Showing 1–50 of 85 results for author: Patel, S

Destabilization of temperature-gradient-driven plasma turbulence by equilibrium $\vec{E}\times \vec{B}$ flow shear

Authors: Haomin Sun, Plamen G. Ivanov, Justin Ball, Stephan Brunner, Bhavin S. Patel

Abstract: A novel physical mechanism whereby sheared equilibrium flow enables temperature-gradient-driven turbulence is identified. Gyrokinetic simulations of ion-scale plasma turbulence show that imposed equilibrium $\vec{E}\times \vec{B}$ flow shear can destroy the self-generated zonal flows that regulate the turbulence. This results in transport that increases sharply with flow shear. A reduced fluid mod… ▽ More A novel physical mechanism whereby sheared equilibrium flow enables temperature-gradient-driven turbulence is identified. Gyrokinetic simulations of ion-scale plasma turbulence show that imposed equilibrium $\vec{E}\times \vec{B}$ flow shear can destroy the self-generated zonal flows that regulate the turbulence. This results in transport that increases sharply with flow shear. A reduced fluid model demonstrates that this is due to the spatial incompatibility of imposed and zonal shear layers. Simulations of spherical tokamak discharges place the inferred rotation shear at, or just below, the threshold of the sharp transport increase, implying that the toroidal rotation can be determined primarily by heat, rather than momentum, injection. △ Less

Submitted 24 July, 2026; v1 submitted 13 July, 2026; originally announced July 2026.

A Wafer-Scale Heterogeneous III-V-on-Silicon Nitride Quantum Photonic Platform

Authors: Lillian Thiel, Boqiang Shen, Jasper R. Venneberg, Melissa A. Guidry, Nic Arnaud, Adam Slater, Lucas Wang, Xuefeng Li, Josh Castro, Yiming Pang, Max Meunier, Sahil D. Patel, Yang Shen, Theodore Morin, Igor Kudelin, Bowen Song, Kaustubh Asawa, John E. Bowers, Kerry Vahala, Nergis Mavalvala, Xinghui Yin, Steven Bowers, Minh A. Tran, Tin Komljenovic, Galan Moody

Abstract: Heterogeneous integration of gain and strongly nonlinear materials with ultra-low-loss silicon nitride (SiN) photonics offers a route to scalable quantum circuits, but concurrent wafer-scale manufacturability, low interlayer loss, and high performance have been challenging to realize. Here we demonstrate a wafer-scale III-V-on-SiN quantum photonic platform that directly integrates III-V layers to… ▽ More Heterogeneous integration of gain and strongly nonlinear materials with ultra-low-loss silicon nitride (SiN) photonics offers a route to scalable quantum circuits, but concurrent wafer-scale manufacturability, low interlayer loss, and high performance have been challenging to realize. Here we demonstrate a wafer-scale III-V-on-SiN quantum photonic platform that directly integrates III-V layers to foundry-fabricated SiN circuits. The SiN layer provides 200-300 nm thick waveguides with $<1$ dB/m loss and a mature passive photonics ecosystem, while III-V materials provide large $χ^{\left(2\right)}$ and $χ^{\left(3\right)}$ nonlinearities for parametric gain, frequency conversion and quantum light generation. Adiabatic interlayer couplers yield $<25$ mdB loss to InGaP waveguides and resonators with intrinsic quality factors exceeding $10^6$, enabling $15\times$ brighter entanglement sources and efficient nonlinear conversion on SiN. Integrated components--including low-loss beam splitters, waveguide crossers, and tunable interferometers--are complemented by III-V lasers and InP photodetectors with amplifiers achieving up to $99^{+1}_{-12}\%$ quantum efficiency and $3$ GHz bandwidth. This architecture unites ultra-efficient sources, nonlinear elements and detectors on a wafer-scale, low-loss platform, establishing a path toward large-scale, low-noise quantum photonic systems. △ Less

Submitted 17 May, 2026; originally announced May 2026.

