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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.14638 |
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| _version_ | 1866918065112875008 |
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| author | Pinon, Mathilde de Mattia, Arnaud Burtin, Étienne Ruhlmann-Kleider, Vanina Codis, Sandrine Paillas, Enrique Cuesta-Lazaro, Carolina |
| author_facet | Pinon, Mathilde de Mattia, Arnaud Burtin, Étienne Ruhlmann-Kleider, Vanina Codis, Sandrine Paillas, Enrique Cuesta-Lazaro, Carolina |
| contents | We present an analytical model for density-split correlation functions, that probe galaxy clustering in different density environments. Specifically, we focus on the cross-correlation between density-split regions and the tracer density field. We show that these correlation functions can be expressed in terms of the two-point probability density function (PDF) of the density field. We derive analytical predictions using three levels of approximation for the two-point PDF: a bivariate Gaussian distribution, a bivariate shifted log-normal distribution, and a prediction based on the Large Deviation Theory (LDT) framework. For count-in-cell densities, obtained through spherical top-hat smoothing, one can leverage spherical collapse dynamics and LDT to predict the density two-point PDF in the large-separation regime relative to the smoothing radius. We validate our model against dark matter N-body simulations in real space, incorporating Poisson shot noise and galaxy bias. Our results show that the LDT prediction outperforms the log-normal approximation, and agrees with simulations on large scales within the cosmic variance of a typical DESI DR1 sample, despite relying on only one degree of freedom. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A theoretical approach to density-split clustering Pinon, Mathilde de Mattia, Arnaud Burtin, Étienne Ruhlmann-Kleider, Vanina Codis, Sandrine Paillas, Enrique Cuesta-Lazaro, Carolina Cosmology and Nongalactic Astrophysics We present an analytical model for density-split correlation functions, that probe galaxy clustering in different density environments. Specifically, we focus on the cross-correlation between density-split regions and the tracer density field. We show that these correlation functions can be expressed in terms of the two-point probability density function (PDF) of the density field. We derive analytical predictions using three levels of approximation for the two-point PDF: a bivariate Gaussian distribution, a bivariate shifted log-normal distribution, and a prediction based on the Large Deviation Theory (LDT) framework. For count-in-cell densities, obtained through spherical top-hat smoothing, one can leverage spherical collapse dynamics and LDT to predict the density two-point PDF in the large-separation regime relative to the smoothing radius. We validate our model against dark matter N-body simulations in real space, incorporating Poisson shot noise and galaxy bias. Our results show that the LDT prediction outperforms the log-normal approximation, and agrees with simulations on large scales within the cosmic variance of a typical DESI DR1 sample, despite relying on only one degree of freedom. |
| title | A theoretical approach to density-split clustering |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2501.14638 |