Publications associées à Meandering particle bunches and a link between averages of time series of particle counts and higher-order moments

Extensions of Peer Prediction Incentive Mechanisms

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

EPFL2024

Higher-order moments of the elliptic flow distribution in PbPb collisions at √sNN=5.02 TeV

Rakesh Chawla, Andrea Rizzi, Matthias Finger, Federica Legger, Matteo Galli, Sun Hee Kim, Jian Zhao, João Miguel das Neves Duarte, Tagir Aushev, Hua Zhang, Alexis Kalogeropoulos, Yixing Chen, Tian Cheng, Ioannis Papadopoulos, Gabriele Grosso, Valérie Scheurer, Meng Xiao, Qian Wang, Michele Bianco, Varun Sharma, Joao Varela, Sourav Sen, Ashish Sharma, Seungkyu Ha, David Vannerom, Csaba Hajdu, Sanjeev Kumar, Sebastiana Gianì, Kun Shi, Abhisek Datta, Miao Hu, Siyuan Wang, Muhammad Waqas, Anton Petrov, Jian Wang, Yi Zhang, Muhammad Ansar Iqbal, Yong Yang, Xin Sun, Muhammad Ahmad, Donghyun Kim, Matthias Wolf, , , , , , , , , , , , , , , , , , , , , , , , , , ,

On distributional autoregression and iterated transportation

Victor Panaretos, Laya Ghodrati

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Wiley2024

Optimizing Dynamic Aperture Studies with Active Learning

Tatiana Pieloni, Massimo Giovannozzi, Ekaterina Krymova, Mike Seidel, Davide Di Croce

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 ...

Iop Publishing Ltd2024

Energy-Efficient Particle Accelerators for Research

Mike Seidel

Particle accelerators are the drivers for large-scale research infrastructures for particle physics but also for many branches of condensed matter research. The types of accelerator-driven research infrastructures include particle colliders, neutron, muon ...

2024

High power electrostatic beam splitter for a proton beamline

Hua Zhang, Mike Seidel

The High Intensity Proton Accelerator facility (HIPA) delivers a 590 MeV cw (50.6 MHz) proton beam with up to 1.4 MW beam power (2.4 mA) to spallation and meson production targets serving particle physics experiments and material research. The main acceler ...

2024

Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

Frédéric Courbin, Fabio Finelli, Richard Massey, Maurizio Martinelli, Jean-Luc Starck, Gianluca Castignani, Marcello Farina, Austin Chandler Peel, Yi Wang

Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale ...

EDP SCIENCES S A2023

Comparison of Three Imputation Methods for Groundwater Level Timeseries

Andrea Rinaldo

This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead bot ...

MDPI2023

Directional sensitivity of cortical neurons towards TMS induced electric fields

Werner Alfons Hilda Van Geit, Aurélien Tristan Jaquier

We derived computationally efficient average response models of different types of cortical neurons, which are subject to external electric fields from Transcranial Magnetic Stimulation. We used 24 reconstructions of pyramidal cells (PC) from layer 2/3, 24 ...

2023

Design and Development of a Diagnostic System for a Non-Intercepting Direct Measure of the SPIDER Ion Source Beamlet Current

Stable and uniform beams with low divergence are required in particle accelerators; therefore, beyond the accelerated current, measuring the beam current spatial uniformity and stability over time is necessary to assess the beam performance, since these pa ...

MDPI2023

Performance of the MALTA telescope

Edoardo Charbon, Francesco Piro, Abhishek Sharma

MALTA is part of the Depleted Monolithic Active Pixel sensors designed in Tower 180 nm CMOS imaging technology. A custom telescope with six MALTA planes has been developed for test beam campaigns at SPS, CERN, with the ability to host several devices under ...

SPRINGER2023

Development of the self-modulation instability of a relativistic proton bunch in plasma

Patrick Blanchard, Ambrogio Fasoli, Ivo Furno, Samuel Rey-Mermet, Yanis Andrebe, Alban Sublet, Sun Hee Kim, Ana Francisca Leal Silva Soares, Riccardo Agnello, Christine Stollberg, Shuai Liu

Self-modulation is a beam-plasma instability that is useful to drive large-amplitude wakefields with bunches much longer than the plasma skin depth. We present experimental results showing that, when increasing the ratio between the initial transverse size ...

2023

Stochastic particle transport by deep-water irregular breaking waves

Debbie Eeltink

Correct prediction of particle transport by surface waves is crucial in many practical applications such as search and rescue or salvage operations and pollution tracking and clean-up efforts. Recent results by Deike et al. (J. Fluid Mech., vol. 829, 2017, ...

Cambridge University Press2023

Electromagnetic forces in the time domain

Olivier Martin, Karim Achouri, Andrei Kiselev

We look beyond the standard time-average approach and investigate optical forces in the time domain. The formalism is developed for both the Abraham and Minkowski momenta, which appear to converge in the time domain. We unveil an extremely rich - and by fa ...

Optica Publishing Group2022

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Andrea Zanoni

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

Design, prototyping, and thermo-mechanical modelling of a neutron spallation target impacted by high-energy proton-beam pulses in the n_TOF facility at CERN

Raffaele Esposito

This manuscript describes the design work and the R&D activities carried out for the third-generation neutron production target in the neutron Time-Of-Flight (n_TOF) facility at the European Laboratory for Particle Physics (CERN). The facility is composed ...

EPFL2022

Modelling electronic transport in monocrystalline metal oxide gas sensors: from the surface kinetics to the experimental response

Roberto Guarino, Gianluca Piccinini

Gas sensing systems and devices based on metal oxides are widely spreading due to their high performance in terms of sensor response and relatively low costs. Despite several experimental studies, as well as molecular simulations, are available in the lite ...

2022

The exploration process of critical Boltzmann planar maps decorated by a triangular O(n) loop model

Aleksandra Korzhenkova

In this paper we investigate pointed (q, g, n)-Boltzmann loop-decorated maps with loops traversing only inner triangular faces. Using peeling exploration Budd (2018) modified to this setting we show that its law in the non-generic critical phase can be cod ...

IMPA2022

Design of the 590 MeV proton beamline for the proposed TATTOOS isotope production target at PSI

Hong Zhang, Mike Seidel

IMPACT (Isotope and Muon Production with Advanced Cyclotron and Target Technologies) is a proposed initiative envisaged for the high-intensity proton accelerator facility (HIPA) at the Paul Scherrer Institute (PSI). As part of IMPACT, a radioisotope target ...

IOP Publishing2022

Sparsely Observed Functional Time Series: Theory and Applications

Tomas Rubin

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021