Enhanced Mid-Infrared Single-Photon Detection with Antenna-Coupled Superconducting Nanowires

Authors: Dip Joti Paul, Stewart Koppell, Gregor G. Taylor, Boris Korzh, Sahil R. Patel, Andrew D. Beyer, Emma E. Wollman, Matthew D. Shaw, Phillip D. Keathley, Karl K. Berggren

Abstract: Scaling the photon-detection area of superconducting nanowire single-photon detectors (SNSPDs) has traditionally been achieved by nanowire meandering. However, material inhomogeneities and fabrication-induced defects, such as line-edge roughness, increase with nanowire length, leading to reduced internal photon-detection efficiency and elevated dark-count rates. This trade-off becomes increasingly… ▽ More Scaling the photon-detection area of superconducting nanowire single-photon detectors (SNSPDs) has traditionally been achieved by nanowire meandering. However, material inhomogeneities and fabrication-induced defects, such as line-edge roughness, increase with nanowire length, leading to reduced internal photon-detection efficiency and elevated dark-count rates. This trade-off becomes increasingly pronounced as nanowires are scaled to sub-100 nm widths and sub-5 nm thicknesses required for mid- to far-infrared sensitivity. Here, we demonstrate an antenna-coupled SNSPD architecture that enhances the effective photon-detection area without increasing nanowire length. A crossed bowtie antenna integrated with an 80 nm-wide, 3 nm-thick WSi nanowire yields 15.7$\times$ increase in effective detection area at 7.4 $μ$m compared to a bare nanowire of identical geometric footprint, while maintaining the same internal detection efficiency and dark-count rate. Antenna coupling provides a scalable approach to increasing photon-detection area while reducing the noise-equivalent power, offering performance benefits for applications in astronomy, biological imaging, and molecular spectroscopy. △ Less

Submitted 12 August, 2026; v1 submitted 20 April, 2026; originally announced April 2026.

Comments: 39 pages, 10 figures

Journal ref: Nano Lett. (2026) 26 (31): 10227-10234

Charge Exchange Dynamics in Cold Collisions of $^{40}$CaH$^+$ and $^{39}$K

Authors: Swapnil Patel, Dibyendu Sardar, Jyothi Saraladevi, Michał Tomza, Kenneth R. Brown

Abstract: We report the observation of charge-exchange collisions between trapped calcium monohydride molecular ions ($^{40}$CaH$^+$) and ultracold potassium atoms ($^{39}$K) in a hybrid ion-atom trap. The measured charge-exchange rate coefficient is significantly suppressed relative to the Langevin rate constant for the system. We use $\mathit{ab\ initio}$ quantum-chemical calculations to model the (CaH-K)… ▽ More We report the observation of charge-exchange collisions between trapped calcium monohydride molecular ions ($^{40}$CaH$^+$) and ultracold potassium atoms ($^{39}$K) in a hybrid ion-atom trap. The measured charge-exchange rate coefficient is significantly suppressed relative to the Langevin rate constant for the system. We use $\mathit{ab\ initio}$ quantum-chemical calculations to model the (CaH-K)$^+$ complex in the ground and excited electronic states and to identify possible charge-exchange mechanisms. Our calculations rule out a direct non-radiative charge-exchange reaction and instead point to a radiative mechanism, but do not quantitatively reproduce the measured rate, highlighting the need for a full-dimensional quantum dynamics treatment that includes vibrational motion and intermediate complex formation. Our work demonstrates that cold hybrid ion-atom platforms with molecular ions enable access to richer chemical complexity and collisional dynamics inaccessible in purely atomic systems. △ Less

Submitted 4 June, 2026; v1 submitted 9 February, 2026; originally announced February 2026.

Journal ref: J. Phys. Chem. Lett. 17, 6574 (2026)

Causal machine learning reveals age-dependent radiation dose effects on mandibular osteoradionecrosis

Authors: Jingyuan Chen, Yunze Yang, Olivia M. Muller, Lei Zeng, Zhengliang Liu, Tianming Liu, Robert L Foote, Daniel J Ma, Samir H Patel, Zhong Liu, Wei Liu

Abstract: Distinguishing causal relationships from statistical correlations remains a fundamental challenge in clinical research, limiting the translation of observational findings into interventional treatment guidelines. Here we apply causal machine learning to establish causal effects of radiation dose parameters on mandibular osteoradionecrosis (ORN) in 931 head and neck cancer patients treated with vol… ▽ More Distinguishing causal relationships from statistical correlations remains a fundamental challenge in clinical research, limiting the translation of observational findings into interventional treatment guidelines. Here we apply causal machine learning to establish causal effects of radiation dose parameters on mandibular osteoradionecrosis (ORN) in 931 head and neck cancer patients treated with volumetric-modulated arc therapy. Using generalized random forests, we demonstrate that all examined dosimetric factors exhibit significant positive causal effects on ORN development (average treatment effects: 0.092-0.141). Integration with explainable machine learning reveals substantial treatment effect heterogeneity, with patients aged 50-60 years showing the strongest causal dose-response relationships (conditional average treatment effects up to 0.229), while patients over 70 years demonstrate minimal effects. These results suggest that age-stratified treatment optimization and personalized treatment planning for the dosimetric factors could reduce ORN risk. Our findings demonstrate that causal inference methods can transform clinical retrospective radiotherapy data into personalized treatment recommendations, providing a methodological framework applicable to toxicity prediction across oncology and other clinical domains where treatment decisions depend on complex dose-response relationships. △ Less

Submitted 14 February, 2026; v1 submitted 5 February, 2026; originally announced February 2026.

