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Radek Stompor

Publications and source records attributed to Radek Stompor.

At least 19 recordsLinked to original sources

Half-wave-plate non idealities propagated to component separated CMB $B$-modes

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 multiple modern CMB efforts, both ground-based, for instance, small aperture telescopes of Simons Observatory or satellite borne, such as LiteBIRD. Using a frequency-dependent parametric model based on the Mueller matrix formalism, we characterize the induced mixing of Stokes parameters. Through end-to-end simulations, we propagate these effects from time-ordered data to cosmology via map-making and component-separation stages, quantifying their impact on the $B$-modes power spectrum and the tensor-to-scalar ratio, $r$. Our analysis shows that neglecting the frequency dependence of a three-layer HWP gives rise to significant polarization leakage, biases foreground spectral parameters, and leads to residual contamination in the recovered CMB maps. To mitigate these effects, we investigate multiple analysis strategies progressively incorporating a more complete description of the instrumental response. At the map-making level, this requires generalizing the standard pointing matrix to account for the full time- and frequency-dependent instrumental response. We find that standard HWP models, reduce the biases only down to $r \sim 10^{-2}$, while a more advanced approach based on a generalization of both map-making and component separation, implemented using JAX, can suppress it down to $r \sim 7 \times 10^{-4}$. Finally, we extend this approach to a time-domain component-separation, enabling a statistically consistent treatment of instrumental response in the presence of time-domain features. We demonstrate its feasibility and validate it by performing a full end-to-end analysis, recovering results in good agreement with the map-based ones.

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Robust CMB polarisation mapmaking with a rotating half-wave plate

We present a novel mapmaking method for obtaining unbiased estimates of CMB polarisation, tailored to modern CMB experiments with a rotating half-wave plate. These experiments are exposed to strong unpolarised contaminant sources, such as atmospheric emission and ground pickup, which can be several orders of magnitude stronger than the sky signal. Our mapmaker mitigates these systematic effects by marginalising over all signals that vary slowly compared to the timescale of a polarimeter's angle rotation on the sky, while recovering high-fidelity polarisation maps. When the variability timescales of the unpolarised signals exceed a quarter of the half-wave plate rotation period, the method can produce maps with nearly optimal noise levels and minimal contamination. Furthermore, if the half-wave plate rotation period is sufficiently short relative to the beam-scale crossing time, the method efficiently mitigates the sky intensity-to-polarisation leakage. This mapmaker, named the Polarisation-Optimised Map-Making Estimator (POMME), is implemented within the open-source FURAX package and is ready for application to upcoming ground-based CMB surveys.

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Furax: A Modular JAX Framework for Linear Operators in Astrophysical and Cosmological Data Analysis

The Framework for Unified and Robust data Analysis with JAX (Furax) is an open-source Python framework for modeling data acquisition systems and solving inverse problems in astrophysics and cosmology. Built on JAX, Furax provides composable building blocks in the form of general-purpose and domain-specific linear operators, along with preconditioners and solvers for their numerical inversion. Domain-specific tools are provided for astrophysical and cosmic microwave background (CMB) data analysis$-$including map-making, instrument modeling, and astrophysical component separation$-$with a modular architecture designed to extend to other fields.

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Recovering unbiased CMB polarization maps using modern ground-based experiments with minimal assumptions about atmospheric emission

We present a study of unbiased reconstruction of cosmic microwave background (CMB) polarization maps from data collected by modern ground-based observatories. Atmospheric emission is a major source of correlated noise in such experiments, complicating the recovery of faint cosmological signals. We consider estimators that require minimal assumptions about unpolarized atmospheric emission properties, instead exploiting hardware solutions commonly implemented in modern instruments, such as pairs of orthogonal antennas in each focal plane pixel, and polarization signal modulation via a continuously rotating half-wave plate (HWP). We focus on two techniques: (i) statistical down-weighting of low-frequency atmospheric signals, and (ii) pair-differencing (PD), which involves differencing signals collected by two detectors in the same focal plane pixel. We compare their performance against the idealized case where the atmospheric signal is perfectly known and cleanly subtracted. We show that PD can be derived from maximum likelihood principles under general assumptions about the atmospheric signal, optimizing map sensitivity. In the absence of instrumental systematics but with reasonable detector noise variations, PD yields polarized sky maps with noise levels only slightly worse than the ideal case. While down-weighting could match this performance, it requires highly accurate atmospheric models that are not readily available. PD performance is affected by instrumental systematics, particularly those leaking atmospheric signal to the difference time stream. However, effects like gain mismatch are efficiently mitigated by a rotating HWP, making PD a competitive, robust, and efficient solution for CMB polarization mapmaking without atmospheric modeling.

