Speaker
Description
To advance the search for 0𝜈𝛽𝛽 decay, the LEGEND-1000 experiment will require scaling-up from its predecessor, LEGEND-200, the cryostat in particular containing a copper reentrant tube (RT) in order to create separate volumes containing underground versus atmospheric argon. As the thinnest part of the RT will only be ~3 mm thick, small-scale pressure and strain testing is underway to confirm structural simulations. During the course of these strain tests, video recordings of the testing process were taken and we opted to use machine learning (ML) techniques to perform a frame-by-frame analysis of the footage, extracting the outline of the test cylinders and thereby tracking material deformation with time and pressure.
Meta’s computer vision software Segment Anything Model 2 (SAM2) was applied to video footage of two of the pressure tests; RT diameters were extracted at multiple z-levels from the masks produced by SAM2. Optical character recognition (OCR) software was used to extract timestamps from the videos, allowing for the synchronization of RT deformation and pressure. Following a physics-based analysis, the yield strength of the copper was found to be consistent with expectations given the material and tube geometry. This demonstrates the effectiveness of non-contact ML-based strain analysis for experiment validation and opens the door for future applications in instrumentation.