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  1. ; ; ; ; ; ; ; ; ; ; et al ( , The European Physical Journal A)
    The double-spin-polarization observable E for γ p → pπ0 has been measured with the CEBAF Large Acceptance Spectrometer (CLAS) at photon beam energies Eγ from 0.367 to 2.173 GeV (corresponding to center-ofmass energies from 1.240 to 2.200 GeV) for pion center-ofmass angles, cos θc.m. π0 , between − 0.86 and 0.82. These new CLAS measurements cover a broader energy range and have smaller uncertainties compared to previous CBELSA data and provide an important independent check on systematics. These measurements are compared to predictions as well as new global fits from The George Washington University, Mainz, and Bonn-Gatchina groups. Their inclusion in multipole analyses will allow us to refine our understanding of the single-pion production contribution to the Gerasimov-Drell- Hearn sum rule and improve the determination of resonance properties, which will be presented in a future publication. 
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    Free, publicly-accessible full text available September 1, 2024
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available July 1, 2024
  3. ; ; ; ; ; ; ; ; ; ; et al ( , The European Physical Journal A)
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  4. ; ; ; ; ; ; ; ; ; ; et al ( , Progress in Particle and Nuclear Physics)
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  5. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
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  6. ; ; ; ( , Geophysical Research Letters)
    Abstract

    Detecting slow slip events (SSEs) at offshore subduction zones is important to understand the slip behavior on offshore subduction megathrusts, where tsunamis can be generated. The most widely used method to detect SSEs is to measure the vertical seafloor deformation caused by SSEs using seafloor pressure data. However, due to the small signal‐to‐noise ratio and instrumental drift, such detection is very difficult. In this study, we trained a machine learning model using synthetic data to detect SSEs and applied it to real pressure data in New Zealand between 2014 and 2015. Our method detected five events, two of which are confirmed by the onshore GPS records. Besides, our model performs better than the traditional matched filter method. We conclude that machine learning could be used to detect SSEs in real seafloor pressure data. The method can be applied to other regions, especially where near trench GPS is not available.

     
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  7. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available May 1, 2024
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review C)
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  9. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
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  10. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review C)
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