arXiv (All)
open access
Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution
Unambiguous identification of Majorana zero modes (MZMs) in topological superconductors (TSCs) remains a challenge due to complex in-gap states that can also produce zero-bias conductance peaks (ZBPs). Here we demonstrate a data-driven workflow that integrates pixel-wise spectral deconvolution with
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Unsupervised machine learning of topological phase transitions from experimental data#1158055
- A Pseudoscalar Representation Mapping from Parent-Group Vibrational Normal Modes to Symmetry-Adapted Magnetic Structures#1012634
- Frequency-resolved ultrafast electron diffraction: visualizing vibrational dynamics in frequency- and real-space#818908
- Frequency-resolved ultrafast electron diffraction: visualizing vibrational dynamics in frequency- and real-space#808916
- Quasiparticle GW for Superconductors: Toward a Unified Treatment of Electron-Phonon and Electron-Plasmon Couplings#609943
Record · ID 1233780
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive