RvSpectML
Better Radial velocities from Stellar Spectroscopy via Machine Learning
Science Score: 28.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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✓Committers with academic emails
2 of 7 committers (28.6%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.1%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Better Radial velocities from Stellar Spectroscopy via Machine Learning
Basic Info
Statistics
- Stars: 11
- Watchers: 2
- Forks: 3
- Open Issues: 5
- Releases: 23
Topics
Metadata Files
README.md
RvSpectML
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RvSpectML.jl is a package to facilitate the analysis of stellar spectroscopic times series.
The primary goal is to measure extremely precise radial velocities (EPRVs).
To support that goal, it will also include tools to deal with intrinsic stellar variability and telluric variability. RvSpectML works with several other related packages.
Scope
RvSpectML.jl is currently able to: - Call EchelleInstruments.jl to: - create a manifest of files (as a DataFrame) to be ingested from a directory (custom filtering via Query.jl) - read datafiles from NEID and EXPRES into a common set of data structures, - perform basic pre-processing (filtering out some orders, pixels within an order, chunks of spectra with NaNs, normalize spectra,...) - read a line list or cross-correlation function (CCF) mask file based on ESPRESSO or VALD, - Call EchelleCCFs.jl: - compute cross-correlation function (CCF) of spectra relative to multiple CCF mask shapes efficiently, - measure RVs based on either the CCF - interpolate spectra to a new set of wavelengths using linear, sinc, or Gaussian process regression algorithms, - combine many files into a template spectra, interpolating them to a common wavelength grid and applying Doppler shift by estimated RV, - measure RVs based on a Taylor expansion of the flux, - perform Doppler-constrained PCA analysis. - Call RvSpectMLPlots.jl to: - make some common plots
RvSpectML.jl and/or its companion packages will eventually include tools to: - read datafiles from additional spectrographs into a common set of data structures, - perform additional pre-processing steps as needed, - measure RVs using additional methods, - calculated additional stellar activity indicators, and - predict contamination due to stellar variability.
Contributing
For now, please start by contributing code that you anticipate is likely useful for collaborators or other researchers.
Please keep code where you are actively experimenting with new approaches in separate github repositories. Once you have a basic working example of how to apply your methods, then please create an example demonstrating that.
Once it is reasonably mature, then please contact Eric to discuss whether to merge your code into this repo, one of the other associated repo or to keep it as an example showing how to use your method in its separate repository.
Related Packages & Repos
- RvSpectMLBase: Types, common small utilities. Minimal dependancies.
- EchelleInstruments.jl: Code specific to each instrument
- EchelleCCFs.jl: Computes CCFs with an anlytic mask
- RVSpectML (this package) holds larger algorithms and code that interconnects the component packages. (Any plotting should be outside of src and not in the Project.toml.)
- Scalpels.jl: Provides Scalpels algorithm for analyzing an ensemble of CCFs and estimating RVs and contamination from stellar variability.
- NeidArchive.jl: Julia wrapper for API to query/download data from Neid archives.
- GPLinearODEMaker: Implements a multi-variate GP time-series likelihood and optimization functions.
- RvSpectMLPlots.jl: Plotting functions/scripts/notebooks, so other packages don't get bogged down by Plots.jl
Owner
- Name: RvSpectML
- Login: RvSpectML
- Kind: organization
- Website: https://rvspectml.github.io/RvSpectML-Overview/
- Repositories: 11
- Profile: https://github.com/RvSpectML
Working towards extremely precise Radial Velocity Spectroscopy via Machine Learning
Citation (CITATION.bib)
@misc{RvSpectML.jl,
author = {Eric B. Ford, Christian Gilbertson, Joe Ninan, Michael L. Palumbo III, Alex Wise, and contributors},
title = {RvSpectML.jl},
url = {https://github.com/eford/RvSpectML.jl},
version = {v0.1.0},
year = {2020},
month = {9}
}
GitHub Events
Total
- Watch event: 1
- Push event: 1
- Pull request event: 1
- Create event: 1
Last Year
- Watch event: 1
- Push event: 1
- Pull request event: 1
- Create event: 1
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Eric Ford | e****d@p****u | 163 |
| Eric Ford | e****1@p****u | 72 |
| github-actions[bot] | 4****] | 32 |
| Alex Wise | 4****e | 10 |
| Eric Ford | e****d | 8 |
| alexander-wise | e****a@g****m | 3 |
| Christian Gilbertson | 3****l | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 1
- Total pull requests: 84
- Average time to close issues: less than a minute
- Average time to close pull requests: about 2 months
- Total issue authors: 1
- Total pull request authors: 4
- Average comments per issue: 20.0
- Average comments per pull request: 0.4
- Merged pull requests: 50
- Bot issues: 0
- Bot pull requests: 69
Past Year
- Issues: 0
- Pull requests: 9
- Average time to close issues: N/A
- Average time to close pull requests: about 10 hours
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 9
Top Authors
Issue Authors
- JuliaTagBot (1)
Pull Request Authors
- github-actions[bot] (80)
- alexander-wise (12)
- eford (2)
- christiangil (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- julia 1 total
- Total dependent packages: 1
- Total dependent repositories: 0
- Total versions: 21
juliahub.com: RvSpectML
Better Radial velocities from Stellar Spectroscopy via Machine Learning
- Documentation: https://docs.juliahub.com/General/RvSpectML/stable/
- License: MIT
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Latest release: 0.2.9
published over 1 year ago