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Abstract Upcoming measurements of the kinetic Sunyaev–Zel’dovich (kSZ) effect, which results from Cosmic Microwave Background (CMB) photons scattering off moving electrons, offer a powerful probe of the Epoch of Reionization (EoR). The kSZ signal contains key information about the timing, duration, and spatial structure of the EoR. A precise measurement of the CMB optical depthτ, a key parameter that characterizes the universe’s integrated electron density, would significantly constrain models of early structure formation. However, the weak kSZ signal is difficult to extract from CMB observations due to significant contamination from astrophysical foregrounds. We present a machine learning approach to extractτfrom simulated kSZ maps. We train advanced machine learning models, including swin transformers, on high-resolution seminumeric simulations of the kSZ signal. To robustly quantify prediction uncertainties ofτ, we employ the Laplace Approximation (LA). This approach provides an efficient and principled Gaussian approximation to the posterior distribution over the model’s weights, allowing for reliable error estimation. We investigate and compare two distinct application modes: a post-hoc LA applied to a pre-trained model, and an online LA where model weights and hyperparameters are optimized jointly by maximizing the marginal likelihood. This proof-of-concept approach provides a framework for constrainingτand its associated uncertainty, which could be adapted for analysis of upcoming CMB surveys like the Simons Observatory and CMB-S4.
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Pairwise kinematic Sunyaev-Zel’dovich signal extraction efficacy and optical depth estimation
We determine the efficacy of the kinematic Sunyaev-Zel’dovich (kSZ) signal extraction pipeline, using pairwise kSZ measurements, in recovering unbiased estimates of the signal and inference of the associated optical depth. We consider the impact of cluster coalignments along the line of sight, the modeling of baryonic clustering, and the presence of diffuse gas, as well as instrument beam convolution and noise. We demonstrate that two complementary approaches, aperture photometry, and a matched filter, can be used to recover an unbiased estimate of the cluster kSZ signal and the associated optical depth. Aperture photometry requires a correction factor accounting for the subtraction of signal in the annulus while the matched filter requires a tuning of the signal template profile. We show that both of these can be calibrated from simulated survey data. The optical depth estimates are also consistent with those inferred from stacked thermal SZ measurements. We apply the approaches to the publicly available Atacama Cosmology Telescope (ACT) data. The techniques developed here provide a promising method to leverage upcoming kSZ measurements, from ACT, Simons Observatory, CCAT, and CMB-S4 with spectroscopic galaxy surveys from DESI, Euclid, and Roman, to constrain cosmological properties of the dark energy, gravity, and neutrino masses.
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- PAR ID:
- 10522397
- Publisher / Repository:
- Physics Review D
- Date Published:
- Journal Name:
- Physical Review D
- Volume:
- 109
- Issue:
- 2
- ISSN:
- 2470-0010
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1103/PhysRevD.109.023513