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Validating a main beam treatment of parametric, pixel-based component separation in the context of CMB observations

We implement a simple, main beam correction in the maximum-likelihood, parametric component separation approach, which allows on accounting for different beamwidths of input maps at different frequencies without any preprocessing. We validate the approach on full-sky and cut-sky simulations and discuss the importance and impact of the assumptions and simplifications. We find that, in the cases when the underlying sky model is indeed parametric, the method successfully recovers component spectral parameters and component maps at the pre-defined resolution. The improvement on the precision of the estimated spectral parameters is found to be minor due to the redness of the foreground angular spectra, however the method is potentially more accurate, in particular if the foreground properties display strong, spatial variability, as it does not assume commutation of the beam smoothing and mixing matrix operators. The method permits a reconstruction of the CMB map with a resolution significantly superior to that of the lowest resolution map used in the analysis and with the nearly optimal noise level, facilitating exploitation of the cosmological information contained on angular scales, which would be otherwise inaccessible. The method preserves all the advantages of a pixel-domain implementation of the parametric approach, and, as it deals with the beams in the harmonic domain, it can also straightforwardly account for spatially stationary map-domain noise correlations.

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Pixel domain implementation of the Minimally Informed CMB MAp foreground Cleaning method

High fidelity separation of astrophysical foreground contributions from the cosmic microwave background (CMB) signal has been recognized as one of the main challenges of modern CMB data analysis, and one which needs to be addressed in a robust way to ensure that the next generation of CMB polarization experiments lives up to its promise. In this work we consider the non-parametric maximum likelihood CMB cleaning approach recently proposed by some of the authors which has been shown to match the performance of standard parametric techniques for simple foreground models, while superseding it in cases where the foregrounds do not exhibit a simple frequency dependence. We present a new implementation of the method in pixel space, extending its functionalities to account for spatial variability of the properties of the foregrounds. We describe the algorithmic details of our approach and its validation against the original code as well as the parametric method for various experimental set-ups and different models of the foreground components. We argue that the method provides a compelling alternative to other state-of-the-art techniques.

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Non-parametric maximum likelihood component separation for CMB polarization data

Mitigation of the impact of foreground contributions to measurements of Cosmic Microwave Background (CMB) polarization is a crucial step in modern CMB data analysis and is of particular importance for a detection of large-scale CMB $B$ modes. A large variety of techniques, based on different assumptions and aiming at either a full component separation or merely cleaning the foreground signals from the CMB maps, have been described in the literature. In this work, we consider this problem within a unified framework based on the maximum likelihood principle, under the assumption that the signal at each frequency can be represented as a linear mixture of sky templates. We discuss the impact of various additional assumptions on the final outcome of the procedure. We find that the component separation problem can be fully solved in two specific situations: when we either know the frequency scaling of all the components or can correctly model them with a limited number of unknown parameters, as is the case in the parametric component separation techniques; or when we either know the statistical properties of all the components, the foregrounds and CMB, or can correctly model them with a limited number of parameters, as for instance in SMICA-like approaches. However, we also show that much less stringent assumptions are sufficient if we only aim at recovering the cleaned CMB signal. In particular, we discuss a ``minimally informed'' non-parametric method based on maximum likelihood. The method only assumes that the component properties are independent on the sky direction, at least over some region of the sky, and that the CMB covariance is known up to some limited number of parameters. We apply this method to recover the CMB $B$ modes polarization signal in the context of forthcoming CMB experiments and compare its performance with that of the standard parametric... (abridged)

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Characterising cosmic birefringence in the presence of galactic foregrounds and instrumental systematic effects

