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NNPDF
NNPDF is the acronym used to identify the parton distribution functions from the NNPDF Collaboration. NNPDF parton densities are extracted from global fits to data based on a combination of a Monte Carlo method for uncertainty estimation and the use of neural networks as basic interpolating functions. Methodology The NNPDF approach can be divided into four main steps: * The generation of a large sample of Monte Carlo replicas of the original experimental data, in a way that central values, errors and correlations are reproduced with enough accuracy. * The training (minimization of the \chi^2) of a set of PDFs parametrized by neural networks on each of the above MC replicas of the data. PDFs are parametrized at the initial evolution scale Q^_ and then evolved to the experimental data scale Q^2 by means of the DGLAP equations. Since the PDF parametrization is redundant, the minimization strategy is based in genetic algorithms as well as gradient descent based minimizers. * Th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parton Distribution Functions
In particle physics, the parton model is a model of hadrons, such as protons and neutrons, proposed by Richard Feynman. It is useful for interpreting the cascades of radiation (a parton shower) produced from quantum chromodynamics (QCD) processes and interactions in high-energy particle collisions. Model Parton showers are simulated extensively in Monte Carlo event generators, in order to calibrate and interpret (and thus understand) processes in collider experiments. As such, the name is also used to refer to algorithms that approximate or simulate the process. Motivation The parton model was proposed by Richard Feynman in 1969 as a way to analyze high-energy hadron collisions. Any hadron (for example, a proton) can be considered as a composition of a number of point-like constituents, termed "partons". The parton model was immediately applied to electron-proton deep inelastic scattering by Bjorken and Paschos. Component particles A hadron is composed of a number of point- ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DGLAP
The Dokshitzer–Gribov–Lipatov–Altarelli–Parisi (DGLAP) evolution equations are equations in QCD describing the variation of parton distribution functions with varying energy scales. Experimentally observed scaling violation in deep inelastic scattering is important evidence for the correctness of the equations and of QCD in general. The equations were first published in the western world by Guido Altarelli and Giorgio Parisi in 1977, and so are still sometimes called the Altarelli–Parisi equations. Only later did it become known that an equivalent formula had been published in Russia by in 1977, and by Vladimir Gribov and Lev Lipatov in 1972.V. N. Gribov, L. N. Lipatov. ''Sov. J. Nucl. Phys.'' 15:438 (1972). The DGLAP QCD evolution equations are widely used in global determinations of parton distributions, like those from the CTEQ or NNPDF collaborations. See also * Jet (particle physics) * HERA * APFEL (Software) APFEL is an opensource software able to perform Do ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Particle Physics
Particle physics or high energy physics is the study of fundamental particles and forces that constitute matter and radiation. The fundamental particles in the universe are classified in the Standard Model as fermions (matter particles) and bosons (force-carrying particles). There are three generations of fermions, but ordinary matter is made only from the first fermion generation. The first generation consists of up and down quarks which form protons and neutrons, and electrons and electron neutrinos. The three fundamental interactions known to be mediated by bosons are electromagnetism, the weak interaction, and the strong interaction. Quarks cannot exist on their own but form hadrons. Hadrons that contain an odd number of quarks are called baryons and those that contain an even number are called mesons. Two baryons, the proton and the neutron, make up most of the mass of ordinary matter. Mesons are unstable and the longest-lived last for only a few hundredths of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Monte Carlo Method
Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribution. In physics-related problems, Monte Carlo methods are useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered materials, strongly coupled solids, and cellular structures (see cellular Potts model, interacting particle systems, McKean–Vlasov processes, kinetic models of gases). Other examples include modeling phenomena with significant uncertainty in inputs such as the calculation of ris ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Networks
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gluon
A gluon ( ) is an elementary particle that acts as the exchange particle (or gauge boson) for the strong force between quarks. It is analogous to the exchange of photons in the electromagnetic force between two charged particles. Gluons bind quarks together, forming hadrons such as protons and neutrons. Gluons are vector gauge bosons that mediate strong interactions of quarks in quantum chromodynamics (QCD). Gluons themselves carry the color charge of the strong interaction. This is unlike the photon, which mediates the electromagnetic interaction but lacks an electric charge. Gluons therefore participate in the strong interaction in addition to mediating it, making QCD significantly harder to analyze than quantum electrodynamics (QED). Properties The gluon is a vector boson, which means, like the photon, it has a spin of 1. While massive spin-1 particles have three polarization states, massless gauge bosons like the gluon have only two polarization states because gauge ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Particle Physics
Computational particle physics refers to the methods and computing tools developed in and used by particle physics research. Like computational chemistry or computational biology, it is, for particle physics both a specific branch and an interdisciplinary field relying on computer science, theoretical and experimental particle physics and mathematics. The main fields of computational particle physics are: lattice field theory (numerical computations), automatic calculation of particle interaction or decay (computer algebra) and event generators (stochastic methods).https://arxiv.org/abs/1301.1211 ''Computational Particle Physics for Event Generators and Data Analysis'' retrieved 8/24/20https://www.researchgate.net/publication/234060239_Computational_Particle_Physics_for_Event_Generators_and_Data_Analysis ''Computational Particle Physics for Event Generators and Data Analysis'' retrieved 8/24/20https://www2.ccs.tsukuba.ac.jp/projects/ILFTNet/ ''International research network for c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