Reaching the intrinsic performance limits of superconducting nanowire single-photon detectors up to 0.1 mm wide

Authors: Kristen M. Parzuchowski, Eli Mueller, Bakhrom G. Oripov, Benedikt Hampel, Ravin A. Chowdhury, Sahil R. Patel, Daniel Kuznesof, Emma K. Batson, Ryan Morgenstern, Robert H. Hadfield, Varun B. Verma, Matthew D. Shaw, Jason P. Allmaras, Martin J. Stevens, Alex Gurevich, Adam N. McCaughan

Abstract: Superconducting nanowire single-photon detectors (SNSPDs) combine high detection efficiency, low noise, and excellent timing resolution, making them a leading platform for photon-counting applications. However, despite decades of materials and fabrication research, detector performance has never been shown to match theoretical performance expectations. Here, we demonstrate for the first time in si… ▽ More Superconducting nanowire single-photon detectors (SNSPDs) combine high detection efficiency, low noise, and excellent timing resolution, making them a leading platform for photon-counting applications. However, despite decades of materials and fabrication research, detector performance has never been shown to match theoretical performance expectations. Here, we demonstrate for the first time in situ tuning of a detector from its typical, suboptimal operation, to a regime limited only by material quality, allowing the device to reach its intrinsic performance limit. Our approach is based on current-biased superconducting "rails" placed on either side of the detector that redistribute current across its width to achieve its peak performance. This technique reduces the dark count rate by ten orders of magnitude. Further, we show operation at this intrinsic performance limit for devices up to 0.1 mm wide, and also demonstrate near-unity internal detection efficiency (IDE) at a wavelength of 4um for a 20um-wide detector--a factor of 20 wider than the current state of the art. This work enables future detectors to overcome the Pearl limit for device width, paving the way for arbitrarily large detectors. △ Less

Submitted 23 July, 2026; v1 submitted 22 January, 2026; originally announced January 2026.

High-Performance Near-Infrared Quantum Emission from Color Centers in hBN

Authors: Sean Doan, Sahil D. Patel, Yilin Chen, Jordan A. Gusdorff. Mark E. Turiansky, Luis Villagomez, Luka Jevremovic, Nicholas Lewis, Kenji Watanabe, Takashi Taniguchi, Lee C. Bassett, Chris Van de Walle, Galan Moody

Abstract: Color centers hosted in hexagonal boron nitride have emerged as a highly promising platform for single-photon emission and spin-photon technologies relevant to quantum communication and quantum networking. As a wide-bandgap van der Waals material, hBN can host optically active quantum defects across a broad spectral range. Here, we demonstrate a simple and scalable oxygen-plasma process that repro… ▽ More Color centers hosted in hexagonal boron nitride have emerged as a highly promising platform for single-photon emission and spin-photon technologies relevant to quantum communication and quantum networking. As a wide-bandgap van der Waals material, hBN can host optically active quantum defects across a broad spectral range. Here, we demonstrate a simple and scalable oxygen-plasma process that reproducibly creates single quantum emitters in hBN with blinking-free zero-phonon lines spanning the near-infrared from 700 up to 971 nm. These emitters combine MHz-level brightness, single-photon purity up to 99.9\%, and ultranarrow cryogenic linewidths down to 2.7~GHz under quasi-resonant excitation, placing them in a particularly attractive regime for quantum photonics. Photostability measurements further reveal resistance to photobleaching, sub-nm spectral stability over long timescales, and near-shot-noise-limited intensity fluctuations. Analysis of the phonon sidebands shows weak vibronic coupling and ZPL-dominated emission, with Debye--Waller factors approaching 50\%. Control experiments together with EDS elemental mapping support oxygen incorporation as a necessary ingredient in activating the NIR emitter population, while first-principles calculations identify O$_N$V$_N$ and O$_N$V$_N$H as the leading defect candidates. These results establish a high-performance NIR quantum-emitter platform in hBN for free-space quantum networking and future integrated quantum-photonic architectures. △ Less

Submitted 23 April, 2026; v1 submitted 18 December, 2025; originally announced December 2025.

