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
[Paper] Characterization and Photometric Performance of the Hyper Suprime-Cam Software Pipeline
JupyterLab kernel configuration for the LSST science pipelines
Translation and optimisation of SEDMORPH's PawlikMorph IDL code for analysing images of galaxies from SDSS data release 7
Verification test framework for DM Alert Production
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.
A package for creating hybrid solar system catalogues for making LSST predictions
Repository with the tools and scripts for "Robust period estimation using mutual information for multi-band light curves in the synoptic survey era", ApJ, 2017
Package to interact with `LSST` `OpSim` outputs for simulations of Time Domain Astronomy objects.
Developing deep learning engines (DLEs) for non-parametric modeling and extracting of information from active galactic nuclei (AGN) light-curves (LCs), which are directly related to the scientific objectives of the LSST Exploring transient optical sky. Developed DLEs Jupyter notebooks might be adaptable for modeling of light-curves of other obje…
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