Conceitos essenciais
A deep learning model is developed to efficiently emulate galaxy intrinsic alignment correlation functions and their uncertainties from halo occupation distribution-based mock galaxy catalogs.
Resumo
The authors present a novel deep learning approach to emulate galaxy position-position (ξ), position-orientation (ω), and orientation-orientation (η) correlation function measurements and uncertainties from halo occupation distribution-based mock galaxy catalogs.
The key highlights are:
- The model uses an encoder-decoder architecture with a shared encoder and three 1D convolutional decoder heads to predict the three correlation functions simultaneously.
- The model is trained to output both point estimates and aleatoric uncertainties for the correlation functions using a mean-variance estimation procedure.
- The model achieves strong Pearson correlation values with the ground truth across all three correlation functions.
- The ξ(r) predictions are generally accurate to ≤10%, while the ω(r) and η(r) correlations exhibit larger fractional errors due to their inherent stochasticity.
- The epistemic uncertainty of the model is typically lower than the aleatoric uncertainty and is well-calibrated.
- The model can perform inference orders of magnitude faster than running the full simulation, enabling efficient modeling and parameter inference.
The authors plan to further validate the model with more complex halo occupation models, extend the correlation range, and explore parameter inference using Markov Chain Monte Carlo techniques.
Estatísticas
The galaxy-galaxy correlation function ξ(r) can reach amplitudes of O(1000) or higher at low separations r.
The position-orientation ω(r) and orientation-orientation η(r) correlations have significantly smaller amplitudes, several orders of magnitude lower than ξ(r), and are inherently very noisy.
Citações
"IA offers insights into the large-scale structure of the universe, but it is also a contaminant for weak gravitational lensing signals."
"Machine learning (ML) techniques, especially neural networks (NNs), have found wide success in the sciences with the advent of high performance computing and large datasets, particularly in astrophysics and cosmology."