Mitigating contamination in LSS surveys: a comparison of methods

Future large-scale structure surveys will measure the locations and shapes of billions of galaxies. The precision of such catalogues will require meticulous treatment of systematic contamination of the observed fields. We compare several existing methods for removing such systematics from galaxy clustering measurements. We show how all the methods, including the popular pseudo-C-l Mode Projection and Template Subtraction methods, can be interpreted under a common regression framework and use this to suggest improved estimators. We show how methods designed to mitigate systematics in the power spectrum can be used to produce clean maps, which are necessary for cosmological analyses beyond the power spectrum, and we extend current methods to treat the next-order multiplicative contamination in observed maps and power spectra, which reduced power spectrum errors from Delta chi(2)(Cl) similar or equal to 10 to similar or equal to 1 in simulated analyses. Two new mitigation methods are proposed, which incorporate desirable features of current state-of-the-art methods while being simpler to implement. Investigating the performance of all the methods on a common set of simulated measurements from Year 5 of the Dark Energy Survey, we test their robustness to various analysis cases. Our proposed methods produce improved maps and power spectra when compared to current methods, while requiring almost no user tuning. We end with recommendations for systematics mitigation in future surveys, and note that the methods presented are generally applicable beyond the galaxy distribution to any field with spatial systematics.

Automated summary

The text discusses the meticulous treatment of systematic contamination in large-scale structure surveys, comparing existing methods for removing systematics from galaxy clustering measurements and suggesting improved estimators. It also explores methods to generate clean maps and reduce errors in power spectra, proposing two new mitigation methods. The performance of these methods is tested on simulated measurements, resulting in improved maps and power spectra with minimal user tuning, providing recommendations for systematics mitigation in future surveys.