The Atacama Cosmology Telescope: High-resolution component-separated maps across one-third of the sky

TL;DR

This paper develops a needlet-ILC (NILC) component-separation framework to extract high-resolution CMB temperature, E-mode polarization, and Compton-y maps by jointly analyzing ACT DR4/DR6 data with Planck NPIPE observations over ~1/3 of the sky. Key contributions include a robust ILC bias mitigation strategy, scale- and frequency-dependent beam corrections, and the option to deproject contaminants such as the CIB and its spectral derivative, all integrated within a flexible Fourier-space filtering correction. The results are arcminute-scale Compton-y and CMB maps with improved small-scale sensitivity and large-area coverage, validated with simulations and cross-checks against Planck maps, and accompanied by a suite of simulations for uncertainty quantification. These maps enable advanced science across cluster astrophysics, cosmic velocity fields, primordial non-Gaussianity searches, and CMB lensing, while providing public data products for cross-correlation studies with external surveys.

Abstract

Observations of the millimeter sky contain valuable information on a number of signals, including the blackbody cosmic microwave background (CMB), Galactic emissions, and the Compton-$y$ distortion due to the thermal Sunyaev-Zel'dovich (tSZ) effect. Extracting new insight into cosmological and astrophysical questions often requires combining multi-wavelength observations to spectrally isolate one component. In this work, we present a new arcminute-resolution Compton-$y$ map, which traces out the line-of-sight-integrated electron pressure, as well as maps of the CMB in intensity and E-mode polarization, across a third of the sky (around 13,000 sq.~deg.). We produce these through a joint analysis of data from the Atacama Cosmology Telescope (ACT) Data Release 4 and 6 at frequencies of roughly 93, 148, and 225 GHz, together with data from the \textit{Planck} satellite at frequencies between 30 GHz and 545 GHz. We present detailed verification of an internal linear combination pipeline implemented in a needlet frame that allows us to efficiently suppress Galactic contamination and account for spatial variations in the ACT instrument noise. These maps provide a significant advance, in noise levels and resolution, over the existing \textit{Planck} component-separated maps and will enable a host of science goals including studies of cluster and galaxy astrophysics, inferences of the cosmic velocity field, primordial non-Gaussianity searches, and gravitational lensing reconstruction of the CMB.

Figures (20)

• Figure 1: Footprints of the different data sets used in this work. ACT DR4 primarily focused on observing the deep patches, denoted by "D" and "BN". Since 2016, ACT used upgraded detectors to observe significantly wider areas, denoted by "wide", to approximately similar depth. We use the subset of Planck data that lies within this "wide" region. Note that the full ACT data set extends into the Galactic plane and in this work we adopt a smaller footprint to avoid contamination from bright Galactic emissions. The excised regions within the main footprint correspond to extended sources that are also masked or inpainted.
• Figure 2: Needlets allow signals to be localized in both real and harmonic spaces. Here we plot the spectral bands used to define our needlets. Wide harmonic-space bands provide better spatial localization, whilst narrow harmonic-space bands enable better separation of signals with different scale, $\ell$, dependence. We balance these two aspects by tuning the width of the bands based on the expected properties of the sky signals. The colors are to aid differentiating one kernel from another. The dotted lines indicate scales that only include Planck data.
• Figure 3: The ILC method requires a frequency-frequency covariance matrix, which is typically not known a priori and therefore must be estimated from the data. This double use of the data, in both the weights and maps in Eq. \ref{['eq:basic_ilc']}, leads to biases in the ILC map. In simulations, this bias can be seen by computing the cross-correlation of the ILC map with the input component map. The orange line shows the size of this ILC bias in our needlet pipeline when analyzing simulations of a subset of ACT and Planck maps. The shaded contours denote the error on the mean. In this work we introduce a novel mitigation strategy, described in Section \ref{['sec:ILCBias']}, and the results of including this in our pipeline are shown in blue. This approach dramatically reduces the ILC bias.
• Figure 4: A schematic demonstrating the large-scale ILC bias mitigation strategy. The region bound by the blue lines denotes the modes selected at one needlet scale in harmonic space. Radial distance corresponds to $\ell$ and azimuth to $m$. The cross-hatched region shows modes used to estimate the weights and the red region shows the modes to which the weights are applied, hereafter the data vector. In the standard method, (a), the weights are computed from all the modes within the needlet band. The double use of each mode, in the weights and the data vector, leads to the ILC bias. In our mitigation method, (b), the weights are computed from the majority of modes within the needlet band, but we explicitly exclude the data vector modes.
• Figure 5: A schematic demonstrating the small-scale ILC bias mitigation strategy. These plots show the modes selected at one needlet scale in real space. As in Fig. \ref{['fig:LargeScalesBiasRed']}, the cross-hatched region shows modes used to estimate the weights and the red region shows the modes to which the weights are applied, that is the data vector. The double use of modes in the standard ILC, (a), for the weights and the data vector, again leads to the ILC bias. In our mitigation strategy, (b), we simply exclude modes within $\theta_i$, the localization scale of the needlet, from the weights.