arXiv subjects

Nicoletta Krachmalnicoff

Publications and source records attributed to Nicoletta Krachmalnicoff.

At least 19 recordsLinked to original sources

arXiv2026

$B$-sure. Part II. Scattering transforms as robustness test for tensor-to-scalar ratio detection from CMB observations

Galactic foregrounds represent a major contamination to the measurement of primordial $B$-modes from observations of the Cosmic Microwave Background polarisation. Even after the application of component separation algorithms, foreground residuals may potentially still bias the estimate of the tensor-to-scalar ratio $r$, causing a false detection. In this work, we present the methodology of a robustness test for the validation of an eventual detection of primordial $B$-modes, as obtained by a future, LiteBIRD-like satellite experiment. The goal of the test is to identify the foreground residuals contamination by looking for non-Gaussian properties in the CMB $B$-modes map, recovered through blind component separation algorithms. We adopt scattering transforms (ST) as our summary statistics sensitive to the non-Gaussian features of foreground residuals and to their correlation with foregrounds tracer maps. We characterise and validate the methodology on realistic sky simulations with different levels of foregrounds complexity. The proposed test is able to identify a bias on the tensor-to-scalar ratio of $\gtrsim 10^{-3}$ in $\sim 90\%$ of our simulations, with this bias value being of the same order of the accuracy targeted by LiteBIRD. Additionally, for our particular experimental configuration, the test is passed when the bias is lower than the sensitivity on the $r$ parameter, and no warning is raised. These results provide an important step forward in the development of statistical tools for validating future measurement of cosmological parameters, against foregrounds contamination.

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arXiv2026

Interpreting map-based $E$/$B$ spectral properties of CMB foregrounds

Map-space $E$/$B$ decompositions of linear polarization are attractive for foreground and CMB analyses because they separate parity families: $B$-family patterns directly contaminate primordial tensor searches, while $E$-family patterns trace coherent Galactic structures. However, the $E$/$B$ transform is not fully local and can induce apparent spectral complexity even when the underlying sky is spectrally simple in $\underline{P}=Q+iU$. We quantify this effect for synchrotron emission using complex log--Taylor and moment expansions for $\underline{P}$, its spin-preserving projections $\underline{P}_E$ and $\underline{P}_B$, and its more standard scalar projections $E$ and $B$. We relate the coefficients of these expansions to physical mechanisms such as line-of-sight mixing, synchrotron ageing, and Faraday effects. Using simple sky models, we show how $\underline{P}_E$ and $\underline{P}_B$ reorganize the spectral behaviour of $\underline{P}$ into parity families with a clear geometric meaning. They retain interpretable amplitudes and angles and satisfy the closure relation $\underline{P}=\underline{P}_E+\underline{P}_B$, which extends to all moment orders. By contrast, scalar quantities such as $|E|$ and $|B|$ show larger induced variability of effective spectral parameters and enhanced spectral complexity, while $E+iB$ lacks interpretable polarization amplitude and angle. Finally, we present simple CMB-oriented applications: three-frequency diagnostics to test whether the sky is better described by a power law in $P$ or by separate effective laws in $(P_E,P_B)$, and idealized ILC and masking examples showing that the preferred map-space field depends on where foreground simplicity and residual contamination reside. This framework provides practical diagnostics for choosing foreground modelling, cleaning, and masking strategies in Galactic and CMB $B$-mode analyses.

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arXiv2026

Status and future development of the COSMOCal Project for absolute CMB polarization calibration

Cosmic Microwave Background (CMB) polarization measurements are pushing instrumental sensitivities to levels where calibration systematics become a dominant limitation. The large dynamic range between cosmological and Galactic emission prevents future experiments from relying only on diffuse sky measurements or standard celestial calibrators. To address this challenge, the COSmological Microwave Observations Calibrator (COSMOCal) project proposes an artificial calibration source deployed as a guest payload on a geostationary satellite, scheduled for launch by the Eutelsat group by 2030. This source will provide stable, well-characterized polarized microwave signals accessible to multiple ground-based observatories. In this work, we present the status of the project, the updated development timeline, and the refined scientific and technical requirements, defined with the observatories that plan to use this calibration source. Furthermore, we investigate the interplay between instrumental systematics and component separation in the presence of complex models of interstellar dust emission. We discuss in this paper how this can impact the recovery of the primordial signal, and whether residual calibration errors can degrade the performance of foreground cleaning algorithms.

