# First Attempts in Automated Defect Recognition in Superconducting Radio-Frequency Cavities

## TL;DR

This paper explores automated defect detection in superconducting RF cavities using image processing, machine learning, and decision trees, aiming to improve quality control for accelerator components.

## Contribution

It introduces new image analysis variables and compares two automated defect detection methods, including a physically validated classification approach.

## Key findings

- Automated defect detection methods show promising results.
- New surface variables improve defect characterization.
- Decision-tree classification correlates surface quality with performance.

## Abstract

The inner surface of superconducting cavities plays a crucial role to achieve highest accelerating fields. The industrial fabrication of cavities for the European X-Ray Free Electron Laser (EXFEL) and the International Linear Collider (ILC) HiGrade Research Project allowed for an investigation of this interplay with a large sample on different cavities undergoing a standardized procedure. For the serial inspection of the inner surface, the optical inspection robot OBACHT was constructed and to analyze the large amount of data, represented in the images of the inner surface, an image processing and analysis code was developed. New variables to describe the cavity surface were obtained. Two approaches using these variables and images to automatically detect defects has been implemented and tested. In addition, a decision-tree based approach of classifying defect free surfaces regarding their accelerating performance was tested and found to be physically valid.