scholarly journals Constraining the parameters of high-dimensional models with active learning

2019 ◽  
Vol 79 (11) ◽  
Author(s):  
Sascha Caron ◽  
Tom Heskes ◽  
Sydney Otten ◽  
Bob Stienen
Keyword(s):  
Active Learning ◽  
Particle Physics ◽  
High Energy Physics ◽  
High Energy ◽  
Physical Models ◽  
High Dimensional ◽  
Physical Parameters ◽  
Model Parameters ◽  
Dimensional Parameter ◽  
Parameter Spaces

AbstractConstraining the parameters of physical models with $$>5-10$$>5-10 parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of computational resources. The commonly used solution of reducing the number of relevant physical parameters hampers the generality of the results. In this paper we show that this problem can be alleviated by the use of active learning. We illustrate this with examples from high energy physics, a field where simulations are often expensive and parameter spaces are high-dimensional. We show that the active learning techniques query-by-committee and query-by-dropout-committee allow for the identification of model points in interesting regions of high-dimensional parameter spaces (e.g. around decision boundaries). This makes it possible to constrain model parameters more efficiently than is currently done with the most common sampling algorithms and to train better performing machine learning models on the same amount of data. Code implementing the experiments in this paper can be found on GitHub "Image missing"

scholarly journals The standard cosmological model

2006 ◽  
Vol 84 (6-7) ◽  
pp. 419-435 ◽  
Author(s):  
D Scott
Keyword(s):  
Standard Model ◽  
Particle Physics ◽  
High Energy Physics ◽  
Initial Conditions ◽  
High Energy ◽  
Dimensional Structure ◽  
Physical Parameters ◽  
Beyond The Standard Model ◽  
Observable Universe ◽  
The Standard Model

The Standard Model of Particle Physics (SMPP) is an enormously successful description of high-energy physics, driving ever more precise measurements to find "physics beyond the standard model", as well as providing motivation for developing more fundamental ideas that might explain the values of its parameters. Simultaneously, a description of the entire three-dimensional structure of the present-day Universe is being built up painstakingly. Most of the structure is stochastic in nature, being merely the result of the particular realization of the "initial conditions" within our observable Universe patch. However, governing this structure is the Standard Model of Cosmology (SMC), which appears to require only about a dozen parameters. Cosmologists are now determining the values of these quantities with increasing precision to search for "physics beyond the standard model", as well as trying to develop an understanding of the more fundamental ideas that might explain the values of its parameters. Although it is natural to see analogies between the two Standard Models, some intrinsic differences also exist, which are discussed here. Nevertheless, a truly fundamental theory will have to explain both the SMPP and SMC, and this must include an appreciation of which elements are deterministic and which are accidental. Considering different levels of stochasticity within cosmology may make it easier to accept that physical parameters in general might have a nondeterministic aspect. PACS Nos.: 98.80.–k, 98.80.Bp, 98.80.Es, 12.60.–i

scholarly journals The Impact of Microelectronics on High Energy Physics Innovation: The Role of 65 nm CMOS Technology on New Generation Particle Detectors

2021 ◽  
Vol 9 ◽  
Author(s):  
N. Demaria
Keyword(s):  
Particle Physics ◽  
High Energy Physics ◽  
Hadron Collider ◽  
Cmos Technology ◽  
High Energy ◽  
Main Role ◽  
Noise Power ◽  
Recent Developments ◽  
The Impact ◽  
Energy Physics

The High Luminosity Large Hadron Collider (HL-LHC) at CERN will constitute a new frontier for the particle physics after the year 2027. Experiments will undertake a major upgrade in order to stand this challenge: the use of innovative sensors and electronics will have a main role in this. This paper describes the recent developments in 65 nm CMOS technology for readout ASIC chips in future High Energy Physics (HEP) experiments. These allow unprecedented performance in terms of speed, noise, power consumption and granularity of the tracking detectors.

