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To detect tracks of charged particles close to the interaction point in high energy physics experiments of the next generation colliders, hybrid pixel detectors, in which sensor and read-out IC are separate entities, constitute the present…

Support vector machines (SVMs) are special kernel based methods and belong to the most successful learning methods since more than a decade. SVMs can informally be described as a kind of regularized M-estimators for functions and have…

The Large Hadron Collider (LHC) experiments ATLAS and CMS have established hybrid pixel detectors as the instrument of choice for particle tracking and vertexing in high rate and radiation environments, as they operate close to the LHC…

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider.…

Multivariate data analysis techniques have the potential to improve physics analyses in many ways. The common classification problem of signal/background discrimination is one example. The Support Vector Machine learning algorithm is a…

In order to fully exploit the physics potential of the future high energy e+e- linear collider, a Vertex Tracker able to provide particle track extrapolation with very high resolution is needed. Hybrid Si pixel sensors are an attractive…

Interest in many-core architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of many-core devices when applied to a typical HEP online task: the…

Tracking detectors are of vital importance for collider-based high energy physics (HEP) experiments. The primary purpose of tracking detectors is the precise reconstruction of charged particle trajectories and the reconstruction of…

For its physics program with a high-intensity hadron beam of up to 2e7 particles/s, the COMPASS experiment at CERN requires tracking of charged particles scattered by very small angles with respect to the incident beam direction. While good…

Mainly due to their outstanding performance the position sensitive silicon detectors are widely used in the tracking systems of High Energy Physics experiments such as the ALICE, ATLAS, CMS and LHCb at LHC, the world's largest particle…

The support vector machines (SVM) is one of the most widely used and practical optimization based classification models in machine learning because of its interpretability and flexibility to produce high quality results. However, the big…

Quantum sensing of meV-scale scattering and absorption of impinging particles with electrons in solid state detectors is a challenging technological advancement with the potential to enable breakthroughs in quantum information applications…

This report reviews current trends in the R&D of semiconductor pixellated sensors for vertex tracking and radiation imaging. It identifies requirements of future HEP experiments at colliders, needed technological breakthroughs and…

Support Vector Machines (SVMs) with various kernels have played dominant role in machine learning for many years, finding numerous applications. Although they have many attractive features interpretation of their solutions is quite…

The e+e- linear collider physics programme sets highly demanding requirements on the accurate determination of charged particle trajectories close to their production point. A new generation of Vertex Trackers, based on different…

Reconstructing charged particle tracks is a fundamental task in modern collider experiments. The unprecedented particle multiplicities expected at the High-Luminosity Large Hadron Collider (HL-LHC) pose significant challenges for track…

The support vector machines (SVM) algorithm is a popular classification technique in data mining and machine learning. In this paper, we propose a distributed SVM algorithm and demonstrate its use in a number of applications. The algorithm…

This paper describes an innovative way to optimize a multivariate classifier, in particular a Support Vector Machine algorithm, on a problem characterized by a biased training sample. This is possible thanks to the feedback of a…

Kernel-based support vector machines (SVMs) are supervised machine learning algorithms for classification and regression problems. We introduce a method to train SVMs on a D-Wave 2000Q quantum annealer and study its performance in…

Van der Waals (vdW) semiconductors are attractive for highly scaled devices and heterogeneous integration since they can be isolated into self-passivated, two-dimensional (2D) layers that enable superior electrostatic control. These…