andersdot
applying modern computational data analysis techniques to massive astronomical surveys to understand the processes driving the Milky Way's evolution
Carnegie ObservatoriesPasadena, CA
Pinned Repositories
Chempy
Start with the Chempy tutorial
dust
infer 3D dust map from noisy distance and integrated dust values
Energy-Forecasting
Compare forecasting models of energy production and needs
extinctions
Inferring extinctions for millions of stars in the Milky Way galaxy
LyA-InvertPhase
A method to reduce unwanted sample variance when predicting Lya forest power spectra, which converges 30x faster enabling tractable inference of cosmological parameters
modelMWkinematics
NbodyPythonTools
Tools to analyze and explore large astronomical simulations of galaxy formation
photoParallax
data-driven photometric parallaxes, built with Gaia and 2MASS
radialmigration
Testing the radial migration of stars using Gaia and Apogee
AstroHackWeek2020
repo for materials for AstroHackWeek 2020
andersdot's Repositories
andersdot/Energy-Forecasting
Compare forecasting models of energy production and needs
andersdot/photoParallax
data-driven photometric parallaxes, built with Gaia and 2MASS
andersdot/Chempy
Start with the Chempy tutorial
andersdot/dust
infer 3D dust map from noisy distance and integrated dust values
andersdot/modelMWkinematics
andersdot/NbodyPythonTools
Tools to analyze and explore large astronomical simulations of galaxy formation
andersdot/radialmigration
Testing the radial migration of stars using Gaia and Apogee
andersdot/stellarSpec
andersdot/stellarTwins
andersdot/theorycmd
Fitting a theoretical cmd with a normalizing flow
andersdot/andersdot.github.io
my personal website
andersdot/ASTR599_homework
Repository for students to submit homework assignments as pull requests
andersdot/bigReds
andersdot/extinctions
Inferring extinctions for millions of stars in the Milky Way galaxy
andersdot/LyA-InvertPhase
A method to reduce unwanted sample variance when predicting Lya forest power spectra, which converges 30x faster enabling tractable inference of cosmological parameters
andersdot/WIDS2023_MI_team4e
WIDS Datathon 2023
andersdot/Bring-DevOps-to-Machine-Learning-with-CML
Leveraging the powerful features of DevOps like CI/CD, automation, workflows and apply them to our data science projects & experiments with MLOps. The CML – Continuous Machine Learning is a very handy tool have for tracking the experiment results, collaborate with others, and automating the entire workflow.
andersdot/CalculatorLibrary
andersdot/cml_base_case
andersdot/dustM31
Data-driven inferences of dust in the Andromeda Galaxy
andersdot/gaiaDR2
andersdot/highMetallicityHalo
Search for structures in the high metallicity stars in the Milky Way halo
andersdot/lf
analysis software for creating luminosity functions from ChaNGa simulations
andersdot/mockstreams
Mock observe disrupted globular clusters with Gaia errors
andersdot/MP-Gadget
massively-parallel cosmology simulator
andersdot/PDP2018
Materials for teaching the Akamai interns as part of the 2018 PDP program
andersdot/shoderivz
Speeding up inferences that include evolving physical systems
andersdot/thesis
The Little Galaxies that could Reionize the Universe