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Found 9 papers, 2 papers with code
Fisher Matrix Stability
Fisher forecasts are a common tool in cosmology with applications ranging from survey planning to the development of new cosmological probes.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs).
Cosmology and Nongalactic Astrophysics
Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances and Weak Lensing
We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset.
Cosmology and Nongalactic Astrophysics
Estimating redshift distributions using Hierarchical Logistic Gaussian processes
Using published galaxy-dark matter bias measurements from the Illustris simulation, we compare these systematic biases with the statistical error budget from a forecasted weak gravitational lensing measurement.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
Our first model, CNN$_\text{1D}$, infers cluster mass directly from the distribution of member galaxy line-of-sight velocities.
Cosmology and Nongalactic Astrophysics
Stacking for machine learning redshifts applied to SDSS galaxies
We observe a significant improvement of between 1. 9% and 21% on all computed metrics when stacking is applied to weak learners (such as SOMs and decision trees).
Anomaly detection for machine learning redshifts applied to SDSS galaxies
We present an analysis of anomaly detection for machine learning redshift estimation.
Cosmology and Nongalactic Astrophysics
Feature importance for machine learning redshifts applied to SDSS galaxies
When compared to the SDSS photometric redshifts, the RDF machine learning redshifts both decreases the standard deviation of residuals scaled by 1/(1+z) by 36% from 0. 066 to 0. 041, and decreases the fraction of catastrophic outliers by 57% from 2. 32% to 0. 99%.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
