calibrate hadronic jet energy at L1 triggers using boosted decision trees for regression and classification. The fwX platform is used for implementation of BDTs on FPGA within the necessary timing and resource constraints. The in-situ pileup suppression can improve trigger performance in the high pileup environment of the HL-LHC.
Transformers are highly effective at capturing both global and local correlations in high-energy particle collisions, yet deploying them in high-data-throughput settings such as the CERN LHC is challenging. The quadratic complexity of full-attention models drives substantial resource usage and increases inference latency, while low-rank approximations like Linformer can degrade classification...
Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition...
The absence of beyond-Standard-Model physics discoveries at the LHC suggests that new physics may evade conventional trigger strategies. The existing ATLAS triggers are required to control data collection rates with high energy thresholds and target signal topologies specific to only certain models. Unsupervised machine learning enables the use of anomaly detection, presenting a unique...
The upcoming high-luminosity upgrade to the LHC will involve a dramatic increase in the number of simultaneous collisions delivered to the Compact Muon Solenoid (CMS) experiment. To deal with the increased number of simultaneous interactions per bunch crossing as well as the radiation damage to the current crystal ECAL endcaps, a radiation-hard high-granularity calorimeter (HGCAL) will be...
