Mitigation of DESI fiber assignment incompleteness effect on two-point clustering with small angular scale truncated estimators

TL;DR

This paper introduces a method to reduce the bias caused by fiber assignment incompleteness in galaxy clustering measurements by truncating small angular scales, validated through simulations for DESI data.

Contribution

The authors develop a novel truncation-based approach and theoretical modifications to mitigate fiber assignment effects in two-point clustering estimators, improving accuracy without significant data loss.

Findings

Unbiased cosmological constraints recovered with truncated estimators.

Method effectively mitigates fiber assignment effects at low computational cost.

Approach reduces sensitivity to high k modes in power spectrum analysis.

Abstract

We present a method to mitigate the effects of fiber assignment incompleteness in two-point power spectrum and correlation function measurements from galaxy spectroscopic surveys, by truncating small angular scales from estimators. We derive the corresponding modified correlation function and power spectrum windows to account for the small angular scale truncation in the theory prediction. We validate this approach on simulations reproducing the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) with and without fiber assignment. We show that we recover unbiased cosmological constraints using small angular scale truncated estimators from simulations with fiber assignment incompleteness, with respect to standard estimators from complete simulations. Additionally, we present an approach to remove the sensitivity of the fits to high $k$ modes in the theoretical power spectrum, by applying a transformation to the data vector and window matrix. We find that our method efficiently mitigates the effect of fiber assignment incompleteness in two-point correlation function and power spectrum measurements, at low computational cost and with little statistical loss.