Statistical Nuances in BAO Analysis: Likelihood Formulations and Non-Gaussianities
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918394358398976 |
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| author | Staicova, Denitsa |
| author_facet | Staicova, Denitsa |
| contents | We present a systematic comparison of statistical approaches to Baryon Acoustic Oscillation (BAO) analysis using DESI DR2 data. We evaluate four methods for handling the nuisance parameter $β=1/(H_0 r_d)$: marginalization, profiling, Taylor expansion, and full likelihood analysis across multiple cosmological models. Our results demonstrate that while these methods yield consistent constraints for $Λ$CDM and $Ω_K$CDM models, they produce notable differences for models with dynamical dark energy parameters. Through eigenvalue decomposition of Fisher matrices, we identify extreme parameter degeneracies in $ww_a$CDM and $Ω_Kww_a$CDM models that explain these statistical sensitivities. Surprisingly, $Ω_K$CDM shows the highest information content across datasets, suggesting BAO measurements are particularly informative about spatial curvature. We further use skewness and kurtosis analysis to identify deviations from Gaussianity, highlighting limitations in Fisher approximations in the dark energy models. Our analysis demonstrates the importance of careful statistical treatment when extracting cosmological constraints from increasingly precise measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18416 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical Nuances in BAO Analysis: Likelihood Formulations and Non-Gaussianities Staicova, Denitsa Cosmology and Nongalactic Astrophysics We present a systematic comparison of statistical approaches to Baryon Acoustic Oscillation (BAO) analysis using DESI DR2 data. We evaluate four methods for handling the nuisance parameter $β=1/(H_0 r_d)$: marginalization, profiling, Taylor expansion, and full likelihood analysis across multiple cosmological models. Our results demonstrate that while these methods yield consistent constraints for $Λ$CDM and $Ω_K$CDM models, they produce notable differences for models with dynamical dark energy parameters. Through eigenvalue decomposition of Fisher matrices, we identify extreme parameter degeneracies in $ww_a$CDM and $Ω_Kww_a$CDM models that explain these statistical sensitivities. Surprisingly, $Ω_K$CDM shows the highest information content across datasets, suggesting BAO measurements are particularly informative about spatial curvature. We further use skewness and kurtosis analysis to identify deviations from Gaussianity, highlighting limitations in Fisher approximations in the dark energy models. Our analysis demonstrates the importance of careful statistical treatment when extracting cosmological constraints from increasingly precise measurements. |
| title | Statistical Nuances in BAO Analysis: Likelihood Formulations and Non-Gaussianities |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2504.18416 |