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

The optical depth to reionization, $\tau$, is the least constrained parameter of the cosmological $\Lambda$CDM model. To date, its most precise value is inferred from large-scale polarized CMB power spectra from the $\textit{Planck}$ 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 $\tau$ are currently obtained through an empirical cross-spectrum likelihood built from simulations. In this paper, we present a likelihood-free inference of $\tau$ from polarized $\textit{Planck}$ 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 $\textit{Planck}$ $\texttt{SRoll2}$ simulations, including CMB, noise, and residual instrumental systematic effects, we train, test and validate NN models considering different setups. We infer the value of $\tau$ directly from $Q$ and $U$ maps at $\sim 4^\circ$ pixel resolution, without computing power spectra. On $\textit{Planck}$ data, we obtain $\tau_{\rm NN}=0.0579\pm 0.0082$, 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 on $\tau$, 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.Comment: 13 pages, 10 figures, 5 tables. Comments welcom

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.Comment: 22 pages, 21 figures, to be submitted to Ap

The Simons Observatory: Magnetic Sensitivity Measurements of Microwave SQUID Multiplexers

The Simons Observatory (SO) will be a cosmic microwave background (CMB) survey experiment with three small-aperture telescopes and one large-aperture telescope, which will observe from the Atacama Desert in Chile. In total, SO will field $\sim$70,000 transition-edge sensor (TES) bolometers in six spectral bands centered between 27 and 280 GHz in order to achieve the sensitivity necessary to measure or constrain numerous cosmological quantities. The SO Universal Focal Plane Modules (UFMs) each contain a 150 mm diameter TES detector array, horn or lenslet optical coupling, cold readout components, and magnetic shielding. SO will use a microwave SQUID multiplexing ($\mu$MUX) readout at an initial multiplexing factor of $\sim$1000; the cold (100 mK) readout components are packaged in a $\mu$MUX readout module, which is part of the UFM, and can also be characterized independently. The 100 mK stage TES bolometer arrays and microwave SQUIDs are sensitive to magnetic fields, and their measured response will vary with the degree to which they are magnetically shielded. We present measurements of the magnetic pickup of test microwave SQUID multiplexers as a study of various shielding configurations for the Simons Observatory. We discuss how these measurements motivated the material choice and design of the UFM magnetic shielding.Comment: 5 pages, 6 figures, conference proceedings submitted to IEEE Transactions on Applied Superconductivit

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 $\Delta\beta_d \lesssim 0.01$ and thus test models of dust composition that predict that $\beta_d$ 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$\sigma$ 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.Comment: Submitted to AAS journals. 33 pages, 10 figure

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 mapmaker that estimates and subtracts correlated atmospheric noise and a beam fitting code designed to compensate for the bias caused by the mapmaker. 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 a 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 an ∼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-measuremen

ForSE: A GAN-based Algorithm for Extending CMB Foreground Models to Subdegree Angular Scales

Abstract We present ForSE (Foreground Scale Extender), a novel Python package that aims to overcome the current limitations in the simulation of diffuse Galactic radiation, in the context of cosmic microwave background (CMB) experiments. ForSE exploits the ability of generative adversarial neural networks (GANs) to learn and reproduce complex features present in a set of images, with the goal of simulating realistic and non-Gaussian foreground radiation at subdegree angular scales. This is of great importance in order to estimate the foreground contamination to lensing reconstruction, delensing, and primordial B-modes for future CMB experiments. We applied this algorithm to Galactic thermal dust emission in both total intensity and polarization. Our results show how ForSE is able to generate small-scale features (at 12′) having as input the large-scale ones (80′). The injected structures have statistical properties, evaluated by means of the Minkowski functionals, in good agreement with those of the real sky and which show the correct amplitude scaling as a function of the angular dimension. The obtained thermal dust Stokes Q and U full-sky maps as well as the ForSE package are publicly available for download

ForSE: a GAN-based algorithm for extending CMB foreground models to subdegree angular scales

We present ForSE (Foreground Scale Extender), a novel Python package which aims at overcoming the current limitations in the simulation of diffuse Galactic radiation, in the context of Cosmic Microwave Background experiments (CMB). ForSE exploits the ability of generative adversarial neural networks (GANs) to learn and reproduce complex features present in a set of images, with the goal of simulating realistic and non-Gaussian foreground radiation at sub-degree angular scales. This is of great importance in order to estimate the foreground contamination to lensing reconstruction, delensing and primordial B-modes, for future CMB experiments. We applied this algorithm to Galactic thermal dust emission in both total intensity and polarization. Our results show how ForSE is able to generate small scale features (at 12 arc-minutes) having as input the large scale ones (80 arc-minutes). The injected structures have statistical properties, evaluated by means of the Minkowski functionals, in good agreement with those of the real sky and which show the correct amplitude scaling as a function of the angular dimension. The obtained thermal dust Stokes Q and U full sky maps are publicly available at https://portal.nersc.gov/project/sobs/users/ForSE

Erratum: “ForSE: A GAN-based Algorithm for Extending CMB Foreground Simulations to Subdegree Angular Scales” (2021, ApJ, 911, 1)

In the published article we used Minkowski functionals, V0, V1, and V2(Mantz et al. 2008), to analyze the statistical properties of small-scale structures produced by our generative adversarial network (GAN)-based Foreground Scale Extender (FORSE) algorithm6. The code used to produce these functionals contained an error that led to it considering only a portion of the input images in the computation. As a result, Figures 4 and 7 in the published article were impacted, and corrected versions are provided in Figures 4 and 7, respectively. The superposition of the Minkowski functional (V0, V1, and V2) computed from the small-scale features generated by the GAN and those obtained from the real total intensity observations are at the level of (64%, 61%, 60%) for Stokes I, (60%, 61%, 62%) for Q, and (64%, 62%, 63%) for U maps, respectively. Despite the lower values obtained in comparison to the previous version, these results still demonstrate the ability of FORSE to generate highly non-Gaussian features, as previously concluded. This is especially clear in the case of polarization, as shown in Figure 7, where the distribution of the functionals from the generated maps (orange) is much closer to the target distribution (black lines) than to that of a Gaussian field (green). We thank Viraj Manwadkar and Susan Clark for finding the error in our computation of Minkowski functionals and Jian Yao for correcting and accelerating the code

A hybrid map-CℓC_\ell component separation method for primordial CMB BB-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