Description
- Abstract:
- The 21cm signal of neutral hydrogen contains a wealth of information about the poorly constrained era of cosmological history, the Epoch of Reionization (EoR). Recently, AI models trained on EoR simulations have gained significant attention as a powerful and flexible option for inferring parameters from 21cm observations. However, previous works show that AI models trained on data from one simulator fail to generalize to data from another, raising doubts about AI models' ability to accurately infer parameters from observation. We develop a new strategy for training AI models on cosmological simulations based on the principle that increasing the diversity of the training dataset improves model robustness by averaging out spurious and contradictory information. We train AI models on data from different combinations of four simulators, then compare the models' performance when predicting on data from held-out simulators acting as proxies for the real universe. We find that models trained on data from multiple simulators perform better on data from a held-out simulator than models trained on data from a single simulator, indicating that increasing the diversity of the training dataset improves a model's ability to generalize. This result suggests that future EoR parameter inference methods can mitigate simulator-specific bias by incorporating multiple simulation approaches into their analyses. Finally, we explore counterfactual explanations as a possible method of interpretation and data augmentation for AI models.
- Notes:
- Thesis (Ph. D.)--Brown University, 2026
- The author acknowledges support from U.S. National Science Foundation (NSF) awards 2106510 and 2509340. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the U.S. National Science Foundation.
Citation
Solt, Jasper,
"AI Parameter Inference for the Epoch of Reionization: Addressing Issues of Model Generalizability and Interpretability"
(2026).
Physics Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/f49m-v396
