High-energy Neutrino Source Cross-correlations with Nearest-neighbor Distributions
The astrophysical origins of the majority of the IceCube neutrinos remain unknown. Effectively characterizing the spatial distribution of the neutrino samples and associating the events with astrophysical source catalogs can be challenging given the large atmospheric neutrino background and underlyi...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad924c |
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| author | Zhuoyang Zhou Jessi Cisewski-Kehe Ke Fang Arka Banerjee |
| author_facet | Zhuoyang Zhou Jessi Cisewski-Kehe Ke Fang Arka Banerjee |
| author_sort | Zhuoyang Zhou |
| collection | DOAJ |
| description | The astrophysical origins of the majority of the IceCube neutrinos remain unknown. Effectively characterizing the spatial distribution of the neutrino samples and associating the events with astrophysical source catalogs can be challenging given the large atmospheric neutrino background and underlying non-Gaussian spatial features in the neutrino and source samples. In this paper, we investigate a framework for identifying and statistically evaluating the cross-correlations between IceCube data and an astrophysical source catalog based on the k -nearest-neighbor cumulative distribution functions ( k NN-CDFs). We propose a maximum likelihood estimation procedure for inferring the true proportions of astrophysical neutrinos in the point-source data. We conduct a statistical power analysis of an associated likelihood ratio test with estimations of its sensitivity and discovery potential with synthetic neutrino data samples and a WISE–2MASS galaxy sample. We apply the method to IceCube’s public ten-year point-source data and find no statistically significant evidence for spatial cross-correlations with the selected galaxy sample. We discuss possible extensions to the current method and explore the method’s potential to identify the cross-correlation signals in data sets with different sample sizes. |
| format | Article |
| id | doaj-art-f863994410b94230bfad3014442ff7ef |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-f863994410b94230bfad3014442ff7ef2025-01-27T10:55:48ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01979219410.3847/1538-4357/ad924cHigh-energy Neutrino Source Cross-correlations with Nearest-neighbor DistributionsZhuoyang Zhou0Jessi Cisewski-Kehe1https://orcid.org/0000-0002-9656-2272Ke Fang2https://orcid.org/0000-0002-5387-8138Arka Banerjee3https://orcid.org/0000-0002-5209-1173Department of Statistics, University of Wisconsin-Madison , Madison, WI, USA; Department of Statistics & Data Science, Carnegie Mellon University , Pittsburgh, PA, USADepartment of Statistics, University of Wisconsin-Madison , Madison, WI, USADepartment of Physics, Wisconsin IceCube Particle Astrophysics Center, University of Wisconsin-Madison , Madison, WI, USADepartment of Physics, Indian Institute of Science Education and Research , Pashan, Pune, IndiaThe astrophysical origins of the majority of the IceCube neutrinos remain unknown. Effectively characterizing the spatial distribution of the neutrino samples and associating the events with astrophysical source catalogs can be challenging given the large atmospheric neutrino background and underlying non-Gaussian spatial features in the neutrino and source samples. In this paper, we investigate a framework for identifying and statistically evaluating the cross-correlations between IceCube data and an astrophysical source catalog based on the k -nearest-neighbor cumulative distribution functions ( k NN-CDFs). We propose a maximum likelihood estimation procedure for inferring the true proportions of astrophysical neutrinos in the point-source data. We conduct a statistical power analysis of an associated likelihood ratio test with estimations of its sensitivity and discovery potential with synthetic neutrino data samples and a WISE–2MASS galaxy sample. We apply the method to IceCube’s public ten-year point-source data and find no statistically significant evidence for spatial cross-correlations with the selected galaxy sample. We discuss possible extensions to the current method and explore the method’s potential to identify the cross-correlation signals in data sets with different sample sizes.https://doi.org/10.3847/1538-4357/ad924cNeutrino astronomyAstrostatistics |
| spellingShingle | Zhuoyang Zhou Jessi Cisewski-Kehe Ke Fang Arka Banerjee High-energy Neutrino Source Cross-correlations with Nearest-neighbor Distributions The Astrophysical Journal Neutrino astronomy Astrostatistics |
| title | High-energy Neutrino Source Cross-correlations with Nearest-neighbor Distributions |
| title_full | High-energy Neutrino Source Cross-correlations with Nearest-neighbor Distributions |
| title_fullStr | High-energy Neutrino Source Cross-correlations with Nearest-neighbor Distributions |
| title_full_unstemmed | High-energy Neutrino Source Cross-correlations with Nearest-neighbor Distributions |
| title_short | High-energy Neutrino Source Cross-correlations with Nearest-neighbor Distributions |
| title_sort | high energy neutrino source cross correlations with nearest neighbor distributions |
| topic | Neutrino astronomy Astrostatistics |
| url | https://doi.org/10.3847/1538-4357/ad924c |
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