Towards Breath Based Diagnostics via Water-mediated Capture of Synthetic Breath Biomarkers in SERS-active Plasmonic Nanogaps

Authors: Aditya Garg, Marissa Morales, Aashini Shah, Daniel Kim, Ming Lei, Sahil Patel, Jia Dong, Seleem Badawy, Sangeeta Bhatia, Loza F. Tadesse

Abstract: Volatile organic compounds (VOCs) are valuable health indicators, with synthetic breath biomarkers offering rapid and disease specific diagnostics. However, their <100 ppb level exhalation requires mass spectrometry, limiting clinical integration. Surface-enhanced Raman spectroscopy (SERS) offers a portable, cost-effective alternative. Yet, detecting synthetic breath biomarkers, with inherently lo… ▽ More Volatile organic compounds (VOCs) are valuable health indicators, with synthetic breath biomarkers offering rapid and disease specific diagnostics. However, their <100 ppb level exhalation requires mass spectrometry, limiting clinical integration. Surface-enhanced Raman spectroscopy (SERS) offers a portable, cost-effective alternative. Yet, detecting synthetic breath biomarkers, with inherently low Raman cross-sections, at <100 ppb remains challenging. We demonstrate SERS detection down to clinically relevant 10 ppb via water-mediated trapping in hydroxylated nanoporous silica-coated plasmonic nanogaps, using pentafluoropropylamine (PFP) as a representative synthetic breath biomarker. Uniform nanogaps, with >1000 times electric field enhancement, were generated between a gold film and gold-silica core-shell nanoparticle assemblies using electric field-driven evaporation. Oxygen plasma treatment hydroxylated the silica, enabling water-mediated hydrogen bonding that strengthened PFP adsorption, confirmed by density functional theory. This mechanism improved SERS sensitivity by 10000 fold, enabling ppb level PFP detection in mouse bronchial fluid and establishing a VOC capturing SERS platform for breath-based diagnostics. △ Less

Submitted 12 December, 2025; originally announced December 2025.

Tungsten Germanide Superconducting Nanowire Single-Photon Detectors with Saturated Internal Detection Efficiency at Wavelengths up to 29 μm

Authors: Benedikt Hampel, Daniel Kuznesof, Andrew S. Mueller, Sahil R. Patel, Robert H. Hadfield, Emma E. Wollman, Matthew D. Shaw, Dirk Schwarzer, Alec M. Wodtke, Khalid Hossain, Allison V. Mis, Alexana Roshko, Richard P. Mirin, Sae Woo Nam, Martin J. Stevens, Varun B. Verma

Abstract: Superconducting nanowire single-photon detectors (SNSPDs) are among the most sensitive single-photon detectors available and have the potential to transform fields ranging from infrared astrophysics to molecular spectroscopy. However, extending their performance into the mid-infrared spectral region - crucial for applications such as exoplanet transit spectroscopy and vibrational fingerprinting of… ▽ More Superconducting nanowire single-photon detectors (SNSPDs) are among the most sensitive single-photon detectors available and have the potential to transform fields ranging from infrared astrophysics to molecular spectroscopy. However, extending their performance into the mid-infrared spectral region - crucial for applications such as exoplanet transit spectroscopy and vibrational fingerprinting of molecules - has remained a major challenge, primarily due to material limitations and scalability constraints. Here, we report on the development of SNSPDs based on tungsten germanide, a novel material system that combines high mid-infrared sensitivity with compatibility for large-scale fabrication. Our detectors exhibit saturated internal detection efficiency at wavelengths up to 29 μm, while using 2.7x thicker films (8 nm vs 3 nm) and up to 4.5x wider nanowires (360 nm vs 80 nm) compared to mid-infrared-optimized SNSPDs fabricated from tungsten silicide. This advance will enable scalable, high-performance single-photon detection in a spectral region that was previously inaccessible, opening new frontiers in remote sensing, thermal imaging, environmental monitoring, molecular physics, and astronomy. △ Less

Submitted 16 June, 2026; v1 submitted 25 November, 2025; originally announced November 2025.

