kNN×KDE Archives

Test results when using the complete properties dataset where the 150 test planets are treated as transit observations, with missing mass values. The left-hand plot shows four proposed imputation algorithms alongside the mBM code in TLG2020. The right-hand plot shows the comparison between the observed mass and imputed mass for the mBM code and the kNN×KDE algorithm. The figure legend shows the average error across all 150 plotted planets. The diagonal dashed line marks a perfect correspondence between the observed and imputed values. The distributions of the three planets marked in the right-hand legend are shown below. -- astro-ph.EP

Estimating Exoplanet Mass Using Machine Learning on Incomplete Datasets

by Keith CowingOctober 11, 2024October 11, 2024

The exoplanet archive is an incredible resource of information on the properties of discovered extrasolar planets, but statistical analysis has been limited by the number of missing values. One of […]

kNN×KDE

Estimating Exoplanet Mass Using Machine Learning on Incomplete Datasets

ALMA Central Molecular Zone Exploration Survey (ACES) I: Overview

RNA And Proteins Joined Up At The Origins Of Life: Persistence Is The Point

Selection For Growth Drives The Emergence Of Genetic Heredity In Protocells

NASA SMD ROSES-25 Amendment 65: New Opportunity: C.4 Planetary Science Enabling Facilities

TESS Photometry and Radial Velocity Analysis of the sub-Neptune Exoplanet π Mensae c and the Wider π Mensae Planetary System