SearcharxivSearch

arXiv subjects

Austin Hayes

Publications and source records attributed to Austin Hayes.

2 recordsLinked to original sources

Femtosecond tunneling spectroscopy of ultrafast band bending dynamics at the atomic limit

Atomic-scale disorder shapes the potential energy landscape traversed by photoexcited charge carriers, while the carriers themselves also dynamically reshape this landscape. However, resolving ultrafast photocarrier motion at atomic length scales has remained a central challenge in materials science. Here, we demonstrate that lightwave-driven terahertz scanning tunneling microscopy (THz-STM) provides access to these dynamics by probing the ultrafast evolution of local electronic structure following resonant interband excitation. Applying this approach to the photoexcited GaAs(110) surface, we image the resulting femtosecond carrier dynamics by tracking the transient photocurrents produced by ultrafast shifts in the energy alignment of surface and bulk electronic states near individual surface defects. Supported by modeling, we experimentally resolve the time-dependent band bending produced by photoinduced charge carriers across the atomic-scale landscape of the sample surface. Crucially, we employ terahertz time-domain spectroscopy in the tip near-field to disentangle the coherent sub-cycle dynamics induced by the terahertz driving field from the intrinsic sample response. We establish a new regime of ultrafast tunneling spectroscopy that captures transient electronic structure and dynamic band alignment with unprecedented spatio-temporal resolution, which has significant implications for understanding carrier transport, defect-mediated processes, and the development of optoelectronic technologies based on dynamically tunable materials.

cond-mat.mes-hall

Waiting for Dabo: A machine learning model for predicting Power 4 college football coaching hire success

Using data on 103 recent P4 college football hires, we built a statistical model for predicting a coach's success at their new school. For each hire, we collected data about their background and experiences, the previous success as a head coach or coordinator and their success since hiring. Over 50 variables on these factors were recorded though we used 29 of these in building our predictive model. Our measure of success is based upon Bill Connelly's SP+ team ratings relative to the performance on the same metric of the school in the 15 year prior to their selection as head coach. Using a cross-validated regularized linear regression, we obtain a predictive model for coaching success. Among the important factors for predicting a successful hire are having been a previous college head coach, leaving a job as an Offensive Coordinator, age and quality of the hiring school's team in the previous 15 years. While we do find these factors are important for the prediction of a successful coaching hire, the trends here are weak. With 66\% accuracy, the model does identify coaching hires that will outperform team performance in the 15 years before the hire. However, no combination of these factors leads to high predictability of identifying a successful coaching hire. All of the data and code for this paper are available in a Github repository.

stat.AP