DeepCormack: Fermi Surface Tomography Using Model-based Data-driven Algorithms

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• Georg Francis Barlaup Lovric (University of Cambridge)
• Thursday 11 September 2025, 15:30-15:35
• External.

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TGM150 - 9th Edwards Symposium – Frontiers in Statistical Physics and Soft Matter

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 essential properties such as electrical conductivity, magnetism, and superconductivity. Experimental techniques for measuring the Fermi surface, such as Angular Correlation of Electron Positron Annihilation Radiation (ACAR), often suffer from poor signal-to-noise ratios, making high-quality data 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 network–based denoising and reconstruction. The approach achieves accurate reconstructions of the Fermi surface from fewer and noisier measurements, reducing reliance on long acquisition times. Moreover, the methodology is adaptable to other techniques for probing the Fermi surface via the 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 characterization of novel material properties. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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