Finding Exoplanets With Machine Learning: the Use of Microsoft Azure Cloud Computing Services to Process NASA Tess Telescope Data
School Name
South Carolina Governor's School for Science & Mathematics
Grade Level
12th Grade
Presentation Topic
Physics
Presentation Type
Mentored
Abstract
The increase of artificial intelligence in the world is changing how we go about our lives. The vast amount of data that is becoming available provides an opportunity to discover physical objects that were hidden before. Using NASA's TESS telescope data in combination with Microsoft Azure, this study focuses on how one can use data and artificial intelligence to go as far as discover a new planet. To do this, a resource group was created on Microsoft Azure using software as a service. From the container instances inside the resource group one can process and view the data downloaded. The data measures the brightness of nearby stars and detects reflex motion. From this information, Full Frame Images are cut into Target Pixel Files to prepare light curves. A machine learning algorithm then predicts the probability of the existence of an exoplanet orbiting the star. Thousands of planets have been discovered by tracking a star's brightness in transit. This number will increase rapidly with the advent of TESS's large amount of data and the increasing sophistication of artificial intelligence. These results are important to the study of both astrophysics and data science, especially the questions of how they can be used together in future studies. It makes a point to show how the onset of artificial intelligence is making a study previously done by a PhD available to anyone with the motivation to learn.
Recommended Citation
Lundblad, Eileen, "Finding Exoplanets With Machine Learning: the Use of Microsoft Azure Cloud Computing Services to Process NASA Tess Telescope Data" (2020). South Carolina Junior Academy of Science. 192.
https://scholarexchange.furman.edu/scjas/2020/all/192
Location
Furman Hall 127
Start Date
3-28-2020 11:45 AM
Presentation Format
Oral Only
Group Project
No
Finding Exoplanets With Machine Learning: the Use of Microsoft Azure Cloud Computing Services to Process NASA Tess Telescope Data
Furman Hall 127
The increase of artificial intelligence in the world is changing how we go about our lives. The vast amount of data that is becoming available provides an opportunity to discover physical objects that were hidden before. Using NASA's TESS telescope data in combination with Microsoft Azure, this study focuses on how one can use data and artificial intelligence to go as far as discover a new planet. To do this, a resource group was created on Microsoft Azure using software as a service. From the container instances inside the resource group one can process and view the data downloaded. The data measures the brightness of nearby stars and detects reflex motion. From this information, Full Frame Images are cut into Target Pixel Files to prepare light curves. A machine learning algorithm then predicts the probability of the existence of an exoplanet orbiting the star. Thousands of planets have been discovered by tracking a star's brightness in transit. This number will increase rapidly with the advent of TESS's large amount of data and the increasing sophistication of artificial intelligence. These results are important to the study of both astrophysics and data science, especially the questions of how they can be used together in future studies. It makes a point to show how the onset of artificial intelligence is making a study previously done by a PhD available to anyone with the motivation to learn.
