Foreground Separation and Constraints on Primordial Gravitational Waves with the PICO Space Mission

TL;DR

The paper evaluates PICO's capability to detect or constrain primordial gravitational waves through advanced foreground separation and delensing techniques, achieving the strongest constraints among foreseeable instruments.

Contribution

It introduces a full-sky, post-component separation map-domain delensing method and assesses PICO's potential to constrain the tensor-to-scalar ratio r with unprecedented precision.

Findings

PICO can constrain r < 1.3×10⁻⁴ to 2.7×10⁻⁴ at 95% confidence.

Achieves residual lensing factor A_lens of 22%-24%.

Detects r=0.003 with high significance (18σ-27σ).

Abstract

PICO is a concept for a NASA probe-scale mission aiming to detect or constrain the tensor to scalar ratio $r$, a parameter that quantifies the amplitude of inflationary gravity waves. We carry out map-based component separation on simulations with five foreground models and input $r$ values $r_{in}=0$ and $r_{in} = 0.003$. We forecast $r$ determinations using a Gaussian likelihood assuming either no delensing or a residual lensing factor $A_{\rm lens}$ = 27%. By implementing the first full-sky, post component-separation, map-domain delensing, we show that PICO should be able to achieve $A_{\rm lens}$ = 22% - 24%. For four of the five foreground models we find that PICO would be able to set the constraints $r < 1.3 \times 10^{-4} \,\, \mbox{to} \,\, r <2.7 \times 10^{-4}\, (95\%)$ if $r_{in}=0$, the strongest constraints of any foreseeable instrument. For these models, $r=0.003$ is recovered with confidence levels between $18\sigma$ and $27\sigma$. We find weaker, and in some cases significantly biased, upper limits when removing few low or high frequency bands. The fifth model gives a $3\sigma$ detection when $r_{in}=0$ and a $3\sigma$ bias with $r_{in} = 0.003$. However, by correlating $r$ determinations from many small 2.5% sky areas with the mission's 555 GHz data we identify and mitigate the bias. This analysis underscores the importance of large sky coverage. We show that when only low multipoles $\ell \leq 12$ are used, the non-Gaussian shape of the true likelihood gives uncertainties that are on average 30% larger than a Gaussian approximation.