A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper uses ResUNet-CMB to reconstruct cosmic polarization rotation from CMB data.
ResUNet-CMB neural network reconstructs CMB effects from noisy data.
CosmoVAE uses deep learning to fill in missing parts of the cosmic microwave background map.
We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed…
Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires hig…
Measurements of cosmic microwave background (CMB) anisotropy are ideal experiments for discovering the non-trivial global topology of the universe. To evaluate the CMB anisotropy in multiply-connected compact cosmological models, one needs to compute the eigenmodes of the Laplace-Beltrami operator. Using the direct bou…
Deep learning helps remove secondary -mode polarization to detect primordial gravitational waves.
21cmEMU speeds up EoR simulations by 10^4x, predicting key observables with sub-percent accuracy.
Study of cosmic microwave background polarization using spin random fields.