Variable Star Research with Undergraduates Using Astropy (click images for link to video clips)
- 1. Python in Astronomy 2016 Matt Craig Department of Physics and Astronomy Minnesota State University Moorhead 1
- 2. Acknowledgements •Current students •Nathan Walker •Laura Herzog •Michael Meraz •Connor Stotts •Erin Aadland •Andy Block •Elias Holte •Laura Maixner •Stefan Nelson •Jane Glanzer •Elizabeth Dougherty 2 •Colleagues •Juan Cabanela •Linda Winkler
- 3. Context •undergraduate-only program •5 ± 3 new astronomy emphasis students/ year •0.5/year go to graduate school •primary responsibility is teaching •prepare students for a (non-astronomy) career •python introduced in first calculus-based physics course, continues throughout curriculum. 3
- 4. Constraints •Some students use Windows Anaconda python distribution. •Cannot Graphical interface to python for 1st year students. •Need record of student work Traditional GUI app does not do that •Use existing, well-supported packages active dev + multiple devs + documentation + modularity = win! 4
- 5. Coding goals •Start participating early •Begin with little or no coding •Develop coding skills over 4 years • Introduce GitHub as early as possible •Introduce git when and if needed •Encourage contributions to open source projects 5
- 6. Variable Star Astronomy •Look for new variable stars in fields containing known exoplanets •Take data •Add metadata •Review data •Reduce data •Identify sources •Perform aperture photometry •Differential ensemble photometry •Analyze time series 6
- 10. Data reduction •ccdproc 1.0 released last week! •New features: •Combine images based on WCS •Cosmic ray removal with astroscrappy •Work with directories of images easily: 8
- 11. Data reduction •HUGE THANKS to Steve Crawford and all of the new contributors: •Christoph Deil •Forrest Gasdia •Carlos Gomez •Hans Moritz Günther •Nathan Heidt •Anthony Horton •Jennifer Karr •Stefan Nelson •Joe Philip Ninan 9 •Punyaslok Pattnaik •Evert Rol •William Schoenell •Michael Seifert •Sourav Singh •Brigitta Sipocz •Connor Stotts •Ole Streicher •Erik Tollerud •Nathan Walker
- 12. Data reduction •reducer: jupyter notebook with interactive widgets 10
- 13. Data reduction •reducer: jupyter notebook with interactive widgets 10
- 14. Data reduction •reducer: jupyter notebook with interactive widgets 10
- 15. Photometry, currently •Use AstroImageJ to: •Choose sources •Click on them once •Save as list •Perform aperture photometry with local background subtraction •Reject outlying pixels in annulus •Result •instrumental magnitudes for night of data •AIJ: http://arxiv.org/abs/1601.02622 11
- 17. Ensemble differential photometry •Magnitude differences to eliminate atmospheric effects •Several comparison stars •Which stars should be used as comparisons? •How should they be combined? •HELP: which approach is “best”? Is there a best? •Everett & Howell 2001 •Broeg, Fernández & Neuhäuser 2005 •lemon (on github) •Collins, Keilkopf & Stassun 2016 •OSCAAR (somewhat like Broeg et al)(on github) •AAVSO 13
- 18. Solution for now •Trial and error… •… and gatspy •Insanely fast Lomb-Scargle periodogram 14
- 19. Solution for now •Trial and error… •… and gatspy •Insanely fast Lomb-Scargle periodogram 14
- 20. Variable? 15
- 21. Image viewer •Image viewer with catalog overlay •astroquery to get catalog from Vizer •ccdproc, astropy.wcs and astropy.coordinates to handle coordinates. •mpld3 to display •transition to bqplot? •%matplotlib notebook? 16
- 22. Image viewer •Image viewer with catalog overlay •astroquery to get catalog from Vizer •ccdproc, astropy.wcs and astropy.coordinates to handle coordinates. •mpld3 to display •transition to bqplot? •%matplotlib notebook? 16
- 23. This week •differential photometry •Which approach to comparing? •Selecting comparison set •idea: use APASS to transform to standard magnitude system frame-by-frame? •reducer updates (help welcome) •start turning these demos into a package •sneak peak on github, repo is: •glowing-waffle/glowing-waffles 17
- 24. Links•lemon: http://lemon.readthedocs.org/en/latest/ •end-to-end data reduction and photometry •OSCAAR: http://oscaar.github.io/OSCAAR/ •Focuses on exoplanet transit measurements •gatspy: http://www.astroml.org/gatspy/ •fast Lomb-Scargle implementation •conda-build-all: https://github.com/SciTools/conda-build-all •eases the pain of building packages •sep: http://sep.readthedocs.org/en/v0.5.x/ •Photometry (uses internals from SExtractor) •astroquery: http://astroquery.readthedocs.org/ •Search a variety of online data sources from python. •ginga: https://ejeschke.github.io/ginga/ •Image viewer framework (and a reference viewer) •ccdproc: http://ccdproc.readthedocs.org/en/latest/ •Data reduction •photutils: https://photutils.readthedocs.org/en/latest/ •Photometry (including, but not limited to, IRAF-equivalents) •AstroImageJ: http://www.astro.louisville.edu/software/astroimagej/ •Very nice graphical interface with sophisticated fitting and graphing •reducer: http://reducer.readthedocs.org/en/latest/ •Widget-interface to ccdproc reduction •glowing-waffles: https://github.com/glowing-waffle/glowing-waffles •Very much work-in-progress, examples from today will be up there by Tue, 3/22/16 •feder_image_shuffle: https://github.com/mwcraig/feder_image_shuffle •Among other things, makes jpeg images and gallery pages, also demonstrates interacting with Github API. •msumastro: https://github.com/mwcraig/msumastro •Infrastructure for adding metadata (largely telescope specific) 18