Related papers: ILD MC production for detector optimization
The efficiency of Hamiltonian Monte Carlo (HMC) can suffer when sampling a distribution with a wide range of length scales, because the small step sizes needed for stability in high-curvature regions are inefficient elsewhere. To address…
The design of the positron source for the International Linear Collider (ILC) is still under consideration. The baseline design plans to use the electron beam for the positron production before it goes to the IP. The high-energy electrons…
The proposed Next Linear Collider (NLC) contains linac systems operating at L,S,C, and X band. This paper describes a wideband modular low-level RF (LLRF) system applicable for all NLC pulsed RF systems. High speed digital IF techniques are…
Sequential Monte Carlo (SMC) methods have recently shown successful results for conditional sampling of generative diffusion models. In this paper we propose a new diffusion posterior SMC sampler achieving improved statistical efficiencies,…
Metal hydrides are promising candidates for hydrogen storage applications. From a materials discovery perspective, an accurate, efficient computational workflow is urgently required that can rapidly analyze/predict thermodynamic properties…
The Multilevel Monte Carlo (MLMC) approach usually works well when estimating the expected value of a quantity which is a Lipschitz function of intermediate quantities, but if it is a discontinuous function it can lead to a much slower…
All the scientific instruments of the Italian National Telescope "Galileo" (TNG). as well as the tracking systems and the Shack-Hartmann wavefront systems use CCDs as detectors. The CCD procurement is crucial to optimize the instruments…
In this paper we report on LCG Monte Carlo Data Base (MCDB) and software which has been developed to operate MCDB. The main purpose of the LCG MCDB project is to provide a storage and documentation system for sophisticated event samples…
Markov chain Monte Carlo (MCMC) produces a correlated sample for estimating expectations with respect to a target distribution. A fundamental question is when should sampling stop so that we have good estimates of the desired quantities?…
Monte Carlo (MC) sampling algorithms are an extremely widely-used technique to estimate expectations of functions f(x), especially in high dimensions. Control variates are a very powerful technique to reduce the error of such estimates, but…
Efficient sampling of two-dimensional statistical physics systems remains a central challenge in computational statistical physics. Traditional Markov chain Monte Carlo (MCMC) methods, including cluster algorithms, provide only partial…
Data-Driven Product Development (DDPD) leverages data to learn the relationship between product design specifications and resulting properties. To discover improved designs, we train a neural network on past experiments and apply Projected…
We discuss the present status of microscopic models for RHIC, with an emphasis on models being realized via the Monte Carlo technique. This review is to a large extent based on the OSCAR3 workshop, where general concepts and new trends in…
Alignment of the ILC main linac will be more critical than for any currently existing accelerators due to its long length and the ultra low emittance required. There are several techniques for measuring the ILC reference network; in this…
We propose a Multi-level Monte Carlo technique to accelerate Monte Carlo sampling for approximation of properties of materials with random defects. The computational efficiency is investigated on test problems given by tight-binding models…
Rate-dependent effects in the electronics used to instrument the tagger focal plane at the MAX IV Laboratory were recently investigated using the novel approach of Monte Carlo simulation to allow for normalization of high-rate experimental…
During 1996, the SLD collaboration completed construction and began operation of a new charge-coupled device (CCD) vertex detector (VXD3). Since then, its performance has been studied in detail and a new topological vertexing technique has…
Sampling from high-dimensional distributions has wide applications in data science and machine learning but poses significant computational challenges. We introduce Subspace Langevin Monte Carlo (SLMC), a novel and efficient sampling method…
Modified Hamiltonian Monte Carlo (MHMC) methods combine the ideas behind two popular sampling approaches: Hamiltonian Monte Carlo (HMC) and importance sampling. As in the HMC case, the bulk of the computational cost of MHMC algorithms lies…
Radiation damage significantly impacts the performance of silicon tracking detectors in Large Hadron Collider (LHC) experiments such as ATLAS and CMS, with signal reduction being the most critical effect; adjusting sensor bias voltage and…