Search Results for author:
Found 8 papers, 4 papers with code
Explaining dark matter halo density profiles with neural networks
We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles.
A robust estimator of mutual information for deep learning interpretability
We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models.
Insights into the origin of halo mass profiles from machine learning
We find two primary scales in the initial conditions (ICs) that impact the final mass profile: the density at approximately the scale of the haloes' Lagrangian patch $R_L$ ($R\sim 0. 7\, R_L$) and that in the large-scale environment ($R\sim 1. 7~R_L$).
Discovering the building blocks of dark matter halo density profiles with neural networks
The additional dimension in the representation contains information about the infalling material in the outer profiles of dark matter halos, thus discovering the splashback boundary of halos without prior knowledge of the halos' dynamical history.
Deep learning insights into cosmological structure formation
We train a three-dimensional convolutional neural network (CNN) to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses.
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
An interpretable machine learning framework for dark matter halo formation
The addition of tidal shear information does not yield an improved halo collapse model over one based on density information alone; the difference in their predictive performance is consistent with the statistical uncertainty of the density-only based model.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Machine learning cosmological structure formation
We train a machine learning algorithm to learn cosmological structure formation from N-body simulations.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
