Search Results for author:
Found 46 papers, 27 papers with code
Towards Optimal Compression: Joint Pruning and Quantization
Compression of deep neural networks has become a necessary stage for optimizing model inference on resource-constrained hardware.
Quantum anomaly detection in the latent space of proton collision events at the LHC
The designed quantum anomaly detection models, namely an unsupervised kernel machine and two clustering algorithms, are trained to find new-physics events in the latent representation of LHC data produced by the autoencoder.
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC).
On the Evaluation of Generative Models in High Energy Physics
There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP).
FIT: A Metric for Model Sensitivity
This response is non-linear and heterogeneous throughout the network.
LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors
In addition, we introduce an outer-product based matrix multiplication approach which is enhanced by the strength reduction for low latency design.
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving.
Learning new physics efficiently with nonparametric methods
We present a machine learning approach for model-independent new physics searches.
End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector.
Machine Learning for Particle Flow Reconstruction at CMS
The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy.
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC.
Lightweight Jet Reconstruction and Identification as an Object Detection Task
We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point.
Autoencoders for Semivisible Jet Detection
The experimental signature is characterised by the presence of reconstructed missing momentum collinear with the visible components of the jets.
Explaining machine-learned particle-flow reconstruction
The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from different subdetectors.
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
We explore the use of graph-based autoencoders, which operate on jets in their "particle cloud" representations and can leverage the interdependencies among the particles within a jet, for such tasks.
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
The recurrent autoencoder outperforms other autoencoders based on different architectures.
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
The proposed approach has been evaluated based on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA.
Particle Cloud Generation with Message Passing Generative Adversarial Networks
We propose JetNet as a novel point-cloud-style dataset for the ML community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models.
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile.
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum.
Fast convolutional neural networks on FPGAs with hls4ml
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs.
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks.
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC).
Anomaly Detection With Conditional Variational Autoencoders
Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question.
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets.
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering.
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption.
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider.
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We discuss the trade-off between model accuracy and resource consumption.
Fast inference of Boosted Decision Trees in FPGAs for particle physics
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process.
Learning Multivariate New Physics
It is designed to be sensitive to small discrepancies that arise in datasets dominated by the reference model.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.
The DNNLikelihood: enhancing likelihood distribution with Deep Learning
We introduce the DNNLikelihood, a novel framework to easily encode, through Deep Neural Networks (DNN), the full experimental information contained in complicated likelihood functions (LFs).
High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics High Energy Physics - Experiment Data Analysis, Statistics and Probability
$\texttt{HEPfit}$: a Code for the Combination of Indirect and Direct Constraints on High Energy Physics Models
$\texttt{HEPfit}$ is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to $\textit{i)}$ fit the model parameters to a given set of experimental observables; $\textit{ii)}$ obtain predictions for observables.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
We develop a jet identification algorithm based on an interaction network, designed to identify high-momentum Higgs bosons decaying to bottom quark-antiquark pairs, distinguish them from ordinary jets originating from the hadronization of quarks and gluons.
High Energy Physics - Experiment High Energy Physics - Phenomenology
JEDI-net: a jet identification algorithm based on interaction networks
We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons.
High Energy Physics - Experiment High Energy Physics - Phenomenology
FPGA-accelerated machine learning inference as a service for particle physics computing
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains.
Data Analysis, Statistics and Probability High Energy Physics - Experiment Computational Physics Instrumentation and Detectors
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction.
LHC analysis-specific datasets with Generative Adversarial Networks
Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost.
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events.
Pileup mitigation at the Large Hadron Collider with Graph Neural Networks
At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup.
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment.
Topology classification with deep learning to improve real-time event selection at the LHC
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider.
Fast inference of deep neural networks in FPGAs for particle physics
For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.
