Gregory Green's repositories
deep-potential
Deep learning for gravitational potentials, based on well-mixed tracers in phase space.
green2020-stellar-model
Data-driven model of stellar photometry, as described in Green+(2020).
heidelberg_grad_days_47
Bayesian Inference on Milky Way Datasets
highlat-dust-gp
High-Galactic-latitude 2D dust map using Gaussian Processes.
legacyviewer_tools
Tools for working with the Legacy Survey Sky Browser.
webgl-volumerender
Volume rendering of a 3D dust map using WebGL fragment shaders.
data-driven-stars
Data-driven stellar spectral energy distributions.
dataverse_utils
Utilities for interacting with the Dataverse.
gaia_XP_forward_model
Forward model of the Gaia XP spectra, learned directly from the data.
anki_tools
Various small tools to use alongside Anki.
bridging-sampler
Combinatorial space sampler using bridging, based on Lin & Fisher (2012).
crossmatching
Simple divide-and-conquer algorithm for crossmatching catalogs, using a HEALPix partitioning of the sky.
decam
simple decam observation planner (borrowing heavily from A. Patej's nightlystrategy.py)
dust_proj_simple
Simple 2D projection code for a Bayestar-like 3D dust map.
keras-preprocessing
Utilities for working with image data, text data, and sequence data.
merge_coordinates
Automatically merge coordinate systems of different spectroscopic stellar parameter catalogs.
neural_ode_dust3d
3D dust mapping, using neural ODEs to integrate line-of-sight dust.
presentations
Various presentations.
probability
Probabilistic reasoning and statistical analysis in TensorFlow
RM_applications
Example code for Soumavo and Greg how to use RoadMapping.