1,785 research outputs found
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder
Model-agnostic anomaly detection is one of the promising approaches in the
search for new beyond the standard model physics. In this paper, we present
Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection
algorithm. We demonstrate a 2x signal efficiency gain compared with traditional
subjettiness-based jet selection. Furthermore, with an eye to the future
deployment to trigger systems, we propose the CLIP-VAE, which reduces the
inference-time cost of anomaly detection by using the KL-divergence loss as the
anomaly score, resulting in a 2x acceleration in latency and reducing the
caching requirement.Comment: 7 pages, 4 figures, accepted at the Machine Learning and the Physical
Sciences Workshop, NeurIPS 202
New Physics Agnostic Selections For New Physics Searches
We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an equivalent search on the original dataset
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. The growing applications of machine
learning algorithms in particle physics for simulation, reconstruction, and
analysis are naturally deployed on such platforms. We demonstrate that the
acceleration of machine learning inference as a web service represents a
heterogeneous computing solution for particle physics experiments that
potentially requires minimal modification to the current computing model. As
examples, we retrain the ResNet-50 convolutional neural network to demonstrate
state-of-the-art performance for top quark jet tagging at the LHC and apply a
ResNet-50 model with transfer learning for neutrino event classification. Using
Project Brainwave by Microsoft to accelerate the ResNet-50 image classification
model, we achieve average inference times of 60 (10) milliseconds with our
experimental physics software framework using Brainwave as a cloud (edge or
on-premises) service, representing an improvement by a factor of approximately
30 (175) in model inference latency over traditional CPU inference in current
experimental hardware. A single FPGA service accessed by many CPUs achieves a
throughput of 600--700 inferences per second using an image batch of one,
comparable to large batch-size GPU throughput and significantly better than
small batch-size GPU throughput. Deployed as an edge or cloud service for the
particle physics computing model, coprocessor accelerators can have a higher
duty cycle and are potentially much more cost-effective.Comment: 16 pages, 14 figures, 2 table
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. One technique to limit model size is quantization, which implies
using fewer bits to represent weights and biases. Such an approach usually
results in a decline in performance. Here, we introduce a method for designing
optimally heterogeneously quantized versions of deep neural network models for
minimum-energy, high-accuracy, nanosecond inference and fully automated
deployment on chip. With a per-layer, per-parameter type automatic quantization
procedure, sampling from a wide range of quantizers, model energy consumption
and size are minimized while high accuracy is maintained. This is crucial for
the event selection procedure in proton-proton collisions at the CERN Large
Hadron Collider, where resources are strictly limited and a latency of
s is required. Nanosecond inference and a resource
consumption reduced by a factor of 50 when implemented on field-programmable
gate array hardware are achieved
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. By extending the
hls4ml library, we demonstrate an inference latency of s using
convolutional architectures, targeting microsecond latency applications like
those at the CERN Large Hadron Collider. Considering benchmark models trained
on the Street View House Numbers Dataset, we demonstrate various methods for
model compression in order to fit the computational constraints of a typical
FPGA device used in trigger and data acquisition systems of particle detectors.
In particular, we discuss pruning and quantization-aware training, and
demonstrate how resource utilization can be significantly reduced with little
to no loss in model accuracy. We show that the FPGA critical resource
consumption can be reduced by 97% with zero loss in model accuracy, and by 99%
when tolerating a 6% accuracy degradation.Comment: 18 pages, 18 figures, 4 table
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. The two complementary FPGA designs are
based on OpenCL, a framework for writing programs that execute across
heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for
neural network to firmware conversion. We evaluate and compare the resource
usage, latency, and tracking performance of our implementations based on a
benchmark dataset. We find a considerable speedup over CPU-based execution is
possible, potentially enabling such algorithms to be used effectively in future
computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron
Collider.Comment: 8 pages, 4 figures, To appear in Third Workshop on Machine Learning
and the Physical Sciences (NeurIPS 2020
Search for supersymmetry in pp collisions at = 13 tev in the single-lepton final state using the sum of masses of large-radius jets
Results are reported from a search for supersymmetric particles in proton-proton collisions in the final state with a single lepton, multiple jets, including at least one b-tagged jet, and large missing transverse momentum. The search uses a sample of proton-proton collision data at = 13âTeV recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 35.9ââ. The observed event yields in the signal regions are consistent with those expected from standard model backgrounds. The results are interpreted in the context of simplified models of supersymmetry involving gluino pair production, with gluino decay into either on- or off-mass-shell top squarks. Assuming that the top squarks decay into a top quark plus a stable, weakly interacting neutralino, scenarios with gluino masses up to about 1.9 TeV are excluded at 95% confidence level for neutralino masses up to about 1 TeV
Search for dark matter produced in association with heavy-flavor quark pairs in proton-proton collisions at = 13 TeV
A search is presented for an excess of events with heavy-flavor quark pairs ( and ) and a large imbalance in transverse momentum in data from protonâproton collisions at a center-of-mass energy of 13TeV. The data correspond to an integrated luminosity of 2.2 collected with the CMS detector at the CERN LHC. No deviations are observed with respect to standard model predictions. The results are used in the first interpretation of dark matter production in and final states in a simplified model. This analysis is also the first to perform a statistical combination of searches for dark matter produced with different heavy-flavor final states. The combination provides exclusions that are stronger than those achieved with individual heavy-flavor final states
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