Comments: 8 pages, 6 figures, Published in APL Photonics 11(6): 066112 (2026) (doi: 10.1063/5.0331641)

Journal ref: APL Photonics 11 (6): 066112 (2026)

Intertwined Hyperferroelectricity, Tunable Multiple Topological Phases and Giant Rashba Effect in Wurtzite LiZnAs

Authors: Saurav Patel, Paras Patel, Shaohui Qiu, Prafulla K. Jha

Abstract: Composite quantum compounds offer a fertile ground for uncovering the complex interrelations between seemingly distinct phenomena in condensed matter physics for advanced nonvolatile and spintronics applications. Beyond topological superconductors and axion insulators, the idea of intertwined Hyperferroelectricity (HyFE), multiple topological phases and Rashba spin-splitting with reversible spin t… ▽ More Composite quantum compounds offer a fertile ground for uncovering the complex interrelations between seemingly distinct phenomena in condensed matter physics for advanced nonvolatile and spintronics applications. Beyond topological superconductors and axion insulators, the idea of intertwined Hyperferroelectricity (HyFE), multiple topological phases and Rashba spin-splitting with reversible spin textures represents the local, global and symmetry-driven characteristics of quantum materials, respectively, offering unique pathways for enhanced functionalities. We unveiled a unified framework to achieve this synergy through the presence of crystalline symmetries and spin-orbit coupling in LiZnAs compound using first-principles calculations. HyFE exhibits ability to maintain spontaneous polarization under open-circuit boundary conditions, even with existence of depolarization field while Rashba effect exhibits paradigmatic spin texture in momentum space with tangential vector field. The presence of unstable $A_{2u}(LO)$ mode leads to free energy minimum with significant well depth and polarization of -66 meV and $P_{HyFE} = 0.282~C/m^2$, respectively indicating stable HyFE. The robust HyFE stem from mode-specific effective charges and larger high-frequency dielectric constants. This study also addresses the subtle question of whether critical point of topological phase transition shifts in response to drastically different Rashba spin-splitting values obtained from VASP and WIEN2k. Moreover, biaxial strain (BAS) induced Weyl semimetal (at 3.4% BAS) and topological insulating phase (after 3.4% BAS) is observed with giant Rashba coefficient of 5.91 eV Å and 2.42 eV Å, respectively. Furthermore, switching of bulk polarization leads to spin texture reversal, providing a robust mechanism to leverage spin degrees of freedom in these Hyperferroelectric Rashba topological materials. △ Less

Submitted 3 November, 2025; originally announced November 2025.

AI in Proton Therapy Treatment Planning: A Review

Authors: Yuzhen Ding, Hongying Feng, Martin Bues, Mirek Fatyga, Tianming Liu, Thomas J. Whitaker, Haibo Lin, Nancy Y. Lee, Charles B. Simone II, Samir H. Patel, Daniel J. Ma, Steven J. Frank, Sujay A. Vora, Jonathan A. Ashman, Wei Liu

Abstract: Purpose: Proton therapy provides superior dose conformity compared to photon therapy, but its treatment planning is challenged by sensitivity to anatomical changes, setup/range uncertainties, and computational complexity. This review evaluates the role of artificial intelligence (AI) in improving proton therapy treatment planning. Materials and methods: Recent studies on AI applications in image r… ▽ More Purpose: Proton therapy provides superior dose conformity compared to photon therapy, but its treatment planning is challenged by sensitivity to anatomical changes, setup/range uncertainties, and computational complexity. This review evaluates the role of artificial intelligence (AI) in improving proton therapy treatment planning. Materials and methods: Recent studies on AI applications in image reconstruction, image registration, dose calculation, plan optimization, and quality assessment were reviewed and summarized by application domain and validation strategy. Results: AI has shown promise in automating contouring, enhancing imaging for dose calculation, predicting dose distributions, and accelerating robust optimization. These methods reduce manual workload, improve efficiency, and support more personalized planning and adaptive planning. Limitations include data scarcity, model generalizability, and clinical integration. Conclusion: AI is emerging as a key enabler of efficient, consistent, and patient-specific proton therapy treatment planning. Addressing challenges in validation and implementation will be essential for its translation into routine clinical practice. △ Less

Submitted 21 October, 2025; originally announced October 2025.

