Foreground separation and constraints on primordial gravitational waves with the PICO space mission
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 rin = 0 and...
| Autores: | , , , , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/349568 |
| Acceso en línea: | http://hdl.handle.net/10261/349568 |
| Access Level: | acceso abierto |
| Palabra clave: | CMBR experiments CMBR polarisation Gravitational waves and CMBR polarization Inflation |
| Sumario: | 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 rin = 0 and rin = 0.003. We forecast r determinations using a Gaussian likelihood assuming either no delensing or a residual lensing factor Alens = 27%. By implementing the first full-sky, post component-separation, map-domain delensing, we show that PICO should be able to achieve Alens = 22% – 24%. For four of the five foreground models we find that PICO would be able to set the constraints r < 1.3 × 10-4 to r < 2.7 × 10-4 (95%) if rin = 0, the strongest constraints of any foreseeable instrument. For these models, r = 0.003 is recovered with confidence levels between 18σ and 27σ. We find weaker, and in some cases significantly biased, upper limits when removing few low or high frequency bands. The fifth model gives a 3σ detection when rin = 0 and a 3σ bias with rin = 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 ℓ ≤ 12 are used, the non-Gaussian shape of the true likelihood gives uncertainties that are on average 30% larger than a Gaussian approximation. |
|---|