1,734 research outputs found
Pileup Mitigation with Machine Learning (PUMML)
Pileup involves the contamination of the energy distribution arising from the
primary collision of interest (leading vertex) by radiation from soft
collisions (pileup). We develop a new technique for removing this contamination
using machine learning and convolutional neural networks. The network takes as
input the energy distribution of charged leading vertex particles, charged
pileup particles, and all neutral particles and outputs the energy distribution
of particles coming from leading vertex alone. The PUMML algorithm performs
remarkably well at eliminating pileup distortion on a wide range of simple and
complex jet observables. We test the robustness of the algorithm in a number of
ways and discuss how the network can be trained directly on data.Comment: 20 pages, 8 figures, 2 tables. Updated to JHEP versio
Pulling Out All the Tops with Computer Vision and Deep Learning
We apply computer vision with deep learning -- in the form of a convolutional
neural network (CNN) -- to build a highly effective boosted top tagger.
Previous work (the "DeepTop" tagger of Kasieczka et al) has shown that a
CNN-based top tagger can achieve comparable performance to state-of-the-art
conventional top taggers based on high-level inputs. Here, we introduce a
number of improvements to the DeepTop tagger, including architecture, training,
image preprocessing, sample size and color pixels. Our final CNN top tagger
outperforms BDTs based on high-level inputs by a factor of --3 or more
in background rejection, over a wide range of tagging efficiencies and fiducial
jet selections. As reference points, we achieve a QCD background rejection
factor of 500 (60) at 50\% top tagging efficiency for fully-merged (non-merged)
top jets with in the 800--900 GeV (350--450 GeV) range. Our CNN can also
be straightforwardly extended to the classification of other types of jets, and
the lessons learned here may be useful to others designing their own deep NNs
for LHC applications.Comment: 33 pages, 11 figure
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
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