Machine Learning-Driven Analysis of kSZ Maps to Predict CMB Optical Depth $\tau$

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 depth $\tau$, 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 extract $\tau$ from 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 of $\tau$, 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 approach provides a framework for robustly constraining $\tau$ and its associated uncertainty, which can enhance the analysis of upcoming CMB surveys like the Simons Observatory and CMB-S4.

arXiv comment15 pages, 7 figures, Accepted to PASP, Content matches the published version

References in corpus (19)

• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• Axiomatic Attribution for Deep Networks
• The Simons Observatory: Science goals and forecasts
• Cosmology at Low Frequencies: The 21 cm Transition and the High-Redshift Universe
• In the Beginning: The First Sources of Light and the Reionization of the Universe
• Eliminating the optical depth nuisance from the CMB with 21 cm cosmology
• An Improved Measurement of the Secondary Cosmic Microwave Background Anisotropies from the SPT-SZ + SPTpol Surveys
• Reionization on Large Scales I: A Parametric Model Constructed from Radiation-Hydrodynamic Simulations
• Reionization on Large Scales III: Predictions for Low-ell Cosmic Microwave Background Polarization and High-ell Kinetic Sunyaev-Zel'dovich Observables
• The Kinetic Sunyaev-Zel'dovich Effect from Reionization: Simulated Full Sky Maps at Arcminute Resolution
• Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning
• Helium Reionization Simulations. II. Signatures of Quasar Activity on the IGM
• Machine Learning Applied to the Reionization History of the Universe in the 21 cm Signal
• The 21 cm-kSZ-kSZ Bispectrum during the Epoch of Reionization
• Prospects for kSZ${}^2$-Galaxy Cross-Correlations during Reionization
• Understanding the Impact of Semi-numeric Reionization Models when using CNNs