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Instruments / Technical Area	Publication Details	Published	Actions
I21-Resonant Inelastic X-ray Scattering (RIXS)	Weak signal extraction enabled by deep neural network denoising of diffraction data Jens Oppliger , M. Michael Denner , Julia Kuespert , Ruggero Frison , Qisi Wang , Alexander Morawietz , Oleh Ivashko , Ann-Christin Dippel , Martin Von Zimmermann , Izabela Bialo , Leonardo Martinelli , Benoît Fauqué , Jaewon Choi , Mirian Garcia-Fernandez , Ke-Jin Zhou , Niels Bech Christensen , Tohru Kurosawa , Naoki Momono , Migaku Oda , Fabian D. Natterer , Mark H. Fischer , Titus Neupert , Johan Chang Nature Machine Intelligence 131 DOI: 10.1038/s42256-024-00790-1 Diamond Proposal Number(s): [31819] Open Access Show Abstract ▼ Abstract: The removal or cancellation of noise has wide-spread applications in imaging and acoustics. In applications in everyday life, such as image restoration, denoising may even include generative aspects, which are unfaithful to the ground truth. For scientific use, however, denoising must reproduce the ground truth accurately. Denoising scientific data is further challenged by unknown noise profiles. In fact, such data will often include noise from multiple distinct sources, which substantially reduces the applicability of simulation-based approaches. Here we show how scientific data can be denoised by using a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction and resonant X-ray scattering data recorded on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data. This success is enabled by supervised training of a deep neural network with pairs of measured low- and high-noise data. We additionally show that using artificial noise does not yield such quantitatively accurate results. Our approach thus illustrates a practical strategy for noise filtering that can be applied to challenging acquisition problems.	Feb 2024
I05-ARPES	Electronic reconstruction forming a C2-symmetric Dirac semimetal in Ca3Ru2O7 M. Horio , Q. Wang , V. Granata , K. P. Kramer , Y. Sassa , S. Jöhr , D. Sutter , A. Bold , L. Das , Y. Xu , R. Frison , R. Fittipaldi , T. K. Kim , C. Cacho , J. E. Rault , P. Le Fèvre , F. Bertran , N. C. Plumb , M. Shi , A. Vecchione , M. H. Fischer , J. Chang Npj Quantum Materials 6 DOI: 10.1038/s41535-021-00328-3 Diamond Proposal Number(s): [20259] Open Access Show Abstract ▼ Abstract: Electronic band structures in solids stem from a periodic potential reflecting the structure of either the crystal lattice or electronic order. In the stoichiometric ruthenate Ca3Ru2O7, numerous Fermi surface-sensitive probes indicate a low-temperature electronic reconstruction. Yet, the causality and the reconstructed band structure remain unsolved. Here, we show by angle-resolved photoemission spectroscopy, how in Ca3Ru2O7 a C2-symmetric massive Dirac semimetal is realized through a Brillouin-zone preserving electronic reconstruction. This Dirac semimetal emerges in a two-stage transition upon cooling. The Dirac point and band velocities are consistent with constraints set by quantum oscillation, thermodynamic, and transport experiments, suggesting that the complete Fermi surface is resolved. The reconstructed structure—incompatible with translational-symmetry-breaking density waves—serves as an important test for band structure calculations of correlated electron systems.	Mar 2021

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