Researchers developed an open-source, user-friendly program for SQUID data processing

Superconducting quantum interference devices (SQUIDs) are ubiquitous in physics due to their ability to quickly, easily and precisely measure tiny magnetic fields. However, background subtraction on SQUID data can be difficult when there is a low signal-to-background ratio. To streamline this process, Coak et al. developed SquidLab, a free-to-download and open source magnetization data processing program.

“Our background is in high pressure physics, where one needs an enormous metal cell to clamp extreme pressures around the sample being measured,” said author Matt Coak. “This was our motivation (application) at the outset, but the technique is completely general, and we quickly saw uses in all kinds of other scenarios.”

The user-friendly program is written and run in MATLAB and can be customized and integrated with the user’s own processing algorithms. After the user uploads a sample and background dataset, SquidLab conducts multidimensional interpolation to ensure the data are at the same external conditions, such as magnetic field and temperature. It then performs the background subtraction, its core operation. Once the background data has been subtracted, the result can be fitted to the expected magnetic dipole form in a variety of customizable methods.

Typically, each data point undergoes background subtraction as it is taken, but a key benefit to SquidLab is the ability to modify various parameters to optimize the result.

“SquidLab gives the user lots of power compared to the typical approach and also saves precious time as runs are not wasted,” Coak said. “You can always subtract backgrounds after the fact or redo only the bits that matter.”

SQUIDs are used in fields from materials science to medicine, and SquidLab’s customizability can provide specialized processing and fitting functionality to a diverse group of researchers.

Source: “SquidLab – a user-friendly program for background subtraction and fitting of magnetization data,” by Matthew J. Coak, Cheng Liu, David M. Jarvis, Seunghyun Park, Matthew J. Cliffe, and Paul A. Goddard, Review of Scientific Instruments (2020). The article can be accessed at https://doi.org/10.1063/1.5137820 .