Identification of hadronic tau lepton decays using a deep neural network

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The polarization of tau leptons is measured using leptonic and hadronic tau lepton decays in Z -> tau(+)tau(-) events in proton-proton collisions at root s = 13 TeV recorded by CMS at the CERN LHC with an integrated luminosity of 36.3 fb(-1). The measured ...

The first search for scalar leptoquarks produced in z-lepton-quark collisions is presented. It is based on a set of proton-proton collision data recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV corresponding to an integrated l ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

A large variety of new physics models suggest that the rates for lepton flavour violating

b

-hadron decays may be much higher than predicted in the Standard Model, which leads to a high interest in the search for such decays. This thesis presents the se ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

A search is presented for new Higgs bosons in proton-proton (pp) collision events in which a same-sign top quark pair is produced in association with a jet, via the pp -> tH/ A -> tt (c) over bar and pp -> tH/ A -> tt

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

The measurement of Z boson production is presented as a method to determine the integrated luminosity of CMS data sets. The analysis uses proton-proton collision data, recorded by the CMS experiment at the CERN LHC in 2017 at a center-of-mass energy of 13 ...

A measurement of the Higgs boson (H) production via vector boson fusion (VBF) and its decay into a bottom quark-antiquark pair (b (b) over bar) is presented using proton-proton collision data recorded by the CMS experiment at root s = 13TeV and correspondi ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

The azimuthal anisotropy of gamma(1S) mesons in high-multiplicity proton-lead collisions is studied using data collected by the CMS experiment at a nucleon-nucleon center-of-mass energy of 8.16 TeV. The gamma(1S) mesons are reconstructed using their dimuon ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

Identification of hadronic tau lepton decays using a deep neural network

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Measurement of the τ lepton polarization in Z boson decays in proton-proton collisions at √s=13 TeV

Search for Scalar Leptoquarks Produced via τ-Lepton-Quark Scattering in pp Collisions at ffiffi s p=13 TeV

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Optimizing Dynamic Aperture Studies with Active Learning

Search for the lepton flavour violating decays B0→e±μ∓ and B0s→e±μ∓ with LHCb Run 2 data

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Safe Deep Neural Networks

Search for new Higgs bosons via same-sign top quark pair production in association with a jet in proton-proton collisions at √s=13 TeV

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Random matrix methods for high-dimensional machine learning models

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Luminosity determination using Z boson production at the CMS experiment

Measurement of the Higgs boson production via vector boson fusion and its decay into bottom quarks in proton-proton collisions at √s=13 TeV

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Study of azimuthal anisotropy of γ(1S) mesons in pPb collisions at √SNN=8.16 TeV

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications