Related papers: BeyondPlanck X. Planck LFI frequency maps with sam…
In the Bayesian approach to inverse problems, data are often informative, relative to the prior, only on a low-dimensional subspace of the parameter space. Significant computational savings can be achieved by using this subspace to…
We compare the absolute gain photometric calibration of the Planck/HFI and Herschel/SPIRE instruments on diffuse emission. The absolute calibration of HFI and SPIRE each relies on planet flux measurements and comparison with theoretical…
Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, while continuous-time models often require sequential solvers prone to drift. To bridge…
We perform an analysis of the CMB temperature data taken by the Planck satellite investigating if there is any significant deviation from cosmological isotropy. We look for differences in the spectrum between two opposite hemispheres and…
The PLANCK mission is the most sensitive all-sky CMB experiment currently planned. The High Frequency Instrument (HFI) will be especially suited for observing clusters of galaxies by their thermal Sunyaev-Zel'dovich (SZ) effect. In order to…
Based on realistic simulations, we propose an hybrid method to reconstruct the lensing potential power spectrum, directly on PLANCK-like CMB frequency maps. It implies using a large galactic mask and dealing with a strong inhomogeneous…
Neural networks are popular state-of-the-art models for many different tasks.They are often trained via back-propagation to find a value of the weights that correctly predicts the observed data. Although back-propagation has shown good…
We describe a simple but efficient method for deriving a consistent set of monopole and dipole corrections for multi-frequency sky map data sets, allowing robust parametric component separation with the same data set. The computational core…
We present Flinch, a fully differentiable and high-performance framework for field-level inference on angular maps, developed to improve the flexibility and scalability of current methodologies. Flinch is integrated with differentiable…
The proposed approach extends the confidence posterior distribution to the semi-parametric empirical Bayes setting. Whereas the Bayesian posterior is defined in terms of a prior distribution conditional on the observed data, the confidence…
The rotation of the linear polarisation plane of photons during propagation, also known as cosmic birefringence, is a powerful probe of parity-violating extensions of standard electromagnetism. Using Planck legacy data, we confirm previous…
This paper provides full sky maps of foreground emission in all WMAP channels, with very low residual contamination from the Cosmic Microwave Background (CMB) anisotropies and controlled level of instrumental noise. Foreground maps are…
We present an analysis of the effects of beam deconvolution on noise properties in CMB measurements. The analysis is built around the artDeco beam deconvolver code. We derive a low-resolution noise covariance matrix that describes the…
We investigate the extent to which foreground cleaned CMB maps can be used to estimate the cosmological parameters at small scales. We use the SMICA method, a blind separation technique which works directly at the spectral level. In this…
We assess the impact of non-ideal, continuously rotating half-wave plates (HWPs) on cosmic microwave background (CMB) polarization measurements targeting large angular scale signal. Such hardware solutions are used in or planned for…
Fast Fourier Transform based phase screen simulations give accurate results only when the screen size ($G$) is much larger than the outer scale parameter ($L_0$). Otherwise, they fall short in correctly predicting both the low and high…
Radiometeric CMB measurements need to be highly stable and this stability is best obtained with differential receivers. The residual 1/f noise in the differential output is strongly dependent on the radiometer input offset which can be…
We propose a novel framework for joint magnetic resonance image reconstruction and uncertainty quantification using under-sampled k-space measurements. The problem is formulated as a Bayesian linear inverse problem, where prior…
One of the main challenges facing upcoming CMB experiments will be to distinguish the cosmological signal from foreground contamination. We present a comprehensive treatment of this problem and study how foregrounds degrade the accuracy…
In this paper we study properties of the Laplace approximation of the posterior distribution arising in nonlinear Bayesian inverse problems. Our work is motivated by Schillings et al. (2020), where it is shown that in such a setting the…