Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918532054253568 |
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| author | Park, Jewook Jeon, Hoyeon Shin, Dongwon Zhang, Guannan McGuire, Michael A Sales, Brian C. Li, An-Ping |
| author_facet | Park, Jewook Jeon, Hoyeon Shin, Dongwon Zhang, Guannan McGuire, Michael A Sales, Brian C. Li, An-Ping |
| contents | 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 machine-learning (ML) to analyze tunneling spectroscopy from FeTe0.55Se0.45, an intrinsic TSC. Based on the local density of states (LDOS) spectra acquired with a millikelvin scanning tunneling microscope under magnetic fields, each spectrum was decomposed into multiple Lorentzian peaks. The extracted peak parameters were assembled into a structured feature set and subsequently embedded and clustered with unsupervised ML algorithms. ML-based clustering identified distinct classes of LDOS spectra, separating superconductor vortices exhibiting ZBPs consistent with established characteristics of MZMs from vortices displaying ZBP-mimicking features of trivial origin. Furthermore, spatially resolved ZBP distributions differentiate isotropic vortex cores with well-defined ZBPs from vortices that exhibit locally distorted ZBPs. By comparing the ZBP distributions to defect locations measured without magnetic field, we found a correlation between local heterogeneity and the ZBP formation, necessitating the systematic, data-driven analysis to disentangle genuine MZM signatures in TSC. This objective and reproducible workflow advances reliable MZM detection in TSCs, providing a foundation for MZM manipulation towards quantum computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15178 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution Park, Jewook Jeon, Hoyeon Shin, Dongwon Zhang, Guannan McGuire, Michael A Sales, Brian C. Li, An-Ping Superconductivity 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 machine-learning (ML) to analyze tunneling spectroscopy from FeTe0.55Se0.45, an intrinsic TSC. Based on the local density of states (LDOS) spectra acquired with a millikelvin scanning tunneling microscope under magnetic fields, each spectrum was decomposed into multiple Lorentzian peaks. The extracted peak parameters were assembled into a structured feature set and subsequently embedded and clustered with unsupervised ML algorithms. ML-based clustering identified distinct classes of LDOS spectra, separating superconductor vortices exhibiting ZBPs consistent with established characteristics of MZMs from vortices displaying ZBP-mimicking features of trivial origin. Furthermore, spatially resolved ZBP distributions differentiate isotropic vortex cores with well-defined ZBPs from vortices that exhibit locally distorted ZBPs. By comparing the ZBP distributions to defect locations measured without magnetic field, we found a correlation between local heterogeneity and the ZBP formation, necessitating the systematic, data-driven analysis to disentangle genuine MZM signatures in TSC. This objective and reproducible workflow advances reliable MZM detection in TSCs, providing a foundation for MZM manipulation towards quantum computation. |
| title | Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution |
| topic | Superconductivity |
| url | https://arxiv.org/abs/2602.15178 |