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arXiv2026

BROOM: a python package for model-independent analysis of microwave astronomical data

We present BROOM, a new python package for the application of blind, minimum-variance component-separation techniques to microwave observations. The package enables the reconstruction of signals with known spectral energy distributions, such as the Cosmic Microwave Background (CMB), Sunyaev--Zeldovich distortions, or foreground moments, in both temperature and polarization through a suite of Internal Linear Combination (ILC) implementations, in the presence of astrophysical and instrumental contaminants. In addition, BROOM supports the blind reconstruction of coherent emission components with unknown covariance properties via a Generalized ILC (GILC) framework. Beyond component separation, the package provides tools to diagnose foreground complexity and to estimate residual contamination leaking into reconstructed maps across angular scales and sky regions. It also includes utilities to generate realistic microwave simulations for arbitrary CMB experiments and to compute angular power spectra of the resulting products. We present a comprehensive description and validation of the implemented pipelines in two representative experimental configurations: a full-sky satellite mission and a ground-based experiment. BROOM is publicly available, fully documented, and easily installable at https://github.com/alecarones/broom

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arXiv2026

Blind mitigation of foreground-induced biases on primordial $B$ modes for ground-based CMB experiments

Observations of the Cosmic Microwave Background (CMB) B-mode polarisation provide a unique probe of inflationary physics. Extracting a reliable constraint on the tensor-to-scalar ratio $r$ nonetheless demands stringent suppression of diffuse Galactic foregrounds, whose residuals can bias the inferred signal. This work introduces and evaluates two extensions of the Needlet Internal Linear Combination (NILC) framework aimed at reducing foreground-induced biases on $r$. The first extension implements the deprojection of selected foreground moments directly within the component-separation step. The second performs a likelihood-level marginalisation over residual foreground power using a data-driven template. Using Simons Observatory Small Aperture Telescope (SO-SAT) - like simulations, we show that both methods effectively control residual contamination, yielding unbiased estimates of $r$ and a consistent reconstruction of the lensing B-mode amplitude. These results indicate that enhanced foreground-mitigation strategies will be useful for next-generation CMB polarisation analyses seeking a robust detection of primordial B-modes.

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arXiv2026

Is cosmic birefringence due to dark energy or dark matter? Simulation-based inference

Simulation-based inference (SBI) is a powerful inference technique for cases where the exact functional form of the likelihood is not known. A prime example is the likelihood of cross-correlation power spectra of the cosmic microwave background (CMB) fields at low multipoles, $\ell\lesssim 10$. In this paper, we investigate a parity-violating cross-correlation between $E$- and $B$- mode polarization fields using SBI. The $EB$ correlation at low $\ell$ is essential to distinguish between possible axion dark energy and dark matter interpretations of `cosmic birefringence', a rotation of the plane of linear polarization of the CMB, recently reported from WMAP, Planck, and Atacama Cosmology Telescope data. We use neural likelihood estimation to infer the likelihood of the $EB$ correlation at low $\ell$ and show that it is highly non-Gaussian. We then employ neural posterior estimation to constrain the scalar field mass ($m_ϕ$), the cosmic birefringence amplitude ($gϕ_\mathrm{in}/2$), and the instrumental miscalibration angle ($α$), from simulated datasets. We find that the posterior on $m_ϕ$ shows two regimes, with a transition marked by $10^{-32}$ eV, highlighting a strong sensitivity to the scale dependence of cosmic birefringence. To quantify this behavior, we compute the probability $p(m_ϕ < 10^{-32}$ eV) for various fiducial values of $m_ϕ$. We find that $α$ and the contribution of lensed $B$ modes ultimately limit our ability to exclude the dark energy scenario fully.

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arXiv2025

Full-sky Models of Galactic Microwave Emission and Polarization at Sub-arcminute Scales for the Python Sky Model

Polarized foreground emission from the Galaxy is one of the biggest challenges facing current and upcoming cosmic microwave background (CMB) polarization experiments. We develop new models of polarized Galactic dust and synchrotron emission at CMB frequencies that draw on the latest observational constraints, that employ the ``polarization fraction tensor'' framework to couple intensity and polarization in a physically motivated way, and that allow for stochastic realizations of small-scale structure at sub-arcminute angular scales currently unconstrained by full-sky data. We implement these models into the publicly available Python Sky Model (PySM) software and additionally provide PySM interfaces to select models of dust and CO emission from the literature. We characterize the behavior of each model by quantitatively comparing it to observational constraints in both maps and power spectra, demonstrating an overall improvement over previous PySM models. Finally, we synthesize models of the various Galactic foreground components into a coherent suite of three plausible microwave skies that span a range of astrophysical complexity allowed by current data.

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arXiv2025

$B$-sure I: Minkowski functionals as robustness test for tensor-to-scalar ratio detection from CMB observations

The detection of primordial $B$-mode polarisation of the Cosmic Microwave Background (CMB) is a major observational goal in modern Cosmology, offering a potential window into inflationary physics through the measurement of the tensor-to-scalar ratio $r$. However, the presence of Galactic foregrounds poses significant challenges, possibly biasing the $r$ estimate. In this study we explore the viability of using Minkowski functionals (MFs) as a robustness test to validate a potential $r$ detection by identifying non-Gaussian features associated with foregrounds contamination. To do so, we simulate sky maps as observed by a LiteBIRD-like CMB experiment, with realistic instrumental and foregrounds modelling. The CMB $B$-mode signal is recovered through blind component separation algorithms, and the obtained (biased) value of $r$ is used to generate Gaussian realisation of CMB signal. Their MFs are then compared with those computed on maps contaminated by foreground residual left by component separation, looking for a detection of non-Gaussianity. Our results demonstrate that, with the experimental configuration considered here, MFs can not be reliably adopted as a robustness test of an eventual $r$ detection, as we find that in the majority of the cases MFs are not able to raise significant warnings about the non-Gaussianity induced by the presence of foreground residuals. In the most realistic and refined scenario we adopted, the test is able to flag non-Gaussianity in $\sim 26\%$ of the simulations, meaning that there is no warning on the biased tensor-to-scalar ratio in $\sim 74\%$ of cases. These results suggest that more advanced statistics than MFs must be considered to look for non-Gaussian signatures of foregrounds, in order to be able to perform reliable null tests in future CMB missions.

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arXiv2025

How bad could it be? Modelling the 3D complexity of the polarised dust signal using moment expansion

The variation of the physical conditions across the three dimensions of our Galaxy is a major source of complexity for the modelling of the foreground signal facing the cosmic microwave background (CMB). In the present work, we demonstrate that the spin-moment expansion formalism provides a powerful framework to model and understand this complexity, with a special focus on that arising from variations of the physical conditions along each line-of-sight on the sky. We perform the first application of the moment expansion to reproduce a thermal dust model largely used by the CMB community, demonstrating its power as a minimal tool to compress, understand and model the information contained within any foreground model. Furthermore, we use this framework to produce new models of thermal dust emission containing the maximal amount of complexity allowed by the current data, remaining compatible with the observed angular power-spectra by the $Planck$ mission. By assessing the impact of these models on the performance of component separation methodologies, we conclude that the additional complexity contained within the third dimension could represent a significant challenge for future CMB experiments and that different component separation approaches are sensitive to different properties of the moments.

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arXiv2025

The Simons Observatory: Assessing the Impact of Dust Complexity on the Recovery of Primordial $B$-modes

We investigate how dust foreground complexity can affect measurements of the tensor-to-scalar ratio, $r$, in the context of the Simons Observatory, using a cross-spectrum component separation analysis. Employing a suite of simulations with realistic Galactic dust emission, we find that spatial variation in the dust frequency spectrum, parametrized by $β_d$, can bias the estimate for $r$ when modeled using a low-order moment expansion to capture this spatial variation. While this approach performs well across a broad range of dust complexity, the bias increases with more extreme spatial variation in dust frequency spectrum, reaching as high as $r\sim0.03$ for simulations with no primordial tensors and a spatial dispersion of $σ(β_d)\simeq0.3$ -- the most extreme case considered, yet still consistent with current observational constraints. This bias is driven by changes in the $\ell$-dependence of the dust power spectrum as a function of frequency that can mimic a primordial $B$-mode tensor signal. Although low-order moment expansions fail to capture the full effect when the spatial variations of $β_d$ become large and highly non-Gaussian, our results show that extended parametric methods can still recover unbiased estimates of $r$ under a wide range of dust complexities. We further find that the bias in $r$, at the highest degrees of dust complexity, is largely insensitive to the spatial structure of the dust amplitude and is instead dominated by spatial correlations between $β_d$ and dust amplitude, particularly at higher orders. If $β_d$ does spatially vary at the highest levels investigated here, we would expect to use more flexible foreground models to achieve an unbiased constraint on $r$ for the noise levels anticipated from the Simons Observatory.

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arXiv2024

The Simons Observatory: Combining cross-spectral foreground cleaning with multitracer $B$-mode delensing for improved constraints on inflation

The Simons Observatory (SO), due to start full science operations in early 2025, aims to set tight constraints on inflationary physics by inferring the tensor-to-scalar ratio $r$ from measurements of CMB polarization $B$-modes. Its nominal design targets a precision $σ(r=0) \leq 0.003$ without delensing. Achieving this goal and further reducing uncertainties requires the mitigation of other sources of large-scale $B$-modes such as Galactic foregrounds and weak gravitational lensing. We present an analysis pipeline aiming to estimate $r$ by including delensing within a cross-spectral likelihood, and demonstrate it on SO-like simulations. Lensing $B$-modes are synthesised using internal CMB lensing reconstructions as well as Planck-like CIB maps and LSST-like galaxy density maps. This $B$-mode template is then introduced into SO's power-spectrum-based foreground-cleaning algorithm by extending the likelihood function to include all auto- and cross-spectra between the lensing template and the SAT $B$-modes. Within this framework, we demonstrate the equivalence of map-based and cross-spectral delensing and use it to motivate an optimized pixel-weighting scheme for power spectrum estimation. We start by validating our pipeline in the simplistic case of uniform foreground spectral energy distributions (SEDs). In the absence of primordial $B$-modes, $σ(r)$ decreases by 37% as a result of delensing. Tensor modes at the level of $r=0.01$ are successfully detected by our pipeline. Even with more realistic foreground models including spatial variations in the dust and synchrotron spectral properties, we obtain unbiased estimates of $r$ by employing the moment-expansion method. In this case, delensing-related improvements range between 27% and 31%. These results constitute the first realistic assessment of the delensing performance at SO's nominal sensitivity level. (Abridged)

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arXiv2024

The Simons Observatory: component separation pipelines for B-modes

The upcoming Simons Observatory (SO) Small Aperture Telescopes aim at observing the degree-scale anisotropies of the polarized CMB to constrain the primordial tensor-to-scalar ratio $r$ at the level of $σ(r=0)\lesssim0.003$ to probe models of the very early Universe. We present three complementary $r$ inference pipelines and compare their results on a set of sky simulations that allow us to explore a number of Galactic foreground and instrumental noise models, relevant for SO. In most scenarios, the pipelines retrieve consistent and unbiased results. However, several complex foreground scenarios lead to a $>2σ$ bias on $r$ if analyzed with the default versions of these pipelines, highlighting the need for more sophisticated pipeline components that marginalize over foreground residuals. We present two such extensions, using power-spectrum-based and map-based methods, and show that they fully reduce the bias on $r$ to sub-sigma level in all scenarios, and at a moderate cost in terms of $σ(r)$.

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arXiv2024

Inference of the optical depth to reionization $τ$ from $\textit{Planck}$ CMB maps with convolutional neural networks

The optical depth to reionization, $τ$, is the least constrained parameter of the cosmological $Λ$CDM model. To date, its most precise value is inferred from large-scale polarized CMB power spectra from the ${\it Planck}$ High-Frequency Instrument (HFI). These maps are known to contain significant contamination by residual non-Gaussian systematic effects, which are hard to model analytically. Therefore, robust constraints on $τ$ are currently obtained through an empirical cross-spectrum likelihood built from simulations. In this paper, we present a likelihood-free inference of $τ$ from polarized ${\it Planck}$ HFI maps which, for the first time, is fully based on neural networks (NNs). NNs have the advantage of not requiring an analytical description of the data and can be trained on state-of-the-art simulations, combining information from multiple channels. By using Gaussian sky simulations and ${\it Planck}$ ${\tt SRoll2}$ simulations, including CMB, noise, and residual instrumental systematic effects, we train, test and validate NN models considering different setups. We infer the value of $τ$ directly from $Q$ and $U$ maps at $\sim 4^\circ$ pixel resolution, without computing power spectra. On ${\it Planck}$ data, we obtain $τ_{NN}=0.058 \pm 0.008$, compatible with current EE cross-spectrum results but with a $\sim30\%$ larger uncertainty, which can be assigned to the inherent non-optimality of our estimator and to the retraining procedure applied to avoid biases. While this paper does not improve on current cosmological constraints, our analysis represents a first robust application of NN-based inference on real data and highlights its potential as a promising tool for complementary analysis of near-future CMB experiments, also in view of the ongoing challenge to achieve a detection of primordial gravitational waves.

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arXiv2024

ForSE+: Simulating non-Gaussian CMB foregrounds at 3 arcminutes in a stochastic way based on a generative adversarial network

We present ForSE+, a Python package that produces non-Gaussian diffuse Galactic thermal dust emission maps at arcminute angular scales and that has the capacity to generate random realizations of small scales. This represents an extension of the ForSE (Foreground Scale Extender) package, which was recently proposed to simulate non-Gaussian small scales of thermal dust emission using generative adversarial networks (GANs). With the input of the large-scale polarization maps from observations, ForSE+ has been trained to produce realistic polarized small scales at 3' following the statistical properties, mainly the non-Gaussianity, of observed intensity small scales, which are evaluated through Minkowski functionals. Furthermore, by adding different realizations of random components to the large-scale foregrounds, we show that ForSE+ is able to generate small scales in a stochastic way. In both cases, the output small scales have a similar level of non-Gaussianity compared with real observations and correct amplitude scaling as a power law. These realistic new maps will be useful, in the future, to understand the impact of non-Gaussian foregrounds on the measurements of the cosmic microwave background (CMB) signal, particularly on the lensing reconstruction, de-lensing, and the detection of cosmological gravitational waves in CMB polarization B-modes.

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arXiv2024

The Simons Observatory: Beam characterization for the Small Aperture Telescopes

We use time-domain simulations of Jupiter observations to test and develop a beam reconstruction pipeline for the Simons Observatory Small Aperture Telescopes. The method relies on a map maker that estimates and subtracts correlated atmospheric noise and a beam fitting code designed to compensate for the bias caused by the map maker. We test our reconstruction performance for four different frequency bands against various algorithmic parameters, atmospheric conditions and input beams. We additionally show the reconstruction quality as function of the number of available observations and investigate how different calibration strategies affect the beam uncertainty. For all of the cases considered, we find good agreement between the fitted results and the input beam model within a ~1.5% error for a multipole range l = 30 - 700 and an ~0.5% error for a multipole range l = 50 - 200. We conclude by using a harmonic-domain component separation algorithm to verify that the beam reconstruction errors and biases observed in our analysis do not significantly bias the Simons Observatory r-measurement.

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arXiv2022

A hybrid map-$C_\ell$ component separation method for primordial CMB $B$-mode searches

The observation of the polarised emission from the Cosmic Microwave Background (CMB) from future ground-based and satellite-borne experiments holds the promise of indirectly detecting the elusive signal from primordial tensor fluctuations in the form of large-scale $B$-mode polarisation. Doing so, however, requires an accurate and robust separation of the signal from polarised Galactic foregrounds. We present a component separation method for multi-frequency CMB observations that combines some of the advantages of map-based and power-spectrum-based techniques, and which is direcly applicable to data in the presence of realistic foregrounds and instrumental noise. We demonstrate that the method is able to reduce the contamination from Galactic foregrounds below an equivalent tensor-to-scalar ratio $r_{\rm FG}\lesssim5\times10^{-4}$, as required for next-generation observatories, for a wide range of foreground models with varying degrees of complexity. This bias reduction is associated with a mild $\sim20-30\%$ increase in the final statistical uncertainties, and holds for large sky areas, and for experiments targeting both the reionisation and recombination bumps in the $B$-mode power spectrum.

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arXiv2022

In-flight polarization angle calibration for LiteBIRD: blind challenge and cosmological implications

We present a demonstration of the in-flight polarization angle calibration for the JAXA/ISAS second strategic large class mission, LiteBIRD, and estimate its impact on the measurement of the tensor-to-scalar ratio parameter, r, using simulated data. We generate a set of simulated sky maps with CMB and polarized foreground emission, and inject instrumental noise and polarization angle offsets to the 22 (partially overlapping) LiteBIRD frequency channels. Our in-flight angle calibration relies on nulling the EB cross correlation of the polarized signal in each channel. This calibration step has been carried out by two independent groups with a blind analysis, allowing an accuracy of the order of a few arc-minutes to be reached on the estimate of the angle offsets. Both the corrected and uncorrected multi-frequency maps are propagated through the foreground cleaning step, with the goal of computing clean CMB maps. We employ two component separation algorithms, the Bayesian-Separation of Components and Residuals Estimate Tool (B-SeCRET), and the Needlet Internal Linear Combination (NILC). We find that the recovered CMB maps obtained with algorithms that do not make any assumptions about the foreground properties, such as NILC, are only mildly affected by the angle miscalibration. However, polarization angle offsets strongly bias results obtained with the parametric fitting method. Once the miscalibration angles are corrected by EB nulling prior to the component separation, both component separation algorithms result in an unbiased estimation of the r parameter. While this work is motivated by the conceptual design study for LiteBIRD, its framework can be broadly applied to any CMB polarization experiment. In particular, the combination of simulation plus blind analysis provides a robust forecast by taking into account not only detector sensitivity but also systematic effects.

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arXiv2021

The Simons Observatory: Galactic Science Goals and Forecasts

Observing in six frequency bands from 27 to 280 GHz over a large sky area, the Simons Observatory (SO) is poised to address many questions in Galactic astrophysics in addition to its principal cosmological goals. In this work, we provide quantitative forecasts on astrophysical parameters of interest for a range of Galactic science cases. We find that SO can: constrain the frequency spectrum of polarized dust emission at a level of $Δβ_d \lesssim 0.01$ and thus test models of dust composition that predict that $β_d$ in polarization differs from that measured in total intensity; measure the correlation coefficient between polarized dust and synchrotron emission with a factor of two greater precision than current constraints; exclude the non-existence of exo-Oort clouds at roughly 2.9$σ$ if the true fraction is similar to the detection rate of giant planets; map more than 850 molecular clouds with at least 50 independent polarization measurements at 1 pc resolution; detect or place upper limits on the polarization fractions of CO(2-1) emission and anomalous microwave emission at the 0.1% level in select regions; and measure the correlation coefficient between optical starlight polarization and microwave polarized dust emission in $1^\circ$ patches for all lines of sight with $N_{\rm H} \gtrsim 2\times10^{20}$ cm$^{-2}$. The goals and forecasts outlined here provide a roadmap for other microwave polarization experiments to expand their scientific scope via Milky Way astrophysics.

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