Towards an Efficient Validation of Dynamical Whole-brain Models

2021 ◽  
Author(s):  
Kevin J. Wischnewski ◽  
Simon B. Eickhoff ◽  
Viktor K. Jirsa ◽  
Oleksandr V. Popovych
Keyword(s):  
Structural Connectivity ◽  
Three Dimensional ◽  
Bayesian Optimization ◽  
High Dimensional ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Phase Oscillators ◽  
Dimensional Parameter ◽  
Whole Brain ◽  
Brain Models

Abstract Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

scholarly journals Impact of detector simulation in particle physics collider experiments - highlights

2019 ◽  
Vol 214 ◽  
pp. 02019
Author(s):  
V. Daniel Elvira
Keyword(s):  
Particle Physics ◽  
High Energy Physics ◽  
Hadron Collider ◽  
High Energy ◽  
Design And Optimization ◽  
The Cost ◽  
Detector Simulation ◽  
The Impact ◽  
Journal Submission ◽  
Energy Physics

Detector simulation has become fundamental to the success of modern high-energy physics (HEP) experiments. For example, the Geant4-based simulation applications developed by the ATLAS and CMS experiments played a major role for them to produce physics measurements of unprecedented quality and precision with faster turnaround, from data taking to journal submission, than any previous hadron collider experiment. The material presented here contains highlights of a recent review on the impact of detector simulation in particle physics collider experiments published in Ref. [1]. It includes examples of applications to detector design and optimization, software development and testing of computing infrastructure, and modeling of physics objects and their kinematics. The cost and economic impact of simulation in the CMS experiment is also presented. A discussion on future detector simulation needs, challenges and potential solutions to address them is included at the end.

scholarly journals Design and Performance Evaluation of Multi-Gb/s Silicon Photonics Transmitters for High Energy Physics

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3569
Author(s):  
Simone Cammarata ◽  
Gabriele Ciarpi ◽  
Stefano Faralli ◽  
Philippe Velha ◽  
Guido Magazzù ◽  
...  
Keyword(s):  
Silicon Photonics ◽  
Particle Physics ◽  
High Energy Physics ◽  
Current Mode ◽  
High Energy ◽  
Silicon On Insulator ◽  
Output Stage ◽  
Optical Links ◽  
And Performance ◽  
Energy Physics

Optical links are rapidly becoming pervasive in the readout chains of particle physics detector systems. Silicon photonics (SiPh) stands as an attractive candidate to sustain the radiation levels foreseen in the next-generation experiments, while guaranteeing, at the same time, multi-Gb/s and energy-efficient data transmission. Integrated electronic drivers are needed to enable SiPh modulators’ deployment in compact on-detector front-end modules. A current-mode logic-based driver harnessing a pseudo-differential output stage is proposed in this work to drive different types of SiPh devices by means of the same circuit topology. The proposed driver, realized in a 65 nm bulk technology and already tested to behave properly up to an 8 MGy total ionizing dose, is hybridly integrated in this work with a lumped-element Mach–Zehnder modulator (MZM) and a ring modulator (RM), both fabricated in a 130 nm silicon-on-insulator (SOI) process. Bit-error-rate (BER) performances confirm the applicability of the selected architecture to either differential and single-ended loads. A 5 Gb/s data rate, in line with the current high energy physics requirements, is achieved in the RM case, while a packaging-related performance degradation is captured in the MZM-based system, confirming the importance of interconnection modeling.

scholarly journals Yifang Wang: high energy physics in China

2016 ◽  
Vol 3 (2) ◽  
pp. 252-256 ◽  
Author(s):  
Ling Wang ◽  
Mu-ming Poo
Keyword(s):  
Neutrino Oscillation ◽  
Particle Physics ◽  
Quantum Interference ◽  
High Energy Physics ◽  
High Energy ◽  
Electron Neutrino ◽  
Daya Bay ◽  
Mass Hierarchy ◽  
Neutrino Mixing ◽  
Energy Physics

Abstract On 8 March 2012, Yifang Wang, co-spokesperson of the Daya Bay Experiment and Director of Institute of High Energy Physics, Chinese Academy of Sciences, announced the discovery of a new type of neutrino oscillation with a surprisingly large mixing angle (θ13), signifying ‘a milestone in neutrino research’. Now his team is heading for a new goal—to determine the neutrino mass hierarchy and to precisely measure oscillation parameters using the Jiangmen Underground Neutrino Observatory, which is due for completion in 2020. Neutrinos are fundamental particles that play important roles in both microscopic particle physics and macroscopic universe evolution. The studies on neutrinos, for example, may answer the question why our universe consists of much more matter than antimatter. But this is not an easy task. Though they are one of the most numerous particles in the universe and zip through our planet and bodies all the time, these tiny particles are like ‘ghost’, difficult to be captured. There are three flavors of neutrinos, known as electron neutrino (νe), muon neutrino (νμ), and tau neutrino (ντ), and their flavors could change as they travel through space via quantum interference. This phenomenon is known as neutrino oscillation or neutrino mixing. To determine the absolute mass of each type of neutrino and find out how they mix is very challenging. In a recent interview with NSR in Beijing, Yifang Wang explained how the Daya Bay Experiment on neutrino oscillation not only addressed the frontier problem in particle physics, but also harnessed the talents and existing technology in Chinese physics community. This achievement, Wang reckons, will not be an exception in Chinese high energy physics, when appropriate funding and organization for big science projects could be efficiently realized in the future.

