用于三点聚类测量的Python/ c++包

Mike Shengbo Wang, Florian Beutler, Naonori S. Sugiyama
{"title":"用于三点聚类测量的Python/ c++包","authors":"Mike Shengbo Wang, Florian Beutler, Naonori S. Sugiyama","doi":"10.21105/joss.05571","DOIUrl":null,"url":null,"abstract":"Triumvirate is a Python/C++ package for measuring the three-point clustering statistics in large-scale structure (LSS) cosmological analyses. Given a catalogue of discrete particles (such as galaxies) with their spatial coordinates, it computes estimators of the multipoles of the three-point correlation function, also known as the bispectrum in Fourier space, in the tri-polar spherical harmonic (TripoSH) decomposition proposed by Sugiyama et al. (2019). The objective of Triumvirate is to provide efficient end-to-end measurement of clustering statistics which can be fed into downstream galaxy survey analyses to constrain and test cosmological models. To this end, it builds upon the original algorithms in the hitomi code developed by Sugiyama et al. (2018, 2019), and supplies a user-friendly interface with flexible input/output (I/O) of catalogue data and measurement results, with the built program configurable through external parameter files and tracked through enhanced logging and warning/exception handling. For completeness and complementarity, methods for measuring two-point clustering statistics are also included in the package.","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Triumvirate: A Python/C++ package for three-point clustering measurements\",\"authors\":\"Mike Shengbo Wang, Florian Beutler, Naonori S. Sugiyama\",\"doi\":\"10.21105/joss.05571\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Triumvirate is a Python/C++ package for measuring the three-point clustering statistics in large-scale structure (LSS) cosmological analyses. Given a catalogue of discrete particles (such as galaxies) with their spatial coordinates, it computes estimators of the multipoles of the three-point correlation function, also known as the bispectrum in Fourier space, in the tri-polar spherical harmonic (TripoSH) decomposition proposed by Sugiyama et al. (2019). The objective of Triumvirate is to provide efficient end-to-end measurement of clustering statistics which can be fed into downstream galaxy survey analyses to constrain and test cosmological models. To this end, it builds upon the original algorithms in the hitomi code developed by Sugiyama et al. (2018, 2019), and supplies a user-friendly interface with flexible input/output (I/O) of catalogue data and measurement results, with the built program configurable through external parameter files and tracked through enhanced logging and warning/exception handling. For completeness and complementarity, methods for measuring two-point clustering statistics are also included in the package.\",\"PeriodicalId\":94101,\"journal\":{\"name\":\"Journal of open source software\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of open source software\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21105/joss.05571\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of open source software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21105/joss.05571","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Triumvirate: A Python/C++ package for three-point clustering measurements
Triumvirate is a Python/C++ package for measuring the three-point clustering statistics in large-scale structure (LSS) cosmological analyses. Given a catalogue of discrete particles (such as galaxies) with their spatial coordinates, it computes estimators of the multipoles of the three-point correlation function, also known as the bispectrum in Fourier space, in the tri-polar spherical harmonic (TripoSH) decomposition proposed by Sugiyama et al. (2019). The objective of Triumvirate is to provide efficient end-to-end measurement of clustering statistics which can be fed into downstream galaxy survey analyses to constrain and test cosmological models. To this end, it builds upon the original algorithms in the hitomi code developed by Sugiyama et al. (2018, 2019), and supplies a user-friendly interface with flexible input/output (I/O) of catalogue data and measurement results, with the built program configurable through external parameter files and tracked through enhanced logging and warning/exception handling. For completeness and complementarity, methods for measuring two-point clustering statistics are also included in the package.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
3 weeks
期刊最新文献
small_gicp: Efficient and parallel algorithms for point cloud registration gollum: An intuitive programmatic and visual interface for precomputed synthetic spectral model grids ExoTiC-LD: thirty seconds to stellar limb-darkening coefficients XRTpy: A Hinode-X-Ray Telescope Python Package regional-mom6: A Python package for automatic generation of regional configurations for the Modular Ocean Model 6
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1