# Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data

## TL;DR

This paper develops a deep learning neural network, AstroNet-K2, to automatically identify exoplanets in K2 data, successfully discovering two new super-Earths and advancing the automation of exoplanet detection across different galactic environments.

## Contribution

The paper introduces AstroNet-K2, a neural network adapted for K2 data, achieving high accuracy in classifying exoplanets and false positives, and demonstrates its effectiveness by discovering two new exoplanets.

## Key findings

- AstroNet-K2 achieves 98% accuracy in classification.
- Successfully identified and validated two new exoplanets.
- Method advances automated exoplanet detection in diverse galactic environments.

## Abstract

For years, scientists have used data from NASA's Kepler Space Telescope to look for and discover thousands of transiting exoplanets. In its extended K2 mission, Kepler observed stars in various regions of sky all across the ecliptic plane, and therefore in different galactic environments. Astronomers want to learn how the population of exoplanets are different in these different environments. However, this requires an automatic and unbiased way to identify the exoplanets in these regions and rule out false positive signals that mimic transiting planet signals. We present a method for classifying these exoplanet signals using deep learning, a class of machine learning algorithms that have become popular in fields ranging from medical science to linguistics. We modified a neural network previously used to identify exoplanets in the Kepler field to be able to identify exoplanets in different K2 campaigns, which range in galactic environments. We train a convolutional neural network, called AstroNet-K2, to predict whether a given possible exoplanet signal is really caused by an exoplanet or a false positive. AstroNet-K2 is highly successful at classifying exoplanets and false positives, with accuracy of 98% on our test set. It is especially efficient at identifying and culling false positives, but for now, still needs human supervision to create a complete and reliable planet candidate sample. We use AstroNet-K2 to identify and validate two previously unknown exoplanets. Our method is a step towards automatically identifying new exoplanets in K2 data and learning how exoplanet populations depend on their galactic birthplace.