scholarly journals Natural philosophy versus philosophy of naturalness

2020 ◽  
Vol 35 (18) ◽  
pp. 2030006 ◽  
Author(s):  
Goran Senjanović
Keyword(s):  
Particle Physics ◽  
High Energy Physics ◽  
Natural Philosophy ◽  
High Energy ◽  
Beyond The Standard Model ◽  
Hierarchy Problem ◽  
Guiding Principle ◽  
The Standard Model ◽  
New Phenomena ◽  
Energy Physics

I reflect on some of the basic aspects of present day Beyond the Standard Model particle physics, focusing mostly on the issues of naturalness, in particular on the so-called hierarchy problem. To all of us, physics as natural science emerged with Galileo and Newton, and led to centuries of unparalleled success in explaining and often predicting new phenomena of nature. I argue here that the long-standing obsession with the hierarchy problem as a guiding principle for the future of our field has had the tragic consequence of deviating high energy physics from its origins as natural philosophy, and turning it into a philosophy of naturalness.

scholarly journals THE FUTURE OF PARTICLE PHYSICS AS A NATURAL SCIENCE

1998 ◽  
Vol 13 (06) ◽  
pp. 863-886 ◽  
Author(s):  
FRANK WILCZEK
Keyword(s):  
Natural Science ◽  
Particle Physics ◽  
High Energy Physics ◽  
High Energy ◽  
Particle Interactions ◽  
Fundamental Particle ◽  
Current State ◽  
The Standard Model ◽  
Near Term ◽  
Energy Physics

In the first part of the paper, I give a low-resolution overview of the current state of particle physics — the triumph of the Standard Model and its discontents. I review and re-endorse the remarkably direct and (to me) compelling argument that existing data, properly interpreted, point toward a unified theory of fundamental particle interactions and toward low-energy supersymmetry as the near-term future of high energy physics as a natural science. I then attempt, as requested, some more "visionary" — i.e. even lower resolution — comments about the farther future. In that spirit, I emphasize the continuing importance of condensed matter physics as a source of inspiration and potential application, in particular for expansion of symmetry concepts, and of cosmology as a source of problems, applications, and perhaps ultimately limitations.

scholarly journals Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models

2021 ◽  
Author(s):  
Annika Vogel ◽  
Hendrik Elbern
Keyword(s):  
Forecast Model ◽  
High Dimensional ◽  
Biogenic Emissions ◽  
Model Parameters ◽  
Emission Rates ◽  
Model Uncertainties ◽  
Dimensional Parameter ◽  
Novel Approach ◽  
Correlated Parameters ◽  
Ensemble Generation

Abstract. Atmospheric chemical forecasts highly rely on various model parameters, which are often insufficiently known, as emission rates and deposition velocities. However, a reliable estimation of resulting uncertainties by an ensemble of forecasts is impaired by the high-dimensionality of the system. This study presents a novel approach to efficiently perturb atmospheric-chemical model parameters according to their leading coupled uncertainties. The algorithm is based on the idea that the forecast model acts as a dynamical system inducing multi-variational correlations of model uncertainties. The specific algorithm presented in this study is designed for parameters which depend on local environmental conditions and consists of three major steps: (1) an efficient assessment of various sources of model uncertainties spanned by independent sensitivities, (2) an efficient extraction of leading coupled uncertainties using eigenmode decomposition, and (3) an efficient generation of perturbations for high-dimensional parameter fields by the Karhunen-Loéve expansion. Due to their perceived simulation challenge the method has been applied to biogenic emissions of five trace gases, considering state-dependent sensitivities to local atmospheric and terrestrial conditions. Rapidly decreasing eigenvalues state high spatial- and cross-correlations of regional biogenic emissions, which are represented by a low number of dominating components. Consequently, leading uncertainties can be covered by low number of perturbations enabling ensemble sizes of the order of 10 members. This demonstrates the suitability of the algorithm for efficient ensemble generation for high-dimensional atmospheric chemical parameters.

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