End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use lowlevel detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves a ROC-AUC score of 0.975±0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier ROC-AUC score increases to 0.9824±0.0013, serving as the first performance benchmark for these CMS Open Data samples.

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Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data

EPJ Web of Conferences ◽

10.1051/epjconf/202125103057 ◽

2021 ◽

Vol 251 ◽

pp. 03057

Author(s):

Michael Andrews ◽

Bjorn Burkle ◽

Shravan Chaudhari ◽

Davide Di Croce ◽

Sergei Gleyzer ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Top Quark ◽

Learning Algorithms ◽

Open Data ◽

High Energy ◽

Machine Learning Algorithms ◽

Multiple Gpus ◽

Event Identification ◽

End To End

Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports on the implementation of the end-to-end deep learning with the CMS software framework and the scaling of the end-to-end deep learning with multiple GPUs. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation for particle and event identification. We demonstrate the end-to-end implementation on a top quark benchmark and perform studies with various hardware architectures including single and multiple GPUs and Google TPU.

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Exploring End-to-end Deep Learning Applications for Event Classification at CMS

EPJ Web of Conferences ◽

10.1051/epjconf/201921406031 ◽

2019 ◽

Vol 214 ◽

pp. 06031 ◽

Cited By ~ 1

Author(s):

Michael Andrews ◽

Manfred Paulini ◽

Sergei Gleyzer ◽

Barnabas Poczos

Keyword(s):

Deep Learning ◽

Hadron Collider ◽

Learning Algorithms ◽

Open Data ◽

New Physics ◽

Machine Learning Techniques ◽

Detector Response ◽

Event Classification ◽

End To End ◽

High Level

An essential part of new physics searches at the Large Hadron Collider (LHC) at CERN involves event classification, or distinguishing potential signal events from those coming from background processes. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phenomenology. While such techniques have proven useful for simple decays, they are highly dependent on our ability to model all aspects of the phenomenology and detector response. Meanwhile, powerful deep learning algorithms are capable of not only training on high-level features, but of performing feature extraction. In computer vision, convolutional neural networks have become the state-of-the-art for many applications. Motivated by their success, we apply deep learning algorithms to low-level detector data from the 2012 CMS Simulated Open Data to directly learn useful features, in what we call, end-to-end event classification. We demonstrate the power of this approach in the context of a physics search and offer solutions to some of the inherent challenges, such as image construction, image sparsity, combining multiple sub-detectors, and de-correlating the classifier from the search observable, among others.

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Application of a Convolutional Neural Network for image classification for the analysis of collisions in High Energy Physics

EPJ Web of Conferences ◽

10.1051/epjconf/201921406017 ◽

2019 ◽

Vol 214 ◽

pp. 06017 ◽

Cited By ~ 2

Author(s):

Celia Fernández Madrazo ◽

Ignacio Heredia ◽

Lara Lloret ◽

Jesús Marco de Lucas

Keyword(s):

Neural Networks ◽

Deep Learning ◽

High Energy Physics ◽

Open Data ◽

Feedforward Neural Networks ◽

Relevant Information ◽

High Energy ◽

Particle Collisions ◽

Learning Framework ◽

Energy Physics

The application of deep learning techniques using convolutional neural networks for the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of particles and jets, into a single image that captures the relevant information, is proposed. The idea is tested using a well-known deep learning framework on a simulation dataset, including leptonic ttbar events and the corresponding background at 7 TeV from the CMS experiment at LHC, available as Open Data. This initial test shows competitive results when compared to more classical approaches, like those using feedforward neural networks.

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A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas

Remote Sensing ◽

10.3390/rs12193205 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3205

Author(s):

Lifu Chen ◽

Peng Zhang ◽

Jin Xing ◽

Zhenhong Li ◽

Xuemin Xing ◽

...

Keyword(s):

Deep Learning ◽

Domain Knowledge ◽

Contextual Information ◽

Improvement Strategy ◽

Sar Images ◽

Low Level ◽

Multi Scale ◽

Water Detection ◽

Multi Level ◽

End To End

Water detection from Synthetic Aperture Radar (SAR) images has been widely utilized in various applications. However, it remains an open challenge due to the high similarity between water and shadow in SAR images. To address this challenge, a new end-to-end framework based on deep learning has been proposed to automatically classify water and shadow areas in SAR images. This end-to-end framework is mainly composed of three parts, namely, Multi-scale Spatial Feature (MSF) extraction, Multi-Level Selective Attention Network (MLSAN) and the Improvement Strategy (IS). Firstly, the dataset is input to MSF for multi-scale low-level feature extraction via three different methods. Then, these low-level features are fed into the MLSAN network, which contains the Encoder and Decoder. The Encoder aims to generate different levels of features using residual network of 101 layers. The Decoder extracts geospatial contextual information and fuses the multi-level features to generate high-level features that are further optimized by the IS. Finally, the classification is implemented with the Softmax function. We name the proposed framework as MSF-MLSAN, which is trained and tested using millimeter wave SAR datasets. The classification accuracy reaches 0.8382 and 0.9278 for water and shadow, respectively; while the overall Intersection over Union (IoU) is 0.9076. MSF-MLSAN demonstrates the success of integrating SAR domain knowledge and state-of-the-art deep learning techniques.

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