Related papers: Quark jet versus gluon jet: fully-connected neural…
This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level…
Classical artificial neural networks have witnessed widespread successes in machine-learning applications. Here, we propose fermion neural networks (FNNs) whose physical properties, such as local density of states or conditional…
Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images. Some researchers have struggled to enhance the parameters' efficiency using grouped convolution. However, the relation between…
Engineering complex systems (aircraft, buildings, vehicles) requires coordinating geometric and performance couplings across subsystems. As generative models proliferate for specialized domains, a key research gap is how to coordinate…
Two suggested models of jet fragmentation in a quark-gluon plasma have been tested, combined and further developed. This has been done by generating hard processes in proton-proton collisions at $\sqrt{s}=2.76$ GeV in PYTHIA 8 with a…
Intrusion detection in unmanned-aerial-vehicle (UAV) swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation corpus that covers five attack types, we benchmark three…
We present a strategy to quantify the performance of jet definitions in kinematic reconstruction tasks. It is designed to make use exclusively of physical observables, in contrast to previous techniques which often used unphysical Monte…
The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society. This, however, causes an ever-increasing number of reported malicious or accidental incidents,…
A simultaneous measurement of 25 substructure observables is presented using large-radius jets with high transverse momentum from proton-proton collisions at $\sqrt{s}$ = 13 TeV. The measurement is carried out on dijet events and…
Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show how decorrelated taggers would break down when the most…
In machine learning for fluid mechanics, fully-connected neural network (FNN) only uses the local features for modelling, while the convolutional neural network (CNN) cannot be applied to data on structured/unstructured mesh. In order to…
We study procedures for discriminating combinatorial jets in a high background environment, such as a heavy ion collision, from signal jets arising from a hard-scattering. We investigate a population of jets clustered from a combined…
A measurement of the substructure of bottom quark jets (b jets) in proton-proton (pp) collisions is presented. The measurement uses data collected in pp collisions at $\sqrt{s}$ = 5.02 TeV, with a low number of simultaneous interactions per…
Jet shapes are weighted sums over the four-momenta of the constituents of a jet and reveal details of its internal structure, potentially allowing discrimination of its partonic origin. In this work we make predictions for quark and gluon…
Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples…
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic…
Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between…
The trend towards higher resolution remote sensing imagery facilitates a transition from land-use classification to object-level scene understanding. Rather than relying purely on spectral content, appearance-based image features come into…
Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional…
We study the issue of separating hadronic jets that contain bottom quarks ($b$-jets) from jets featuring light partons only. We develop a novel approach to $b$-tagging that exploits the application of QCD-inspired jet substructure…