Speaker
Description
Modern CMB analyses face a statistical bottleneck: the data are high-dimensional, non-Gaussian, affected by complex systematics, and often have intractable likelihoods, while the end-to-end simulations needed for Monte Carlo validation or Simulation-Based Inference are prohibitively expensive. I will present a unified framework based on the Scattering Covariance: an interpretable, physics-informed analogue of a convolutional neural network, built from fixed oriented wavelets, nonlinearities, and cross-scale/cross-channel covariance statistics. In recent work ([Campeti et al., A&A, 2025][1]), we used this representation to construct a fast map-level generative emulator for CMB instrumental systematics simulations. Even when trained on as few as ten high-fidelity simulations, the emulator generates statistically independent approximate realizations that reproduce power spectra, scattering statistics, Minkowski functionals, and pixel-covariance structure, enabling orders-of-magnitude simulation augmentation at negligible cost compared with full end-to-end campaigns. I will then describe how the same scattering-covariance latent space can be used for transparent SBI pipelines for CMB polarization in Planck, LiteBIRD and Simons Observatory data, to infer parameters such as the optical depth to reionization and the tensor-to-scalar ratio while controlling non-stationary foreground and instrumental residuals. The goal is a simulation-efficient, interpretable alternative to ``black-box'' neural inference: using a few expensive simulations as anchors for large, calibrated, statistically-controlled inference pipelines. [1]: https://www.aanda.org/articles/aa/full_html/2025/08/aa54540-25/aa54540-25.html