We study a possibility of constraining isotropic cosmic birefringence with help of cosmic microwave background polarisation data in the presence of polarisation angle miscalibration without relying on any assumptions about the Galactic foreground angular power spectra and in particular on their EB correlation. We propose a new analysis framework based on a generalised parametric component separation approach, which accounts simultaneously on the presence of galactic foregrounds, relevant instrumental effects and external priors. We find that upcoming multi-frequency CMB data with appropriate calibration priors will allow producing an instrumental-effect-corrected and foreground-cleaned CMB map, which can be used to estimate the isotropic birefringence angle and the tensor-to-scalar ratio, accounting on statistical and systematic uncertainties incurred during the entire procedure. In particular, in the case of a Simons Observatory-like, three Small Aperture Telescopes, we derive an uncertainty on the birefringence angle of $σ(β_{b}) = 0.07^\circ$ (0.1$^\circ$), assuming the standard cosmology and calibration priors for all (single) frequency channels with the precision of $σ(α_i)= 0.1^\circ$ as aimed at by the near future ground-based experiments. This implies that these experiments could confirm or disprove the recently detected value of $β_b=0.35^\circ$ with a significance between $3$ and $5 σ$. [abridged version]

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Improved Gibbs samplers for Cosmic Microwave Background power spectrum estimation

We study different variants of the Gibbs sampler algorithm from the perspective of their applicability to the estimation of power spectra of the cosmic microwave background (CMB) anisotropies. These include approaches studied earlier in the CMB literature as well as new ones which are proposed in this work. We demonstrate all these variants on full and cut sky simulations and compare their performance, assessing both their computational and statistical efficiency. For this we employ a consistent comparison metric, an effective sample size (ESS) per second, commonly used in this context in the statistical literature. We show that one of the proposed approaches, referred to as Centered overrelax, which capitalizes on additional, auxiliary variables to minimize computational time needed per sample, and uses overrelaxation to decorrelate subsequent samples, performs better than the standard Gibbs sampler by a factor between one and two orders of magnitude in the nearly full-sky, satellite-like cases. It therefore potentially provides an interesting alternative to the currently favored approaches.

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MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experiments

Forthcoming cosmic microwave background (CMB) polarized anisotropy experiments have the potential to revolutionize our understanding of the Universe and fundamental physics. The sought-after, tale-telling signatures will be however distributed over voluminous data sets which these experiments will collect. These data sets will need to be efficiently processed and unwanted contributions due to astrophysical, environmental, and instrumental effects characterized and efficiently mitigated in order to uncover the signatures. This poses a significant challenge to data analysis methods, techniques, and software tools which will not only have to be able to cope with huge volumes of data but to do so with unprecedented precision driven by the demanding science goals posed for the new experiments. A keystone of efficient CMB data analysis are solvers of very large linear systems of equations. Such systems appear in very diverse contexts throughout CMB data analysis pipelines, however they typically display similar algebraic structures and can therefore be solved using similar numerical techniques. Linear systems arising in the so-called map-making problem are one of the most prominent and common ones. In this work we present a massively parallel, flexible and extensible framework, comprised of a numerical library, MIDAPACK, and a high level code, MAPPRAISER, which provide tools for solving efficiently such systems. The framework implements iterative solvers based on conjugate gradient techniques: enlarged and preconditioned using different preconditioners. We demonstrate the framework on simulated examples reflecting basic characteristics of the forthcoming data sets issued by ground-based and satellite-borne instruments, executing it on as many as 16,384 compute cores. The software is developed as an open source project freely available to the community at: https://github.com/B3Dcmb/midapack .

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Framework for analysis of next generation, polarised CMB data sets in the presence of galactic foregrounds and systematic effects

Reaching the sufficient sensitivity to detect primordial B-modes requires modern CMB polarisation experiments to rely on new technologies, necessary for the deployment of arrays thousands of detectors with a broad frequency coverage and operating them for extended periods of time. This increased complexity of experimental design unavoidably introduces new instrumental and systematic effects, which may impact performance of the new instruments. In this work we extend the standard data analysis pipeline by including a (parametric) model of instrumental effects directly in the data model. We then correct for them in the analysis, accounting for the additional uncertainty in the final results. We embed these techniques within a general, end-to-end formalism for estimating the impact of the instrument and foreground models on constraints on the amplitude of the primordial B-mode signal. We focus on the parametric component separation approach which we generalize to allow for simultaneous estimation of instrumental and foreground parameters. We demonstrate the framework by studying the effects induced by an achromatic half-wave plate (HWP), which lead to a frequency-dependent variation of the instrument polarisation angle, and experimental bandpasses which define observational frequency bands. We assume a typical Stage-3 CMB polarisation experiment, and show that maps recovered from raw data collected at each frequency band will unavoidably be linear mixtures of the Q and U Stokes parameters. We then derive a new generalized data model appropriate for such cases, and extend the component separation approach to account for it. We find that some of the instrumental parameters, in particularly those describing the HWP can be successfully constrained by the data themselves without need for external information, while others, like bandpasses, need to be known with good precision in advance.

