Conveners
Flash talks / poster session: Flash talks and poster session in Auditorium Mezzanine
- Thea Aarrestad (ETH Zurich (CH))
Description
All contributors will present a poster, which they will need to print by themselves. Poster holders will be available on Monday at the mezzanine. Please also upload the pdf of the poster in the agenda.
Some contributors have also asked for a flash talk. The duration of the flash talks is 3 mins. Please upload the flash talk contribution by Monday morning 11am. Thanks!
The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows us to look for rare deviations that can be due to new phenomena not previously observed. The CMS Collaboration is investigating how novel machine learning algorithms, specifically Normalizing Flows and...
Optimizing the inference of Graph Neural Networks (GNNs) for track finding is crucial for enhancing the computing performance of particle collision event reconstruction. Track finding involves identifying and reconstructing the paths of particles from complex, noisy detector data. By leveraging GNNs, we can model the relationships between detector hits as a graph, where nodes represent hits...
In recent years, the demand for real-time machine learning (ML)-based computing solutions has driven the rapid growth of edge computing. The adopted hardware must strike a delicate balance by providing sufficient computational power to meet stringent real-time constraints while minimizing energy consumption. General-purpose graphics processing units (GPGPU) are a commonly adopted solution to...
Current Imaging Atmospheric Cherenkov Telescopes use combined analog and digital electronics for their trigger systems, implementing simple but fast algorithms. Such trigger techniques are used due to high data rates and strict timing requirements. In recent years, in the context of a possible upgraded camera for the Large-Sized Telescopes (LSTs) of the Cherenkov Telescope Array (CTA) based on...
Effective pile-up suppression, particle ID and clustering are essential for maximising the physics performance of the Phase-II Global trigger in ATLAS. To address this, we train both convolutional and DeepSets neural networks to exploit cluster topologies to accurately predict calorimeter cell labels, and benchmark performance against existing approaches. We optimise the networks for firmware...
The NGT WP 2.2 aims to improve the robustness of the L0 muon trigger system and include additional trigger strategies for non-pointing signatures from decay of long-lived exotic particles implementing novel trigger strategies in firmware. Here we present the goals of the project, as well as the needs and requirements from available tools such as HLS4ML.
We present an interpretable implementation of the autoencoding algorithm, used as an anomaly detector, built with a forest of deep decision trees on FPGA, field programmable gate arrays. Scenarios at the Large Hadron Collider are considered for which the autoencoder is trained using the Standard Model. The design is then deployed for anomaly detection of unknown processes. The inference is...
Irradiation facilities, infrastructures for assessing devices and material radiation-hardness, face a variety of challenges, from the management of the experiment-selection process to the monitoring of the beam quality they need. While addressing vastly different issues, the answers may be found in carefully engineered Machine Learning and Artificial Intelligence (AI) solutions.
The...
This work presents a new search for soft dark photons from charm decays, made possible by the novel real-time analysis (RTA) capabilities of the upgraded LHCb detector. The challenge consists in finding a peak on top of an irreducible non-resonant background of several kHz. In LHC Run 3, LHCb can read out the entire detector in real time (at 30 MHz) and filter interesting events through a...
One of the main goals of the LHCb experiment is to study charge-parity violation by looking at the decays of the large variety of beauty mesons created in pp collisions at LHC. Such studies are particularly challenging in the presence of $B$$-\overline{B}$ oscillations as the $B$ meson flavour at production time might be different from the flavour at its decay time.
Flavour Tagging...
The next decade will see an order of magnitude increase in data collected by high-energy physics experiments, driven by the High-Luminosity LHC (HL-LHC). The reconstruction of charged particle trajectories (tracks) has always been a critical part of offline data processing pipelines. The complexity of HL-LHC data will however increasingly mandate track finding in all stages of an experiment's...
Three machine learning models are used to perform jet origin classification.
These models are optimized for deployment on a field-programmable gate array device.
In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm.
Moreover, the models proposed here are designed to work on the type of data and under the foreseen...
We address the challenge of compressing a sequence of models for deployment on computing- and memory-constrained devices. This task differs from single model compression, as the decision to apply compression schemes either independently or jointly across all sub-networks introduces a new degree of freedom. We evaluate the performance of pruning and quantization techniques for model compression...
We explore the implications of copyright on the composition, quality, and, therefore, inherent bias of AI training datasets. We study the example of LAION5B, a dataset of 5 billion images from the web, which is widely used in research and commercial applications of ML, including generative AI. We document the extent to which images in this dataset have a clear license status and to which...
