4 papers
Large Language Models -- the Future of Fundamental Physics?
Caroline Heneka, Florian Nieser, Ayodele Ore +2
For many fundamental physics applications, transformers, as the state of the art in learning complex correlations, benefit from pretraining on quasi-out-of-domain data. The obvious…
Iterative HOMER with uncertainties
Anja Butter, Ayodele Ore, Sofia Palacios Schweitzer +10
We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap betwe…
SKATR: A Self-Supervised Summary Transformer for SKA
Ayodele Ore, Caroline Heneka, Tilman Plehn
The Square Kilometer Array will initiate a new era of radio astronomy by allowing 3D imaging of the Universe during Cosmic Dawn and Reionization. Modern machine learning is crucial…
Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN
Benedikt Schosser, Caroline Heneka, Tilman Plehn
Modern machine learning will allow for simulation-based inference from reionization-era 21cm observations at the Square Kilometre Array. Our framework combines a convolutional summ…