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
  • [31] Goodness-of-fit for a concentrated von Mises-Fisher distribution
    Sousa Figueiredo, Adelaide Maria
    COMPUTATIONAL STATISTICS, 2012, 27 (01) : 69 - 82
  • [32] Improved likelihood ratio tests on the von Mises-Fisher distribution
    Larsen, PV
    Blæsild, P
    Sorensen, MK
    BIOMETRIKA, 2002, 89 (04) : 947 - 951
  • [33] Correlation Properties in Channels With von Mises-Fisher Distribution of Scatterers
    Turbic, Kenan
    Kasparick, Martin
    Stanczak, Slawomir
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2024, 13 (12) : 3638 - 3642
  • [34] Confidence regions for the mean direction of the von Mises-Fisher distribution
    Watamori, Y
    Fujioka, T
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2005, 34 (03) : 671 - 678
  • [35] Goodness-of-fit for a concentrated von Mises-Fisher distribution
    Adelaide Maria Sousa Figueiredo
    Computational Statistics, 2012, 27 : 69 - 82
  • [36] Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution
    Guan, Hao
    Smith, William A. P.
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 26 (02) : 711 - 723
  • [37] Point Cloud Simplification Method Using von Mises-Fisher Distribution to Extract Features
    Liu Yuan
    Zuo Xiaoqing
    Li Yongfa
    Yang Xu
    Zhou Dingyi
    Huang Kun
    LASER & OPTOELECTRONICS PROGRESS, 2023, 60 (02)
  • [38] Identification of multiple sources in a plate structure using pre-filtering process for reduction of interference wave
    Lee, S. K.
    Moon, Y. S.
    Park, J. H.
    SMART STRUCTURES AND SYSTEMS, 2011, 8 (02) : 219 - 237
  • [39] An Approach for Pre-Filtering Images from Big Data Sets
    Kanungo, Suvendu
    Tiwari, Sachin
    2015 INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION & AUTOMATION (ICCCA), 2015, : 1110 - 1115
  • [40] Multivariate saddlepoint tests on the mean direction of the von Mises-Fisher distribution
    Gatto, R.
    METRIKA, 2017, 80 (6-8) : 733 - 747