Overview / Usage

We download the Kepler Telescope captured light curves from the NASA website and use this dataset to train a model that predicts the probability of the extraterrestrial object being an exoplanet. Exoplanets are planets that do not revolve around our star (sun). As a matter of fact, the Kepler Telescope was launched in space to observe the Kepler star.

Methodology / Approach

First, let’s consider how data collected by the Kepler telescope is used to detect the presence of a planet. The plot below is called a light curve, and it shows the brightness of the star (as measured by Kepler’s photometer) over time. When a planet passes in front of the star, it temporarily blocks some of the light, which causes the measured brightness to decrease and then increase again shortly thereafter, causing a “U-shaped” dip in the light curve.

However, other astronomical and instrumental phenomena can also cause the measured brightness of a star to decrease, including binary star systems, starspots, cosmic ray hits on Kepler’s photometer, and instrumental noise.

To search for planets in Kepler data, scientists use automated software (e.g. the Kepler data processing pipeline) to detect signals that might be caused by planets, and then manually follow up to decide whether each signal is a planet or a false positive. To avoid being overwhelmed with more signals than they can manage, the scientists apply a cutoff to the automated detections: those with signal-to-noise ratios above a fixed threshold are deemed worthy of follow-up analysis, while all detections below the threshold are discarded. Even with this cutoff, the number of detections is still formidable: to date, over 30,000 detected Kepler signals have been manually examined, and about 2,500 of those have been validated as actual planets!

Perhaps you’re wondering: does the signal-to-noise cutoff cause some real planet signals to be missed? The answer is, yes! However, if astronomers need to manually follow up on every detection, it’s not really worthwhile to lower the threshold, because as the threshold decreases the rate of false-positive detections increases rapidly and actual planet detections become increasingly rare. However, there’s a tantalizing incentive: it’s possible that some potentially habitable planets like Earth, which are relatively small and orbit around relatively dim stars, might be hiding just below the traditional detection threshold — there might be hidden gems still undiscovered in the Kepler data!

Technologies Used

TensorFlow

Pandas

NumPy

SciPy

AstroPy

PyDI

Bazel

Abseil Python Common Libraries

Repository

https://github.com/DikshitaDesai/exoplanet-ml