scholarly journals A factorisation-aware Matrix element emulator

2021 ◽  
Vol 2021 (11) ◽  
Author(s):  
D. Maître ◽  
H. Truong
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Matrix Element ◽  
Percent Level ◽  
Matrix Elements ◽  
Leading Order ◽  
Jet Production ◽  
The Neural Network ◽  
The Matrix ◽  
The One

Abstract In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements. In doing so we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. We apply our model to the case of leading-order jet production in e+e− collisions with up to five jets. Our results show that this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, and a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage.

scholarly journals Multiplicative-accumulative matching of NLO calculations with parton showers

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Paolo Nason ◽  
Gavin P. Salam
Keyword(s):  
Phase Space ◽  
Matrix Element ◽  
Parton Shower ◽  
Leading Order ◽  
Positive Weight ◽  
New Approach ◽  
The Matrix ◽  
Core Features

Abstract We propose a new approach for combining next-to-leading order (NLO) and parton shower (PS) calculations so as to obtain three core features: (a) applicability to general showers, as with the MC@NLO and POWHEG methods; (b) positive-weight events, as with the KrkNLO and POWHEG methods; and (c) all showering attributed to the parton shower code, as with the MC@NLO and KrkNLO methods. This is achieved by using multiplicative matching in phase space regions where the shower overestimates the matrix element and accumulative (additive) matching in regions where the shower underestimates the matrix element, an approach that can be viewed as a combination of the MC@NLO and KrkNLO methods.

scholarly journals The matrix element method at next-to-leading order

2012 ◽  
Vol 2012 (11) ◽  
Author(s):  
John M. Campbell ◽  
Walter T. Giele ◽  
Ciaran Williams
Keyword(s):  
Matrix Element ◽  
Leading Order ◽  
Matrix Element Method ◽  
The Matrix ◽  
Element Method

One-Dimensional Harmonic Oscillator in Quantum Phase Space

2011 ◽  
Vol 110-116 ◽  
pp. 3750-3754
Author(s):  
Jun Lu ◽  
Xue Mei Wang ◽  
Ping Wu
Keyword(s):  
Phase Space ◽  
Harmonic Oscillator ◽  
Quantum Phase ◽  
Space Representation ◽  
Wave Mechanics ◽  
One Dimensional ◽  
Matrix Mechanics ◽  
The Matrix ◽  
Quantum Wave ◽  
The One

Within the framework of the quantum phase space representation established by Torres-Vega and Frederick, we solve the rigorous solutions of the stationary Schrödinger equations for the one-dimensional harmonic oscillator by means of the quantum wave-mechanics method. The result shows that the wave mechanics and the matrix mechanics are equivalent in phase space, just as in position or momentum space.

scholarly journals Neural Network Approach to MPPT Control and Irradiance Estimation

2020 ◽  
Vol 10 (15) ◽  
pp. 5051
Author(s):  
Žarko Zečević ◽  
Maja Rolevski
Keyword(s):  
Neural Network ◽  
Computational Complexity ◽  
Low Complexity ◽  
Maximum Power ◽  
Photovoltaic Module ◽  
Neural Network Approach ◽  
Point Tracking ◽  
The Neural Network ◽  
Power Point ◽  
The One

Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is predicted directly from the measurements. The proposed algorithm cannot instantaneously determine the optimal voltage, but it contains a tunable parameter for controlling the trade-off between the tracking speed and computational complexity. Numerical results indicate that the relative error between the actual maximum power and the one obtained by the proposed algorithm is less than 0.1%, which is up to ten times smaller than in the available algorithms.

CKM MATRIX ELEMENT MAGNITUDES

2004 ◽  
Vol 19 (07) ◽  
pp. 1004-1016 ◽  
Author(s):  
K. R. SCHUBERT
Keyword(s):  
Matrix Element ◽  
Cp Violation ◽  
B Meson ◽  
Matrix Elements ◽  
Perfect Agreement ◽  
B Meson Decays ◽  
Ckm Matrix ◽  
Cp Violations ◽  
The Matrix ◽  
Cp Symmetry

The present status of experimental results for the magnitudes of Cabibbo-Kobayashi-Maskawa matrix elements is reviewed and used for a unitarity test. The matrix is found to be unitary within ±1.8 standard deviations. The matrix violates CP-symmetry and the size of its CP-violation, as derived from only magnitude measurements and unitarity, is in perfect agreement with the observed CP-violations in K and B meson decays.

scholarly journals INFORMATION SUPPORT OF THE REMOTE NITROGEN MONITORING SYSTEM IN AGRICULTURAL CROPS

2018 ◽  
pp. 47-54
Author(s):  
Vitalii Lysenko ◽  
Oleksiy Opryshko ◽  
Dmytro Komarchuk ◽  
Natalia Pasichnyk ◽  
Natalya Zaets ◽  
...  
Keyword(s):  
Neural Network ◽  
Nitrogen Nutrition ◽  
Information Support ◽  
Statistical Characteristics ◽  
Intermediate Layers ◽  
The Neural Network ◽  
The Matrix ◽  
Data Volume ◽  
Linear Elements ◽  
Neural Network Input

The article addresses issues on application of unmanned aerial vehicles (UAV) to monitor nitrogen nutrition through the example of wheat plants. The optical spectral range can be used to monitor exploitation of the UAV. It is recommended to develop specialized spectral indices for such equipment. The article provides calibration curves for nitrogen nutrition monitoring. In the created neural networks, the linear model is represented as a network without intermediate layers, which in the output layer contains only linear elements, the weight corresponds to the elements of the matrix, and the thresholds are the components of the shear vector. During the operation, the neural network actually multiplies the vector of inputs into the matrix of scales, and then adds a vector of displacement to the resulting vector. Results of the research show how to create the specialized RPVI adapted to technological capabilities of UAVs. It has been experimentally proved that input parameters that describe the state of agricultural plantations are regularly distributed. The average statistical characteristics for additive color RGB model is advisable to be the neural network input instead of large sample data volume.

scholarly journals DIAGONAL AND NONDIAGONAL MATRIX ELEMENTS OF THE EFFECTIVE HAMILTONIAN OF ELECTRONS IN A SEMICONDUCTOR (TAKING INTO ACCOUNT SPIN-ORBIT INTERACTION)

2020 ◽  
pp. 120-127
Author(s):  
Voxob Rustamovich Rasulov ◽  
Rustam Yavkachovich Rasulov ◽  
Akhmedov Bahodir Bahromovich ◽  
Ravshan Rustamovich Sultanov
Keyword(s):  
Wave Function ◽  
Matrix Element ◽  
Valence Band ◽  
Heavy Hole ◽  
Orbit Interaction ◽  
Effective Hamiltonian ◽  
Spin Orbit ◽  
Matrix Elements ◽  
The Matrix ◽  
Split Band

The matrix elements of the effective Hamiltonian of current carriers are calculated as in the Kane approximation, where the conduction band, the valence band consisting of light and heavy hole subbands, and the spin-split band, as well as in the Luttinger-Kohn model, are considered. KEYWORDS: matrix element, effective Hamiltonian, current carriers, wave function.

Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

2016 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Pituk Bunnoon
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Systems ◽  
Peak Load ◽  
Electric Power System ◽  
Load Demand ◽  
The Neural Network ◽  
Forecasting Method ◽  
Neural Network Algorithm ◽  
The One

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.

Sign in / Sign up

Export Citation Format

Share Document