Skip to main content

About

Registration is required for participation in the live sessions.
Register for the Workshop

The MAST Summer Webinar is comprised of 6 weekly, interactive sessions, each one hour long. Each week, we'll log into the TIKE Cloud Science Platform and work through a Jupyter Notebook. The Notebooks are designed to teach you how to work on the cloud, with a particular focus on data from the TESS Mission. Topics will include:

  • Programmatically accessing data, using APIs
  • Cloud computing
  • Machine learning
  • Exoplanet transits
  • Stellar properties: asteroseismology, flares, and rotation
  • Transient Phenomena

Since this is an interactive workshop, each session will include at least 10-15 minutes for participants to solve coding exercises. Classes are designed with all skill levels in mind, but you should have some experience with Python to get the most out of the webinar.

 

Schedule

Convert to your local time

The webinar will be run on a weekly basis, on Tuesdays (US/Eastern). We offer three sessions for your convenience, but all sessions will cover the same content, so you only need to attend one. Click on the links below to convert these options to your local time zone.

Tuesdays 10:00 US/Eastern Tuesdays 16:00 US/Eastern Tuesdays 22:30 US/Eastern*

* This special late-night option will only be offered if a sufficient number of people register, so please make sure to follow the link at the top of this page. The final schedule will be available at least 1 month before the event begins.

Sessions and Topics

Reserve these spots on your calendar now! The final schedule, with topics, will be posted at least 1 month before the event.

Webinars will be recorded; don't worry if you can't make all of the sessions.

Session Date Topic Learning Goals
1 21 May

Introduction to the Cloud and TIKE
  • Define cloud terminology: what is a “bucket” or a server? For that matter, what is the “cloud”?
  • Outline the features of the TIKE cloud platform.
  • Programmatically query MAST data by name, region, or criteria (using astroquery).
  • Load TESS data and display an image.
2 28 May TESS Mission Introduction: Exoplanets and More
  • Understand how missions like TESS and Kepler look for repeated changes in brightness to detect planets.
  • Contrast the uses of mission-generated light curves, target pixels, and full-frame images.
  • Plot a light curve using each of the three main mission-generated products.
  • List other common uses of time series data like stellar astrophysics, asteroseismology, etc.
3 4 June Asteroseismology: Not Every Signal is an Exoplanet!
  • List possible non-planetary sources of periodic signals in light curves, like binary and variable stars.
  • Model light curves and periodograms for binary or variable targets to understand the impact they can have on data.
  • Subtract a non-planetary signal from a periodogram to reveal the signal of a transiting exoplanet.
  • List possible sources of false transit positives, including the effects of TESS’s orbit and detector noise.
BREAK 11 June Break for AAS 244  
4 18 June Transient Phenomena from ZTF in TESS
  • Use astroquery to find the fields of view, start times, and end times of all TESS full-frame images (FFIs).
  • Load an online table of transient events as an astropy table.
  • Determine if a transient event (based on location and time) was observed by TESS.
  • Use lightkurve and tesscut to extract the background-subtracted lightcurve of a single pixel centered on a transient event.
5 25 June Stellar Rotation, Flares, and Machine Learning

For this Notebook, these goals are a work in progress! There are a lot of topics to cover in one hour, so the final goals may vary slightly.

  • Extract a stellar rotation rate from a TESS light curve using lightkurve
  • Apply the Nyquist-Shannon Sampling Theorem when finding stellar rotation rates by restricting periodogram searches
  • Assess the level of correlation between two quantities (rotation rate and flare rate)
6 2 July Periodogram Statistics
  • Fit a Gaussian process to a rotating star's light curve.
  • 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.