Joyce and the Anglo-Irish

Platt, Len

doi:10.1163/9789004485068

Cited by 18 publications

(5 citation statements)

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“…The approximate function for ŷ(x) is then ĥ(x) = n i=1 (α * i − α i )k(x i , x) + b. This dual problem is a simple quadratic programming problem that could be solved fast with the sequential minimal optimization (SMO) [92].…”

Section: Discussionmentioning

confidence: 99%

Investigating the dark matter signal in the cosmic ray antiproton flux with the machine learning method

Lin

Yin

2019

Phys. Rev. D

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We investigate the implications on the dark matter (DM) signal from the AMS-02 cosmic antiproton flux. Global fits to the data are performed under different propagation and hadronic interaction models. The uncertainties from the injection spectrum, propagation effects and solar modulation of the cosmic rays are taken into account comprehensively. Since we need to investigate extended parameter regions with multiple free parameters in the fit, the machine learning method is adopted to maintain a realistic time cost. We find all the effects considered in the fitting process interplay with each other, among which the hadronic interaction model is the most important factor affecting the result. In most hadronic interaction and CR propagation models no DM signal is found with significance larger than 2σ except that the EPOS-LHC interaction model requires a more than 3σ DM signal with DM mass around 1 TeV. For the diffusive reacceleration propagation model there is a highly significant DM signal with mass around 100 GeV. However, the signal becomes less than 1σ if we take a charge dependent solar modulation potential in the analysis. PACS numbers: 96.50.S-,95.35.+d arXiv:1903.09545v1 [astro-ph.HE]Cosmic ray (CR) antiproton is a very promising probe in the dark matter (DM) indirect detection. In recent years, a great progress of the measurement has been made by the AMS-02 experiment, which precisely measured the CRp/p ratio andp flux up to ∼ 450 GeV.Based on these results, lots of theoretical studies have been performed to investigate the possible contribution from DM in the antiproton flux [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. As the deviation between the expected secondary CR antiproton flux produced by the astrophysical processes and the data is not significant, any slight change of the background expectation could influence the DM implication. Therefore, the theoretical uncertainties of the secondary CR antiproton flux, mainly arising from the CR propagation process and the hadronic interactions between the CR particles and interstellar medium (ISM), are very important in the determination of the DM contribution.Although the principle of the strong interaction has been well described by the quantum chromodynamics (QCD), only the processes with large momentum transfers can be perturbatively calculated. The production processes of secondary CR antiprotons, which involve the forward scattering processes with multi-particle production, are always calculated under extra simplified assumptions or using some empirical parametrizations. This kind of processes has been a long time difficulty in the cosmic ray study. Many phenomenological models with different simplified assumptions have been constructed in the literature [19][20][21][22][23][24][25]. In addition, several empirical parametrizations only aiming to reproduce the antiproton production cross section have also been developed [26][27][28]. On the other hand, the CR propagation can be determined by the fit to the observed secondary-to-prim...

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Section: Discussionmentioning

confidence: 99%

Investigating the dark matter signal in the cosmic ray antiproton flux with the machine learning method

Lin

Yin

2019

Phys. Rev. D

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“…A good classification is achieved by constructing a linear separating hyperplane in this feature space with the maximal margin to the nearest samples of any class. Here sequential minimal optimization (SMO) is adopted as the iterative method for solving this quadratic programming (QP) problem [14]. Proper selection of kernel function for corresponding classification problem can optimize the performance by mapping samples to appropriate feature space.…”

Section: Applications Of Tdgs Methods In Class-imbalanced Density Dat...mentioning

confidence: 99%

Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method

2017

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A: Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class−imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class−imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class−balanced effect of Time−Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry−INTerferometry (POINT) system. 1Corresponding author.

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“…Proper selection of kernel function for different classification problems can optimize the performance by mapping samples to appropriate high-dimensional feature space. Sequential minimal optimization (SMO) is adopted as a common iterative method for solving this quadratic programming (QP) problem [28].…”

Section: Feature Idmentioning

confidence: 99%

“…The performance in this paper refers to the accuracy rate of classification results about physical similarity. In this example, Support Vector Machine (SVM) is adopted as the classification algorithm, which has advantage in solving nonlinear, high-dimensional problems [25][26][27][28]. The k-fold cross-validation is used as model assessment method because it can provide an effectively unbiased error estimate.…”

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confidence: 99%

Time-domain global similarity method for automatic data cleaning for multi-channel measurement systems in magnetic confinement fusion devices

Lan

Liu

Qin

et al. 2019

Computer Physics Communications

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To guarantee the availability and reliability of data source in Magnetic Confinement Fusion (MCF) devices, incorrect diagnostic data, which cannot reflect real physical properties of measured objects, should be sorted out before further analysis and study. Traditional data sorting cannot meet the growing demand of MCF research because of the low-efficiency, time-delay, and lack of objective criteria. In this paper, a Time-Domain Global Similarity (TDGS) method based on machine learning technologies is proposed for the automatic data cleaning of MCF devices. Traditional data sorting aims to the classification of original diagnostic data sequences, which are different in both length and evolution properties under various discharge parameters. Hence the classification criteria are affected by many discharge parameters and vary shot by shot.The focus of TDGS method is turned to the physical similarity between data sequences from different channels, which are more essential and independent of discharge parameters. The complexity arisen from real discharge parameters during data cleaning is avoided in the TDGS method by transforming the general data sorting problem into a binary classification problem about the physical similarity between data sequences. As a demonstration of its application to multi-channel measurement systems, the TDGS method is applied to the EAST POlarimeter-INterferomeTer (POINT) system. The optimized performance of the method evaluated by 24-fold cross-validation has reached 0.9871±0.0385.

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Joyce and the Anglo-Irish

Cited by 18 publications

References 0 publications

Investigating the dark matter signal in the cosmic ray antiproton flux with the machine learning method

Investigating the dark matter signal in the cosmic ray antiproton flux with the machine learning method

Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method

Time-domain global similarity method for automatic data cleaning for multi-channel measurement systems in magnetic confinement fusion devices

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