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MAST Staff - 2024 Jul 30

 

Updated Notebooks

The PySIAF Footprint Viewer Notebook has had two major improvements. It can now generate DS9 region files, so you can export to a viewer of your choice, like Jdaviz/Imviz. It also allows for source catalog overlays, to see which objects fall within a footprint.

New Videos

MAST Summer Webinar (Webinar YouTube Playlist)

The MAST Summer Webinar series is now complete! The last two videos have now been uploaded.

  • Lesson 4: Machine Learning and Flares. In this lesson, we:
    • Extract a stellar rotation rate from a TESS light curve using lightkurve.
    • Apply the Nyquist-Shannon Samping Theorem when finding stellar rotation rates by restricting periodogram searches.
    • Plot a correlation between two quantities (rotation rate and flare rate).
  • Lesson 5: Gaussian Process Statistics. In this lesson, we:
    • Fit a Gaussian process to a rotating star's lightcurve.
    • Understand how to apply Bayesian statistics to astrophysical problems.
    • Use Bayesian statistics to determine a population-level trend for stellar rotation as a function of flare rate.
    • Use Python code accelerators to speed up computation.

 

the Roman WFI footprint is overplotted with GAIA sources that fall within the field of view. one source is selected, highlighted in green, with tabular information about the source from GAIA shown below
PySIAF Footprint Viewer Notebook:a Roman WFI, centered on ARP 244. Each footprint is filled with smaller blue circles representing GAIA sources. One source is selected (highlighted in green), and the corresponding information from GAIA is in a table at the bottom of the image.
a screenshot from the machine learning and flares webinar. shown is a light curve for a star displaying obvious variability. less clear is the origin of multiple, sudden peaks in the otherwise regularly repeating data. the image of a dashing archive scientist is visible in the top right
MAST Summer Webinar Lesson 4: Machine Learning and Flares: this screenshot shows a variable light curve, with what appears to be a regular, "baseline" repeating pattern. Also visible are large peaks that may or may not be flares. Watch the full video to see how we use machine learning to classify these peaks.

 

 

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