Inspiration
The code that I provided queries one or more of the astronomical archives (MAST) to retrieve a large dataset of astronomical observations.
What it does
This demonstrates the power of using computer programs to automate the process of collecting and analyzing data, which can help researchers to more efficiently and effectively understand the universe.
How we built it
This took a lot of research into ADQL and a lot of trial and error with the notebook cells.
Challenges we ran into
ADQL is pretty unforgiving of typographic errors.
Accomplishments that we're proud of
Greatly relieved to see it as complete and working as it is.
What we learned
This was a bit harder than I expected and took four times as long as scheduled. This is the life of future astronomers and astrophysicists data acquisition, EDA, data wrangling and Machine learning on the resulting cleaned data.
What's next for ADQL=SQL-for-Astrophysicists
Follow-up work that is planned, is to present the query to a series of interested scientists at the RUBIN/LSST DP0 delegate assembly, where I will discuss any potential applications or use cases for the project, or any opportunities for collaboration or expansion.
Built With
- adql
- ipython
- jupyternotebook
- mast
- python
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