$\mathbf{12\times2}$pt combined probes: pipeline, neutrino mass, and data compression

TL;DR

This paper introduces a new simulation pipeline combining multiple cosmological probes at the map level, enabling improved neutrino mass constraints and data analysis efficiency in large-scale structure and CMB studies.

Contribution

It presents a novel pipeline, UFalconv2, for fast, self-consistent multi-probe simulations and data compression, validated on a 12x2pt analysis with improved neutrino mass constraints.

Findings

LSS data improves neutrino mass constraints by up to 35%.

Full data combination can achieve 3σ neutrino mass detection.

MOPED outperforms PCA in data compression.

Abstract

With the rapid advance of wide-field surveys it is increasingly important to perform combined cosmological probe analyses. We present a new pipeline for simulation-based multi-probe analyses, which combines tomographic large-scale structure (LSS) probes (weak lensing and galaxy clustering) with cosmic microwave background (CMB) primary and lensing data. These are combined at the $C_\ell$-level, yielding 12 distinct auto- and cross-correlations. The pipeline is based on $\texttt{UFalconv2}$, a framework to generate fast, self-consistent map-level realizations of cosmological probes from input lightcones, which is applied to the $\texttt{CosmoGridV1}$ N-body simulation suite. It includes a non-Gaussian simulation-based covariance for the LSS tracers, several data compression schemes, and a neural network emulator for accelerated theoretical predictions. We validate our framework, apply it to a simulated $12\times2$pt tomographic analysis of KiDS, BOSS, and $\textit{Planck}$, and forecast constraints for a $\Lambda$CDM model with a variable neutrino mass. We find that, while the neutrino mass constraints are driven by the CMB data, the addition of LSS data helps to break degeneracies and improves the constraint by up to 35%. For a fiducial $M_\nu=0.15\mathrm{eV}$, a full combination of the above CMB+LSS data would enable a $3\sigma$ constraint on the neutrino mass. We explore data compression schemes and find that MOPED outperforms PCA. We also study the impact of an internal lensing tension in the CMB data, parametrized by $A_L$, on the neutrino mass constraint, finding that the addition of LSS to CMB data including all cross-correlations is able to mitigate the impact of this systematic. $\texttt{UFalconv2}$ and a MOPED compressed $\textit{Planck}$ CMB primary + CMB lensing likelihood are made publicly available. [abridged]