1,069 research outputs found
Teichmüller theory and collapse of flat manifolds
We provide an algebraic description of the Teichmüller space and moduli space of flat metrics on a closed manifold or orbifold and study its boundary, which consists of (isometry classes of) flat orbifolds to which the original object may collapse. It is also shown that every closed flat orbifold can be obtained by collapsing closed flat manifolds, and the collapsed limits of closed flat 3-manifolds are classified
Neural network determination of the non-singlet quark distribution
We summarize the main features of our approach to parton fitting, and we show
a preliminary result for the non-singlet structure function. When comparing our
result to other PDF sets, we find a better description of large x data and
larger error bands in the extrapolation regions.Comment: 4 pages, 1 eps figure. Presented at the XIV International Workshop on
Deep Inelastic Scattering (DIS2006), Tsukuba, Japan, 20-24 April 200
Monte Carlo analysis of CLAS data
We present a fit of the virtual-photon scattering asymmetry of polarized Deep
Inelastic Scattering which combines a Monte Carlo technique with the use of a
redundant parametrization based on Neural Networks. We apply the result to the
analysis of CLAS data on a polarized proton target.Comment: To appear in the proceedings of 16th International Workshop on
Deep-Inelastic Scattering and Related Subjects (DIS2008), 7-11 April 2008,
University College London, U
A determination of alpha_s from scaling violations with truncated moments
We describe a determination of the strong coupling alpha_s(M_Z) from scaling
violations of the nonsinglet DIS structure function, which is based on two
novel techniques aimed at controlling and minimizing the theoretical error: a
neural network parametrization of BCDMS and NMC data, and QCD evolution by
means of truncated Mellin moments.Comment: 5 pages, no figures. Talk given by L. Magnea at QCD02, Montpellier,
July 200
Recent progress on NNPDF for LHC
We present recent results of the NNPDF collaboration on a full DIS analysis
of Parton Distribution Functions (PDFs). Our method is based on the idea of
combining a Monte Carlo sampling of the probability measure in the space of
PDFs with the use of neural networks as unbiased universal interpolating
functions. The general structure of the project and the features of the fit are
described and compared to those of the traditional approaches.Comment: 4 pages, 6 figures, contribution for the proceedings of the
conference "Rencontres de Moriond, QCD and High Energy Interactions
Progress on neural parton distributions
We give a status report on the determination of a set of parton distributions
based on neural networks. In particular, we summarize the determination of the
nonsinglet quark distribution up to NNLO, we compare it with results obtained
using other approaches, and we discuss its use for a determination of
.Comment: 4 pages, 2 figs, uses dis2007.cls, to appear in the DIS 2007 workshop
proceeding
Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations
Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β
N
levels that would have kept the RWM stable for a set of unstable discharges
The first eigenvalue of a homogeneous CROSS
We provide explicit formulae for the first eigenvalue of the Laplace-Beltrami
operator on a compact rank one symmetric space (CROSS) endowed with any
homogeneous metric. As consequences, we prove that homogeneous metrics on
CROSSes are isospectral if and only if they are isometric, and also discuss
their stability (or lack thereof) as solutions to the Yamabe problem.Comment: LaTeX2e, 40 page
Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas
One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects, , for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value of has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST)
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