2025/12/10 by Sarkar, Debanjan, Iles, Ella, Liu, Adrian
#Cosmology and Nongalactic Astrophysics (astro-ph.CO) #Data Analysis #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2512.09984
Estimating the auto power spectrum of cosmological tracers from line-intensity mapping (LIM) data is often limited by instrumental noise, residual foregrounds, and systematics. Cross-power spectra between multiple lines offer a robust alternative, mitigating noise bias and systematics. However, inferring the auto spectrum from cross-correlations relies on two key assumptions: that all tracers are linearly biased with respect to the matter density field, and that they are strongly mutually correlated. In this work, we introduce a new diagnostic statistic, \(Q\), which serves as a data-driven null test of these assumptions. Constructed from combinations of cross-spectra between four distinct spectral lines, \(Q\) identifies regimes where cross-spectrum-based auto-spectrum reconstruction is unbiased. We validate its behavior using both analytic toy models and simulations of LIM observables, including star formation lines ([CII], [NII], [CI],[OIII]) and the 21-cm signal. We explore a range of redshifts and instrumental configurations, incorporating noise from representative surveys. Our results demonstrate that the criterion \( Q ≈ 1 \) reliably selects the modes where cross-spectrum estimators are valid, while significant deviations are an indicator that the key assumptions have been violated. The \( Q \) diagnostic thus provides a simple yet powerful data-driven consistency check for multi-tracer LIM analyses.