Weilun Zheng, Kwan Chuen Chan, Haojie Xu, Le Zhang, Ruiyu Song
{"title":"通过联合自校准和聚类-红移协同优化红移分布推断","authors":"Weilun Zheng, Kwan Chuen Chan, Haojie Xu, Le Zhang, Ruiyu Song","doi":"arxiv-2409.12009","DOIUrl":null,"url":null,"abstract":"Accurately characterizing the true redshift (true-$z$) distribution of a\nphotometric redshift (photo-$z$) sample is critical for cosmological analyses\nin imaging surveys. Clustering-based techniques, which include\nclustering-redshift (CZ) and self-calibration (SC) methods--depending on\nwhether external spectroscopic data are used--offer powerful tools for this\npurpose. In this study, we explore the joint inference of the true-$z$\ndistribution by combining SC and CZ (denoted as SC+CZ). We derive simple\nmultiplicative update rules to perform the joint inference. By incorporating\nappropriate error weighting and an additional weighting function, our method\nshows significant improvement over previous algorithms. We validate our\napproach using a DES Y3 mock catalog. The true-$z$ distribution estimated\nthrough the combined SC+CZ method is generally more accurate than using SC or\nCZ alone. To account for the different constraining powers of these methods, we\nassign distinct weights to the SC and CZ contributions. The optimal weights,\nwhich minimize the distribution error, depend on the relative constraining\nstrength of the SC and CZ data. Specifically, for a spectroscopic redshift\nsample that represents 1% of the photo-$z$ sample, the optimal combination\nreduces the total error by 20% (40%) compared to using CZ (SC) alone, and it\nkeeps the bias in mean redshift [$\\Delta \\bar{z} / (1 + z) $] at the level of\n0.3%. Furthermore, when CZ data is only available in the low-$z$ range and the\nhigh-$z$ range relies solely on SC data, SC+CZ enables consistent estimation of\nthe true-$z$ distribution across the entire redshift range. Our findings\ndemonstrate that SC+CZ is an effective tool for constraining the true-$z$\ndistribution, paving the way for clustering-based methods to be applied at\n$z\\gtrsim 1$.","PeriodicalId":501207,"journal":{"name":"arXiv - PHYS - Cosmology and Nongalactic Astrophysics","volume":"13 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Redshift Distribution Inference through Joint Self-Calibration and Clustering-Redshift Synergy\",\"authors\":\"Weilun Zheng, Kwan Chuen Chan, Haojie Xu, Le Zhang, Ruiyu Song\",\"doi\":\"arxiv-2409.12009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurately characterizing the true redshift (true-$z$) distribution of a\\nphotometric redshift (photo-$z$) sample is critical for cosmological analyses\\nin imaging surveys. Clustering-based techniques, which include\\nclustering-redshift (CZ) and self-calibration (SC) methods--depending on\\nwhether external spectroscopic data are used--offer powerful tools for this\\npurpose. In this study, we explore the joint inference of the true-$z$\\ndistribution by combining SC and CZ (denoted as SC+CZ). We derive simple\\nmultiplicative update rules to perform the joint inference. By incorporating\\nappropriate error weighting and an additional weighting function, our method\\nshows significant improvement over previous algorithms. We validate our\\napproach using a DES Y3 mock catalog. The true-$z$ distribution estimated\\nthrough the combined SC+CZ method is generally more accurate than using SC or\\nCZ alone. To account for the different constraining powers of these methods, we\\nassign distinct weights to the SC and CZ contributions. The optimal weights,\\nwhich minimize the distribution error, depend on the relative constraining\\nstrength of the SC and CZ data. Specifically, for a spectroscopic redshift\\nsample that represents 1% of the photo-$z$ sample, the optimal combination\\nreduces the total error by 20% (40%) compared to using CZ (SC) alone, and it\\nkeeps the bias in mean redshift [$\\\\Delta \\\\bar{z} / (1 + z) $] at the level of\\n0.3%. Furthermore, when CZ data is only available in the low-$z$ range and the\\nhigh-$z$ range relies solely on SC data, SC+CZ enables consistent estimation of\\nthe true-$z$ distribution across the entire redshift range. Our findings\\ndemonstrate that SC+CZ is an effective tool for constraining the true-$z$\\ndistribution, paving the way for clustering-based methods to be applied at\\n$z\\\\gtrsim 1$.\",\"PeriodicalId\":501207,\"journal\":{\"name\":\"arXiv - PHYS - Cosmology and Nongalactic Astrophysics\",\"volume\":\"13 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Cosmology and Nongalactic Astrophysics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.12009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Cosmology and Nongalactic Astrophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.12009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing Redshift Distribution Inference through Joint Self-Calibration and Clustering-Redshift Synergy
Accurately characterizing the true redshift (true-$z$) distribution of a
photometric redshift (photo-$z$) sample is critical for cosmological analyses
in imaging surveys. Clustering-based techniques, which include
clustering-redshift (CZ) and self-calibration (SC) methods--depending on
whether external spectroscopic data are used--offer powerful tools for this
purpose. In this study, we explore the joint inference of the true-$z$
distribution by combining SC and CZ (denoted as SC+CZ). We derive simple
multiplicative update rules to perform the joint inference. By incorporating
appropriate error weighting and an additional weighting function, our method
shows significant improvement over previous algorithms. We validate our
approach using a DES Y3 mock catalog. The true-$z$ distribution estimated
through the combined SC+CZ method is generally more accurate than using SC or
CZ alone. To account for the different constraining powers of these methods, we
assign distinct weights to the SC and CZ contributions. The optimal weights,
which minimize the distribution error, depend on the relative constraining
strength of the SC and CZ data. Specifically, for a spectroscopic redshift
sample that represents 1% of the photo-$z$ sample, the optimal combination
reduces the total error by 20% (40%) compared to using CZ (SC) alone, and it
keeps the bias in mean redshift [$\Delta \bar{z} / (1 + z) $] at the level of
0.3%. Furthermore, when CZ data is only available in the low-$z$ range and the
high-$z$ range relies solely on SC data, SC+CZ enables consistent estimation of
the true-$z$ distribution across the entire redshift range. Our findings
demonstrate that SC+CZ is an effective tool for constraining the true-$z$
distribution, paving the way for clustering-based methods to be applied at
$z\gtrsim 1$.