Propagating data-driven galaxy redshift distribution uncertainties in 3$\times$2-pt analyses
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913065301180416 |
|---|---|
| author | Ruiz-Zapatero, Jaime Hang, Qianjun Zhang, Yun-Hao Joachimi, Benjamin Zuntz, Joe Harrison, Ian García-García, Carlos Malz, Alex Stölzner, Benjamin Collaboration, the LSST Dark Energy Science |
| author_facet | Ruiz-Zapatero, Jaime Hang, Qianjun Zhang, Yun-Hao Joachimi, Benjamin Zuntz, Joe Harrison, Ian García-García, Carlos Malz, Alex Stölzner, Benjamin Collaboration, the LSST Dark Energy Science |
| contents | Uncertainties in the radial distribution of galaxies, $\boldsymbol{n}(\boldsymbol{z})$, are one of the major contributions to the error budget of early Stage-IV galaxy survey analyses of weak gravitational lensing, galaxy clustering and galaxy-galaxy lensing (3$\times$2-pt). Based on ensembles of simulated $\boldsymbol{n}(\boldsymbol{z})$ including stochastic and systematic variations, we study the impact of four different $\boldsymbol{n}(\boldsymbol{z})$ uncertainty models: shifts, shifts & stretches, Gaussian processes (GP) and principal component analysis (PCA). Due to the high dimensionality of the latter models, we make use of state-of-the-art gradient-based inference methods as well as approximate analytical marginalisation schemes. Our results show that Stage-IV 3$\times$2-pt analyses must go beyond simple shift & stretch models. In particular, we advocate for the adoption of PCA models even in early Stage-IV surveys. Our results show that considering a five-parameters PCA model only degrades the constraint on the $S_{\rm 8}$ parameter by $5$ per cent with respect to the case when only a shift and a stretch parameter are included, while incurring half the bias in its constituents parameters, $Ω_{\rm m}$ and $σ_{\rm 8}$. We demonstrate that all models considered can be safely marginalised analytically, with speed-ups of up to a factor of 25 depending on the dimensionality of the model. This will allow Stage-IV analyses to safely include higher-dimensional $\boldsymbol{n}(\boldsymbol{z})$ uncertainty models in their analysis at negligible additional computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24425 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Propagating data-driven galaxy redshift distribution uncertainties in 3$\times$2-pt analyses Ruiz-Zapatero, Jaime Hang, Qianjun Zhang, Yun-Hao Joachimi, Benjamin Zuntz, Joe Harrison, Ian García-García, Carlos Malz, Alex Stölzner, Benjamin Collaboration, the LSST Dark Energy Science Cosmology and Nongalactic Astrophysics Uncertainties in the radial distribution of galaxies, $\boldsymbol{n}(\boldsymbol{z})$, are one of the major contributions to the error budget of early Stage-IV galaxy survey analyses of weak gravitational lensing, galaxy clustering and galaxy-galaxy lensing (3$\times$2-pt). Based on ensembles of simulated $\boldsymbol{n}(\boldsymbol{z})$ including stochastic and systematic variations, we study the impact of four different $\boldsymbol{n}(\boldsymbol{z})$ uncertainty models: shifts, shifts & stretches, Gaussian processes (GP) and principal component analysis (PCA). Due to the high dimensionality of the latter models, we make use of state-of-the-art gradient-based inference methods as well as approximate analytical marginalisation schemes. Our results show that Stage-IV 3$\times$2-pt analyses must go beyond simple shift & stretch models. In particular, we advocate for the adoption of PCA models even in early Stage-IV surveys. Our results show that considering a five-parameters PCA model only degrades the constraint on the $S_{\rm 8}$ parameter by $5$ per cent with respect to the case when only a shift and a stretch parameter are included, while incurring half the bias in its constituents parameters, $Ω_{\rm m}$ and $σ_{\rm 8}$. We demonstrate that all models considered can be safely marginalised analytically, with speed-ups of up to a factor of 25 depending on the dimensionality of the model. This will allow Stage-IV analyses to safely include higher-dimensional $\boldsymbol{n}(\boldsymbol{z})$ uncertainty models in their analysis at negligible additional computational cost. |
| title | Propagating data-driven galaxy redshift distribution uncertainties in 3$\times$2-pt analyses |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2604.24425 |