Application of machine learning techniques at the CERN Large Hadron Collider

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Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

Non-invasive beam diagnostics are essential for operating modern high-intensity accelerators such as the Large Hadron Collider (LHC). Among these diagnostics, Schottky monitors stand out for their ability to measure tune, chromaticity, momentum spread, and ...

A search for heavy neutral gauge bosons (Z ') decaying into a pair of tau leptons is performed in proton-proton collisions at root s = 13 TeV at the CERN LHC. The data were collected with the CMS detector and correspond to an integrated luminosity of 138 f ...

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

The TOTEM Roman pot detectors are used to reconstruct the transverse momentum of scattered protons and to estimate the transverse location of the primary interaction. This paper presents new methods of track reconstruction, measurements of strip-level dete ...

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

Slope deformation poses significant risks to infrastructure, ecosystems, and human safety, making early and accurate predictions essential for mitigating slope failures and landslides. In this study, we propose a novel approach that integrates a fractional ...

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

To retrieve Surface Solar Radiation (SSR) from satellite images, a baseline reflectance of the observed ground in clear-sky conditions, also called background reflectance, is necessary to distinguish atmospheric absorption and scattering effects from surfa ...

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

The first measurement of the CP asymmetry of the decay rate (A(CP)) and the CP average (Sigma A(FB)) and CP asymmetry (Delta A(FB)) of the forward-backward asymmetry in the muon system of Lambda(+)(c) -> p mu(+) mu(-) decays is reported. The measurement is ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

The branching fraction of the decay B + → ψ ( 2 S ) ϕ ( 1020 ) K + , relative to the topologically similar decay B + → J / ψ ϕ ( 1020 ) K + , is measured using proton-proton collision data collected by the LHCb experiment at center-of-mass energies of 7, 8 ...

Interactive simulations enhance science education and foster inquiry skills, but their open-ended nature can be cognitively overloading. While adaptive systems offer timely support, research on predicting conceptual understanding in these environments is l ...

Overview This dataset provides high-resolution, georeferenced vehicle trajectories collected via drone footage at three roundabouts located in the municipalities of Frick and Laufenburg, Canton of Aargau, Switzerland. The data were collected as part of a ...

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

Application of machine learning techniques at the CERN Large Hadron Collider

Chattez avec Graph Search

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Schottky Spectrum Modelling for High Intensity Bunched Particle Beams and Experimental Applications

Search for Heavy Neutral Resonances Decaying to Tau Lepton Pairs in Proton-proton Collisions at √s=13 Tev

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Proton reconstruction with the TOTEM Roman pot detectors for high-β∗ LHC data

Image steganalysis using active learning and hyperparameter optimization

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and K-Means Clustering

On the added value of sequential deep learning for the upscaling of evapotranspiration

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Search for Resonance-enhanced Cp and Angular Asymmetries in the Λc+ → Pμ+ Μ- Decay at Lhcb

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Branching fraction measurement of the decay B+→ψ(2S)ϕ(1020)K+

One Code to Predict Them All: Universal Encoding for Inquiry Modeling

Vehicle Trajectory Dataset from Drone-Collected Data at Three Swiss Roundabouts

Enhancing skin lesion classification: a CNN approach with human baseline comparison