In:
Astronomy & Astrophysics, EDP Sciences, Vol. 675 ( 2023-07), p. A13-
Abstract:
We present the intensity foreground algorithms and model employed within the B EYOND P LANCK analysis framework. The B EYOND P LANCK analysis is aimed at integrating component separation and instrumental parameter sampling within a global framework, leading to complete end-to-end error propagation in the Planck Low Frequency Instrument (LFI) data analysis. Given the scope of the B EYOND P LANCK analysis, a limited set of data is included in the component separation process, leading to foreground parameter degeneracies. In order to properly constrain the Galactic foreground parameters, we improve upon the previous Commander component separation implementation by adding a suite of algorithmic techniques. These algorithms are designed to improve the stability and computational efficiency for weakly constrained posterior distributions. These are: (1) joint foreground spectral parameter and amplitude sampling, building on ideas from M IRAMARE ; (2) component-based monopole determination; (3) joint spectral parameter and monopole sampling; and (4) application of informative spatial priors for component amplitude maps. We find that the only spectral parameter with a significant signal-to-noise ratio using the current B EYOND P LANCK data set is the peak frequency of the anomalous microwave emission component, for which we find ν p = 25.3 ± 0.5 GHz; all others must be constrained through external priors. Future works will be aimed at integrating many more data sets into this analysis, both map and time-ordered based, thereby gradually eliminating the currently observed degeneracies in a controlled manner with respect to both instrumental systematic effects and astrophysical degeneracies. When this happens, the simple LFI-oriented data model employed in the current work will need to be generalized to account for both a richer astrophysical model and additional instrumental effects. This work will be organized within the Open Science-based C OSMOGLOBE community effort.
Type of Medium:
Online Resource
ISSN:
0004-6361
,
1432-0746
DOI:
10.1051/0004-6361/202243186
Language:
English
Publisher:
EDP Sciences
Publication Date:
2023
detail.hit.zdb_id:
1458466-9
SSG:
16,12
