My simple ML model for Kaggle's PLAsTiCC Astronomical Classification 2018
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Updated
Dec 21, 2018 - Python
My simple ML model for Kaggle's PLAsTiCC Astronomical Classification 2018
Repo for my thesis research development at UC Riverside: "Classifying Galaxy Morphologies Using Bayesian Neural Networks for LSST"
JupyterLab kernel configuration for the LSST science pipelines
Cloud-based delivery of the LSST science pipelines
Awareness of the signal anomalies in the overscan regions of LSST images is an integral part of obtaining precise signal baselines. This is the code for a primary assessment of overscan anomalies that appear in flat images with long exposure times.
Code for "Predicting High Magnification Events in Microlensed Quasars in the Era of LSST using Recurrent Neural Networks"
How to configure CernVM FS to use LSST binary distribution [won't be updated - please see https://sw.lsst.eu]
[Paper] Characterization and Photometric Performance of the Hyper Suprime-Cam Software Pipeline
A tutorial on periodic variable star discrimination using machine learning
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