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Found 7 papers, 1 papers with code
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.
A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys
We present a deep machine learning (ML)-based technique for accurately determining $\sigma_8$ and $\Omega_m$ from mock 3D galaxy surveys.
Cosmology and Nongalactic Astrophysics
Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning
This procedure is performed for two different mock observation series in an effort to bracket the potential enhancement in mass predictions that can be made possible by including dynamical state information.
Cosmology and Nongalactic 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
A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters
In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v.
Cosmology and Nongalactic Astrophysics
A First Look at creating mock catalogs with machine learning techniques
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties.
Cosmology and Nongalactic Astrophysics
