Interpretable Neural Marked Statistics for Cosmological Inference
Recovering cosmological information beyond the power spectrum is a central goal for upcoming cosmological surveys, since late-time non-Gaussian signal in the matter density cannot be accessed through two-point statistics alone. Marked statistics fold part of this information back into the two-point level by reweighting the field with non-linear functions. We propose a neural marking scheme to generalize this process through a set of interpretable, physically motivated transformations that directly allow to interpret the gain in cosmological information at the morphological level. We employ a contrastive learning objective to align learnable marked summaries with the underlying cosmologica...
| Authors | Federico Semenzato, Benjamin D. Wandelt, Michele Liguori et al. |
|---|---|
| Primary category | astro-ph.CO |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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