Combining electron spin resonance spectroscopy with scanning tunneling microscopy at high magnetic fields.

The continuous increase in storage densities and the desire for quantum memories and computers push the limits of magnetic characterization techniques. Ultimately, a tool that is capable of coherently manipulating and detecting individual quantum spins is needed. Scanning tunneling microscopy (STM) is the only technique that unites the prerequisites of high spatial and energy resolution, low temperature, and high magnetic fields to achieve this goal. Limitations in the available frequency range for electron spin resonance STM (ESR-STM) mean that many instruments operate in the thermal noise regime. We resolve challenges in signal delivery to extend the operational frequency range of ESR-STM by more than a factor of two and up to 100 GHz, making the Zeeman energy the dominant energy scale at achievable cryogenic temperatures of a few hundred millikelvin. We present a general method for augmenting existing instruments into ESR-STM to investigate spin dynamics in the high-field limit. We demonstrate the performance of the instrument by analyzing inelastic tunneling in a junction driven by a microwave signal and provide proof of principle measurements for ESR-STM.

Software Training in HEP.

The long-term sustainability of the high-energy physics (HEP) research software ecosystem is essential to the field. With new facilities and upgrades coming online throughout the 2020s, this will only become increasingly important. Meeting the sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g., Unix, version control, C++, and continuous integration). The second is knowledge of domain-specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving specialized techniques, including parallel programming, machine learning and data science tools, and techniques to maintain software projects at all scales. This paper discusses the collective software training program in HEP led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients for the solution of HEP computing challenges. Beyond serving the community by ensuring that members are able to pursue research goals, the program serves individuals by providing intellectual capital and transferable skills important to careers in the realm of software and computing, inside or outside HEP.