A novel approach for pre-filtering event sources using the von Mises–Fisher distribution

被引:0
|
作者
D. Costantin
G. Menardi
A. R. Brazzale
D. Bastieri
J. H. Fan
机构
[1] Guangzhou University,Center for Astrophysics
[2] Astronomy Science and Technology Research Laboratory of Education of Guangdong Province,Department of Statistical Sciences
[3] University of Padova,Department of Physics and Astronomy “G. Galilei”
[4] University of Padua,Padua Division
[5] National Institute for Nuclear Physics,undefined
来源
Astrophysics and Space Science | 2020年 / 365卷
关键词
Astrostatistics; Finite mixture; High energy photon; von Mises-Fisher distribution;
D O I
暂无
中图分类号
学科分类号
摘要
Searching for as yet undetected γ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\gamma $\end{document}-ray sources is one of the main stated goals of the Fermi Large Area Telescope Collaboration. In this paper, we explore the capability of a filtering method based on a finite mixture of von Mises–Fisher distributions. The proposed procedure is specifically designed to handle data with support on the unit sphere. The assumption of a parametric model for each high energy emitting source allows us to derive an explicit expression for both the direction of the sources and their angular resolutions. The corresponding measures are based on the directional mean and the quantiles of the single mixture components. Sound criteria of model selection can provide an automatic way to determine the number of detected sources. Additionally, a likelihood-ratio test is developed to evaluate their significance. The procedure is tested on simulated data sets of photon emissions from high energy sources within the energy range [10−1,000] GeV\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$[10 - 1,000]\text{ GeV}$\end{document}. A real data example consisting of a sample of the Fermi LAT data collected over a period of about 7.2 years within the energy range [10−1,000] GeV\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$[10 - 1,000]\text{ GeV}$\end{document}, in a subregion of the γ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\gamma $\end{document}-ray sky, is furthermore provided.
引用
收藏
相关论文
共 50 条
  • [21] An Adaptive Approach to Improve the Accuracy of Packet Pre-Filtering
    Jesudoss, Sofiya
    Jesudoss, Auxeeliya
    Sulaiman, Ashraph
    ADVANCES IN MATERIALS AND SYSTEMS TECHNOLOGIES III, 2012, 367 : 241 - +
  • [22] Joint Calibration of Inertial Sensors and Magnetometers using von Mises-Fisher Filtering and Expectation Maximization
    Hostettler, Roland
    Garcia-Fernandez, Angel F.
    Tronarp, Filip
    Sarkka, Simo
    2019 22ND INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2019), 2019,
  • [23] A novel approach to tube design via von Mises probability distribution
    Oral, Atacan
    Subasi, Omer
    Ozturk, Caglar
    Lazoglu, Ismail
    Subay, Sehmuz Ali
    ENGINEERING OPTIMIZATION, 2024, 56 (03) : 319 - 337
  • [24] Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling
    Zhang, Fan
    Hancock, Edwin R.
    Goodlett, Casey
    Gerig, Guido
    MEDICAL IMAGE ANALYSIS, 2009, 13 (01) : 5 - 18
  • [25] Progressive von Mises-Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation
    Li, Kailai
    Pfaff, Florian
    Hanebeck, Uwe D.
    SENSORS, 2021, 21 (09)
  • [26] A Pre-Filtering and Post-Filtering Approach to Blind Source Separation
    Scarpiniti, Michele
    Bunkheila, Gabriele
    Parisi, Raffaele
    Uncini, Aurelio
    NEURAL NETS WIRN10, 2011, 226 : 89 - 98
  • [27] Robust estimation of location and concentration parameters for the von Mises–Fisher distribution
    Shogo Kato
    Shinto Eguchi
    Statistical Papers, 2016, 57 : 205 - 234
  • [28] A GOODNESS-OF-FIT TEST FOR THE VON MISES-FISHER DISTRIBUTION
    MARDIA, KV
    HOLMES, D
    KENT, JT
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1984, 46 (01) : 72 - 78
  • [29] SAIZ interferogram phase filtering based on the Von Mises distribution
    Huber, R
    Dutra, LV
    Freitas, CD
    IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS, 2001, : 2816 - 2818
  • [30] Multivariate saddlepoint tests on the mean direction of the von Mises–Fisher distribution
    R. Gatto
    Metrika, 2017, 80 : 733 - 747