arXiv subjects

Peihan Sun

Publications and source records attributed to Peihan Sun.

8 recordsLinked to original sources

arXiv2026

Robust Betatron-Tune Measurement from Schottky Spectra: Complementary Classical and Deep-Learning Paradigms

Schottky spectra provide key beam diagnostics, with betatron sidebands encoding the fractional tune. Reliable tune measurement is particularly important for third-order resonance slow extraction in compact medical proton synchrotrons, where low signal-to-noise ratios and limited frequency resolution can compromise conventional peak-detection and curve-fitting methods. This work develops two complementary tune estimators with a shared spectral front-end but different temporal representations. The classical estimator coherently pools motion-compensated spectra, detects the sideband using a multi-width matched-filter bank, and performs sub-bin estimation through local argmax and an adaptive MAD-gated centroid. The deep-learning estimator converts each spectrum into a tune-likelihood map using a convolutional neural network with FFT-based global convolutions, then propagates the posterior with a discrete two-dimensional (q,v) Bayesian tracker under a Gaussian motion model while also reporting posterior uncertainty. On a synthetic dynamic-tune benchmark, the deep-learning estimator outperforms published baselines across the operating range, while the classical estimator exceeds the latency-compensated baseline and requires neither training data nor GPU acceleration. On near-stationary SAPT beam data, both methods operate end-to-end, with the deep-learning model requiring no retraining. Median per-frame latency remains below 1 ms on commodity hardware, supporting real-time-capable tune measurement in compact medical synchrotrons.

physics.acc-ph↗

arXiv2025

Real-Time-Capable Betatron Tune Measurement from Schottky Spectra Using Deep Learning and Uncertainty-Aware Kalman Filtering

Betatron tune measurement is essential for beam control in compact proton-therapy synchrotrons, yet conventional peak-detection techniques are not robust under the low signal-to-noise ratio (SNR) conditions typical of these machines. This work presents a lightweight convolutional neural network that performs real-time tune extraction from Schottky spectra with sub-millisecond inference latency and calibrated uncertainty estimates. The model uses attention-based pooling for reliable peak localization and a dual-branch architecture that jointly predicts the tune and its associated uncertainty. Trained with a Laplace negative log-likelihood loss, it produces uncertainty estimates whose magnitude tracks the instantaneous prediction error, which enables uncertainty-aware Kalman filtering for temporal smoothing. Experiments on a large synthetic dataset spanning SNR levels from 0 to $-20$\,dB demonstrate substantial performance gains over traditional peak-detection baselines, while the Kalman filter further suppresses transient outliers in time-series operation. Preliminary validation on operational beam data confirms stable tune tracking without retraining. With only about $2.0\times 10^{4}$ trainable parameters and real-time inference on commodity GPU hardware, the proposed diagnostic offers a practical solution for rapid and accurate betatron tune monitoring in compact medical synchrotrons and similar accelerators.

physics.acc-ph↗

arXiv2025

Coexistence of near-EF van Hove singularity and in-gap topological Dirac surface states in superconducting electrides

Superconducting electrides have attracted growing attention for their potential to achieve high superconducting transition temperatures (TC) under pressure. However, many known electrides are chemically reactive and unstable, making high-quality single-crystal growth, characterization, and measurements difficult, and most do not exhibit superconductivity at ambient pressure. In contrast, La3In stands out for its ambient-pressure superconductivity (TC ~ 9.4 K) and the availability of high-quality single crystals. Here, we investigate its low-energy electronic structure using angle-resolved photoemission spectroscopy and first-principles calculations. The bands near the Fermi energy are mainly derived from La 5d and In 5p orbitals. A saddle point is directly observed at the Brillouin zone (BZ) boundary, while a three-dimensional van Hove singularity crosses EF at the BZ corner. First-principles calculations further reveal topological Dirac surface states within the bulk energy gap above EF. The coexistence of a high density of states and in-gap topological surface states near EF suggests that La3In offers a promising platform for tuning superconductivity and exploring possible topological superconducting phases through doping or external pressure.

cond-mat.supr-con↗

arXiv2025

Superconductivity in cubic La3Al with interstitial anionic electrons

We report the observation of superconductivity in cubic La3Al single crystal. It shows a metallic behavior at a normal state without observable structural transition and enters the superconducting state below Tc ~ 6.32 K. Detailed characterizations and analysis indicate that cubic La3Al is a bulk type-II BCS superconductor. Moreover, theoretical calculations show that it can host interstitial anionic electrons, which are located at the body center of cubic unit cell, and confirm the electron-phonon coupling as the superconducting mechamism. Thus, cubic La3Al can be regarded as an novel electride superconductor.