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Measurement of the Cosmic Microwave Background Polarization Lensing Power Spectrum from Two Years of POLARBEAR Data

We present a measurement of the gravitational lensing deflection power spectrum reconstructed with two seasons cosmic microwave background polarization data from the POLARBEAR experiment. Observations were taken at 150 GHz from 2012 to 2014 which survey three patches of sky totaling 30 square degrees. We test the consistency of the lensing spectrum with a Cold Dark Matter (CDM) cosmology and reject the no-lensing hypothesis at a confidence of 10.9 sigma including statistical and systematic uncertainties. We observe a value of A_L = 1.33 +/- 0.32 (statistical) +/- 0.02 (systematic) +/- 0.07 (foreground) using all polarization lensing estimators, which corresponds to a 24% accurate measurement of the lensing amplitude. Compared to the analysis of the first year data, we have improved the breadth of both the suite of null tests and the error terms included in the estimation of systematic contamination.

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Characterizing bias on large scale CMB $B$-modes after galactic foregrounds cleaning

We study the performance of a typical near-future full sky CMB space mission, aiming at the characterization of the large scale $B$-modes polarization anisotropies with precision on $r\sim 0.001$, after a map-based parametric cleaning of galactic dust and synchrotron, and in the case of spatially varying astrophysical spectral emission laws. Ignoring the spatial variability of the spectral emission laws may result in a bias on $r$ as high as $\mathcal{O}(0.01)$ for realistic models of the variability. However, we show that the component separation formalism can be extended to suppress this bias efficiently. We demonstrate this within the context of the semianalytic formalism of~\citep{Stompor2016}, which we generalize to such cases and use it to propagate the foreground residuals to a cosmological likelihood on tensor-to-scalar ratio, $r$. In particular, we investigate the effects due to introducing extra, independent sets of scaling parameters for different sky areas and including additional scaling parameters per sky area. We then show that the residuals resulting in such cases can be efficiently described with help of extra terms introduced in a model of the covariance of the component-separated CMB maps and which lead to suppression of the bias on $r$ down to the level lower than the expected statistical uncertainty. We discuss how these additional terms can be constructed self-consistently from the available data.

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Impact of Polarized Galactic Foreground Emission on CMB Lensing Reconstruction and Delensing of B-Modes

Next generation CMB experiments such as CMB-S4 aim at measuring the CMB lensing potential at sub-percent precision where most of the constraining power will come from CMB polarization. We investigate the prospects of achieving this goal in the presence of large-scale, diffuse galactic foreground emission by using non-Gaussian sky simulations and exploit multi-frequency information to clean those. We show that, while prior to foreground cleaning, cosmological parameter estimates from the contaminated lensing potential estimation can be significantly biased, these can be successfully mitigated by applying a parametric foreground cleaning approach. We further observe no significant additional bias in the delensed B-mode power spectrum after applying foreground cleaning and are therefore able to obtain an unbiased measurement of the tensor-to-scalar ratio, $r$, after delensing.

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Evidence for the Cross-correlation between Cosmic Microwave Background Polarization Lensing from POLARBEAR and Cosmic Shear from Subaru Hyper Suprime-Cam

We present the first measurement of cross-correlation between the lensing potential, reconstructed from cosmic microwave background (CMB) {\it polarization} data, and the cosmic shear field from galaxy shapes. This measurement is made using data from the POLARBEAR CMB experiment and the Subaru Hyper Suprime-Cam (HSC) survey. By analyzing an 11~deg$^2$ overlapping region, we reject the null hypothesis at 3.5$σ$\ and constrain the amplitude of the {\bf cross-spectrum} to $\widehat{A}_{\rm lens}=1.70\pm 0.48$, where $\widehat{A}_{\rm lens}$ is the amplitude normalized with respect to the Planck~2018{} prediction, based on the flat $Λ$ cold dark matter cosmology. The first measurement of this {\bf cross-spectrum} without relying on CMB temperature measurements is possible due to the deep POLARBEAR map with a noise level of ${\sim}$6\,$μ$K-arcmin, as well as the deep HSC data with a high galaxy number density of $n_g=23\,{\rm arcmin^{-2}}$. We present a detailed study of the systematics budget to show that residual systematics in our results are negligibly small, which demonstrates the future potential of this cross-correlation technique.

