Conveners
Regression, Calibration, and Fast Inference: Calibration
- Jennifer Ngadiuba (FNAL)
- Ines Ochoa (LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)
Regression, Calibration, and Fast Inference: Regression
- Ines Ochoa (LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)
- Jennifer Ngadiuba (FNAL)
Regression, Calibration, and Fast Inference: Fast Inference
- Ines Ochoa (LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)
- Jennifer Ngadiuba (FNAL)
In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the...
Reconstructing the jet transverse momentum ($p_{\rm T}$)is a challenging task, particularly in heavy-ion collisions due to the large fluctuating background from the underlying event. In the recent years, ALICE has developed a novel method to correct jets for this large background using machine learning techniques. This analysis intentionally does not utilize deep learning methods and instead...
The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find new solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momenta at very high energy, where the curvature provided by conceivable...
Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling production spectrum introduces a strong bias toward small energy losses that obfuscates a direct interpretation of the impact of medium effects in the...
Generating large numbers of events efficiently is a major bottleneck for ML projects. As a first step towards a full-fledged event generator for modern GPUs, we investigated different recursive strategies. The GPU implementations are compared to the state-of-the-art CPU codes, showing promise for using these in other pipelines. Finally, we propose baseline implementations for the development...
We investigate the possibility of using Deep Learning algorithms for jet identification in the L1 trigger at HL-LHC. We perform a survey of architectures (MLP, CNN, Graph Networks) and benchmark their performance and resource consumption on FPGAs using a QKeras+hls4ml compression-aware training procedure. We use the HLS4ML jet dataset to compare the results obtained in this study to previous...
The high collision rates at the Large Hadron Collider (LHC) make it impossible to store every single observed interaction. For this reason, only a small subset that passes so-called triggers — which select potentially interesting events — are saved while the remainder is discarded. This makes it difficult to perform searches in regions that are usually ignored by trigger setups, for example at...