cond-mat.supr-con↗

arXiv2024

High-Accuracy Schottky Diagnostics for Low-SNR Betatron Tune Measurement in Ramping Synchrotrons

This study introduces a novel real-time betatron tune measurement algorithm, utilizing Schottky signals and an FPGA-based backend architecture, specifically designed for rapidly ramping synchrotrons, with particular application to the Shanghai Advanced Proton Therapy (SAPT) facility. The developed algorithm demonstrates improved measurement accuracy under challenging operational conditions, especially in scenarios with limited sampling time and signal-to-noise ratios (SNR) as low as $-20$ dB. By applying Short-Time Fourier Transform (STFT) analysis, the algorithm effectively accommodates the rapid increase in revolution frequency from 4 MHz to 7.5 MHz over 0.35 seconds, along with tune shifts. A macro-particle simulation methodology is employed to generate Schottky signals, which are then combined with real noise collected from an analog-to-digital converter (ADC) to simulate practical conditions. The proposed betatron tune measurement algorithm integrates advanced spectral processing techniques and an enhanced peak detection algorithm specifically tailored for low SNR conditions. Experimental validation confirms the superior performance of the proposed algorithm over conventional approaches in terms of measurement accuracy, stability, and system robustness, while meeting the stringent operational requirements of proton therapy applications. This innovative approach effectively addresses critical limitations associated with Schottky diagnostics for betatron tune measurement in rapidly ramping synchrotrons operating under low SNR conditions, laying a robust foundation and providing a viable solution for advanced applications in proton therapy and related accelerator physics fields.

physics.acc-ph↗

arXiv2024

Deep Learning Models for Colloidal Nanocrystal Synthesis

Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between synthetic parameters of chemical reaction and physical properties of nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis model that correlates synthetic parameters with the final size and shape of target nanocrystals, using a dataset of 3500 recipes covering 348 distinct nanocrystal compositions. The size and shape labels were obtained from transmission electron microscope images using a segmentation model trained with a semi-supervised algorithm on a dataset comprising 1.2 million nanocrystals. By applying the reaction intermediate-based data augmentation method and elaborated descriptors, the synthesis model was able to predict nanocrystal's size with a mean absolute error of 1.39 nm, while reaching an 89% average accuracy for shape classification. The synthesis model shows knowledge transfer capabilities across different nanocrystals with inputs of new recipes. With that, the influence of chemicals on the final size of nanocrystals was further evaluated, revealing the importance order of nanocrystal composition, precursor or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis model offers a powerful tool to expedite the development of high-quality nanocrystals.

cond-mat.mtrl-sci↗

arXiv2024

Interstitial anionic electrons involved superconductivity and T-linear resistivity behavior in electride La3In

Electrides are unique materials because of the existence of interstitial anionic electrons (IAEs). Due to these loosely bound IAEs and their strong interaction with the framework of cations, electrides can host superconductivity with rather high Tc, especially under high pressure, as predicted in theory. However, the experimental observations of superconductivity in electrides are very rare, let alone the detailed studies on intrinsic properties of single crystals. Here, we report the superconducting and normal-state properties of electride La3In single crystals. La3In shows a type-II superconductivity with Tc ~ 9.4 K and a T-linear resistivity in a wide temperature range. Experimental measurements and theoretical calculations suggest that the relatively high Tc could be ascribed to the high density of states around the Fermi level caused by short flat bands along R-M direction and the strong electron-phonon coupling, partially derived from the IAEs. Meanwhile, the T-linear resistivity may reflect the significant electronic correlation effect in this material. These findings will shed light on understanding the role of IAEs in superconductivity and open a promising way to explore high-temperature superconductors in electrides.

cond-mat.supr-con↗

arXiv2023

Emergent Electronic Kagome Lattice in Correlated Charge-Density-Wave State of 1T-TaS$_2$

Quantum materials with tunable correlated and/or topological electronic states, such as the electronic Kagome lattice, provide an ideal platform to study the exotic quantum properties. However, the real-space investigations on the correlated electronic Kagome lattice have been rarely reported. Herein, we report on the electronic Kagome lattice emerging in the correlated charge-density-wave (CDW) state of 1T-TaS$_2$ at ~200 K via variable-temperature scanning tunneling microscopy (VT-STM). This emergent Kagome lattice can be considered a fractional electron-filling superstructure with reduced translational and rotational symmetries, confirmed by STM measurements and density functional theory simulations. The characteristic band structure and density of states of this electronic Kagome lattice are further explored based on theoretical calculations. Our results demonstrate a self-organized electronic Kagome lattice from the correlated CDW state via the effective tuning parameter of temperature and provide a platform to directly explore the interplay of correlated electrons and topological physics.

cond-mat.mtrl-sci↗