Classification of observations

15th of Januar 2021

10:00-13:00

REGISTER

Note that this workshop will be completely online. The instructor will be online to answer all your questions and guide you through the theory part and the hands-on parts. No in-person presence is possible due to the COVID-19 pandemic.Agenda
10:00-11:00 - Theory part (more information coming soon)
11:00-12:00 - Hands-on session with guided exercises and examples (Python)
12:00-13:00 - Invited Talk (see below)

Invited Talk (speaker will present live)

Title: Classification of Lightcurve Variability in Kepler/TESS with Convolutional Neural Networks

Speaker: Dr. Megan Ansdell (see Team page)

Abstract:
The recent windfall of time-series photometric data (i.e., lightcurves) from large-scale space missions such as Kepler and TESS has driven the search for data analysis techniques beyond the traditional methods that rely on manual or hand-crafted approaches. Deep learning, and in particular Convolutional Neural Networks (CNNs), have proven successful with the automatic identification and/or classification of lightcurve variability. In this talk, I will showcase two recent examples in the literature. First, a project from NASA FDL 2018 where we enhanced a CNN with scientific domain knowledge to better separate exoplanet transits from false positives such as (background) eclipsing binary stars or instrument glitches in both Kepler and TESS datasets. Second, a CNN called Stella that automatically detects flares in TESS data and was recently used to study the evolution of stellar activity in young stars.

Technical Prerequisites

You will need to have access to a reasonably fast internet connection to be able to follow the live stream of the lecture. The exercises will be all carried out in Google Colab, therefore you only need Google Chrome installed on your computer. A google account is also necessary.

Know-how Prerequisites

To be able to follow this lecture, you will need an intermediate understanding of mathematics, linear algebra and statistics. No previous knowledge of neural networks is assumed.

An intermediate Python experience will be required to be able to follow and work on the exercises.