46 citations · 46 across the 1 of their papers we have counts for
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astro-ph.GA2020★ 46 cited
A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
S. Mucesh, W. G. Hartley, A. Palmese +72
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorit…
astro-ph.GA2020
The sum of the masses of the Milky Way and M31: a likelihood-free inference approach
Pablo Lemos, Niall Jeffrey, Lorne Whiteway +3
We use Density Estimation Likelihood-Free Inference, Cold Dark Matter simulations of galaxy pairs, and data from Gaia and the Hubble Space Telescope to infer the sum…
astro-ph.GA2020
The impact of spectroscopic incompleteness in direct calibration of redshift distributions for weak lensing surveys
W. G. Hartley, C. Chang, S. Samani +74
Obtaining accurate distributions of galaxy redshifts is a critical aspect of weak lensing cosmology experiments. One of the methods used to estimate and validate redshift distribut…