Search Results for author:
Found 10 papers, 9 papers with code
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference.
Bayesian Neural Networks
In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models.
Super-resolution emulator of cosmological simulations using deep physical models
We present an extension of our recently developed Wasserstein optimized model to emulate accurate high-resolution features from computationally cheaper low-resolution cosmological simulations.
Cosmology and Nongalactic Astrophysics
Neural physical engines for inferring the halo mass distribution function
Here we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
The Quijote simulations
The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Painting halos from 3D dark matter fields using Wasserstein mapping networks
We present a novel halo painting network that learns to map approximate 3D dark matter fields to realistic halo distributions.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Fast likelihood-free cosmology with neural density estimators and active learning
Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational effects that can be successfully included in the simulations.
Cosmology and Nongalactic Astrophysics
Towards online triggering for the radio detection of air showers using deep neural networks
The detection of air-shower events via radio signals requires to develop a trigger algorithm for a clean discrimination between signal and background events in order to reduce the data stream coming from false triggers.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Data Analysis, Statistics and Probability
Automatic physical inference with information maximising neural networks
We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.
Instrumentation and Methods for Astrophysics
Deep Recurrent Neural Networks for Supernovae Classification
When using only the data for the early-epoch challenge defined by the SPCC we achieve a classification accuracy of 93. 1\%, AUC of 0. 977 and $F_1=0. 58$, results almost as good as with the whole light-curve.
