# CASI: A Convolutional Neural Network Approach for Shell Identification

## TL;DR

This paper introduces CASI, a deep learning convolutional neural network inspired by U-Net, designed to identify wind-driven shells and bubbles in simulated molecular cloud data with high accuracy, aiding astrophysical analysis.

## Contribution

The paper presents CASI, a novel CNN architecture tailored for shell identification in astrophysical simulations, demonstrating high accuracy and low false positives.

## Key findings

- Achieves over 90% true positive rate in shell detection
- Maintains only 1% false positive rate
- Performs well on both segmentation and regression tasks

## Abstract

We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architecture similar to U-Net and apply it to the problem of identifying wind-driven shells and bubbles using data from magneto-hydrodynamic simulations of turbulent molecular clouds with embedded stellar sources. The network is applied to two tasks, dense regression and segmentation, on two varieties of data, simulated density and synthetic 12 CO observations. Our Convolutional Approach for Shell Identification (CASI) is able to obtain a true positive rate greater than 90\%, while maintaining a false positive rate of 1\%, on two segmentation tasks and also performs well on related regression tasks. The source code for CASI is available on GitLab.