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SUMMARY:DeepCormack: Fermi Surface Tomography Using Model-based Data-drive
 n Algorithms - Georg Francis Barlaup Lovric (University of Cambridge)
DTSTART:20250911T143000Z
DTEND:20250911T143500Z
UID:TALK233350@talks.cam.ac.uk
DESCRIPTION:The Fermi surface is a fundamental concept in condensed matter
  physics\, defining the boundary in reciprocal space between occupied and 
 unoccupied electronic states at zero temperature. Its topology governs ess
 ential properties such as electrical conductivity\, magnetism\, and superc
 onductivity. Experimental techniques for measuring the Fermi surface\, suc
 h as Angular Correlation of Electron Positron Annihilation Radiation (ACAR
 )\, often suffer from poor signal-to-noise ratios\, making high-quality da
 ta acquisition both costly and time-consuming. Inspired by recent machine 
 learning advances in medical imaging\, this work introduces DeepCormack\, 
 a hybrid framework that combines the Modified Cormack Method with neural n
 etwork&ndash\;based denoising and reconstruction. The approach achieves ac
 curate reconstructions of the Fermi surface from fewer and noisier measure
 ments\, reducing reliance on long acquisition times. Moreover\, the method
 ology is adaptable to other techniques for probing the Fermi surface via t
 he electron momentum density\, including Compton scattering. By enhancing 
 reconstruction accuracy and efficiency\, DeepCormack has the potential to 
 broaden access to Fermi surface studies and accelerate the discovery and c
 haracterization of novel material properties.&nbsp\;
LOCATION:External
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