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Iterative map-making with two-level preconditioning for polarized Cosmic Microwave Background data sets

An estimation of the sky signal from streams of Time Ordered Data (TOD) acquired by Cosmic Microwave Background (\cmb) experiments is one of the most important steps in the context of \cmb data analysis referred to as the map-making problem. The continuously growing \cmb data sets render the \cmb map-making problem more challenging in terms of computational cost and memory in particular in the context of ground based experiments. In this context, we study a novel class of the Preconditioned Conjugate Gradient (PCG) solvers which invoke two-level preconditioners. We compare them against PCG solvers commonly used in the map-making context considering their precision and time-to-solution. We compare these new methods on realistic, simulated data sets reflecting the characteristics of current and forthcoming \cmb ground-based experiment. We develop an embarrassingly parallel implementation of the approach where each processor performs a sequential map-making for a subset of the TOD. We find that considering the map level residuals the new class of solvers permits achieving tolerance of up to 3 orders of magnitude better than the standard approach, where the residual level often saturates before convergence is reached. This corresponds to an important improvement in the precision of recovered power spectra in particular on the largest angular scales. The new method also typically requires fewer iterations to reach a required precision and thus shorter runtimes for a single map-making solution. However, the construction of an appropriate two-level preconditioner can be as costly as a single standard map-making run. Nevertheless, if the same problem needs to be solved multiple times, e.g., as in Monte Carlo simulations, this cost has to be incurred only once, and the method should be competitive not only as far as its precision but also its performance is concerned.

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Making maps of Cosmic Microwave Background polarization for B-mode studies: the POLARBEAR example

Analysis of cosmic microwave background (CMB) datasets typically requires some filtering of the raw time-ordered data. Filtering is frequently used to minimize the impact of low frequency noise, atmospheric contributions and/or scan synchronous signals on the resulting maps. In this work we explicitly construct a general filtering operator, which can unambiguously remove any set of unwanted modes in the data, and then amend the map-making procedure in order to incorporate and correct for it. We show that such an approach is mathematically equivalent to the solution of a problem in which the sky signal and unwanted modes are estimated simultaneously and the latter are marginalized over. We investigate the conditions under which this amended map-making procedure can render an unbiased estimate of the sky signal in realistic circumstances. We then study the effects of time-domain filtering on the noise correlation structure in the map domain, as well as impact it may have on the performance of the popular pseudo-spectrum estimators. We conclude that although maps produced by the proposed estimators arguably provide the most faithful representation of the sky possible given the data, they may not straightforwardly lead to the best constraints on the power spectra of the underlying sky signal and special care may need to be taken to ensure this is the case. By contrast, simplified map-makers which do not explicitly correct for time-domain filtering, but leave it to subsequent steps in the data analysis, may perform equally well and be easier and faster to implement. We focus on polarization-sensitive measurements targeting the B-mode component of the CMB signal and apply the proposed methods to realistic simulations based on characteristics of an actual CMB polarization experiment, POLARBEAR.

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Forecasting performance of CMB experiments in the presence of complex foreground contaminations

We present a new, semi-analytic framework for estimating the level of residuals present in CMB maps derived from multi-frequency Cosmic Microwave Background (CMB) data and forecasting their impact on cosmological parameters. The data are assumed to contain non-negligible signals of astrophysical and/or Galactic origin, which we clean using parametric component separation technique. We account for discrepancies between the foreground model assumed during the separation procedure and the true one, allowing for differences in scaling laws and/or their spatial variations. Our estimates and their uncertainties include both systematic and statistical effects and are averaged over the instrumental noise and CMB signal realizations. The framework can be further extended to account self-consistently for existing uncertainties in the foreground models. We demonstrate and validate the framework on simple study cases which aim at estimating the tensor-to-scalar ratio, r. The proposed approach is computationally efficient permitting an investigation of hundreds of set-ups and foreground models on a single CPU.

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