Towards High-Efficiency Particle Detection Using Superconducting Microwire Arrays

Authors: Christina Wang, Cristián Peña, Adolf Bornheim, Shuoxing Wu, Alexander Albert, Thomas Sievert, Artur Apresyan, Emanuel Knehr, Boris Korzh, Jamie Luskin, Ludovico Mori, Sahil Patel, Guillermo Reales Gutiérrez, Manish Sahu, Ekkehart Schmidt, Matthew Shaw, Elise Sledge, Maria Spiropulu, Towsif Taher, Si Xie

Abstract: We present a detailed study of an 8-channel $1\times1$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV hadron beam and 120 GeV muon beam at the CERN Super Proton Synchrotron H6 beamline. Following up on our first detailed characterization of the efficiency and response of an SMSPD fabricated on a 3 nm WSi film, we report measurements of enhanced parti… ▽ More We present a detailed study of an 8-channel $1\times1$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV hadron beam and 120 GeV muon beam at the CERN Super Proton Synchrotron H6 beamline. Following up on our first detailed characterization of the efficiency and response of an SMSPD fabricated on a 3 nm WSi film, we report measurements of enhanced particle detection efficiency using a sensor fabricated from a thicker 4.7nm-thick WSi film. We also report the first SMSPD detection efficiency measurement made for muons. Measurements are enabled by a silicon tracking telescope providing 10 $μ$m in-situ spatial resolution. The results show a fill factor-normalized detection efficiency of 75% and a time resolution of about 130 ps across pixels. These findings represent a significant advancement toward developing high-efficiency SMSPD charged particle tracking systems with simultaneous precision timing, with potential applications in future accelerator-based experiments such as the FCC-ee and Muon Collider. △ Less

Submitted 14 January, 2026; v1 submitted 8 October, 2025; originally announced October 2025.

Comments: arXiv admin note: substantial text overlap with arXiv:2410.00251

Report number: FERMILAB-PUB-25-0682-ETD

Journal ref: Journal of Instrumentation, Volume 20, P12033, 2025

Causal Machine Learning Analysis of Empirical Relative Biological Effectiveness (RBE) for Mandible Osteoradionecrosis in Head and Neck Cancer Radiotherapy

Authors: Jingyuan Chen, Zhong Liu, Yunze Yang, Olivia M. Muller, Zhengliang Liu, Tianming Liu, Lei Zeng, Robert L. Foote, Daniel J. Ma, Samir H. Patel, Wei Liu

Abstract: Mandible Osteoradionecrosis (ORN) is one of the most severe adverse events (AEs) for head and neck (H&N) cancer radiotherapy. Previous retrospective investigations on real-world data relied on conventional statistical models that primarily elucidate correlation rather than establishing causal relationships. Through the novel causal machine learning, we aim to obtain empirical relative biological e… ▽ More Mandible Osteoradionecrosis (ORN) is one of the most severe adverse events (AEs) for head and neck (H&N) cancer radiotherapy. Previous retrospective investigations on real-world data relied on conventional statistical models that primarily elucidate correlation rather than establishing causal relationships. Through the novel causal machine learning, we aim to obtain empirical relative biological effectiveness (RBE) for ORN in H&N cancer patients treated with pencil-beam-scanning proton therapy (PBSPT). 335 patients treated by PBSPT and 931 patients treated by volumetric-modulated arc therapy (VMAT) were included. We use 1:1 case-matching to minimize the imbalance in clinical factors between PBSPT and VMAT. The bias test of standardized mean differences (SMD) was applied on the case-matched patient cohorts. The causal machine learning method, causal forest (CF), was adopted to investigate the causal effects between dosimetric factors and the incidence of ORN. The dose volume constraints (DVCs) for VMAT and PBSPT were derived based on causal effects. RBE values were further empirically derived based on tolerance curves formed from DVCs. 335 VMAT patients were case-matched to 335 PBSPT patients; however, SMD analysis revealed persistent covariate imbalances within each group, indicating residual confounding influence. Using CF modeling, we identified DVCs of mandible ORN and found that PBSPT had lower critical volumes than those of VMAT, leading to empirical RBE exceeding 1.1 in the moderate dose range (1.61 at 40 Gy[RBE=1.1], 1.30 at 50 Gy, and 1.13 at 60 Gy). This study presents a novel application of causal machine learning to evaluate mandible ORN in radiotherapy. The results indicate that proton RBE may significantly exceed 1.1 in the moderate dose range, underscoring the importance of incorporating the variable RBE into PBSPT treatment planning to mitigate the risk of ORN. △ Less

Submitted 25 September, 2025; originally announced September 2025.

A flux bounce-back scheme for the filtered Spectral Element Lattice Boltzmann Method

Authors: Chunheng Zhao, Saumil Patel, Hai Lu Lin, Misun Min, Taehun Lee

Abstract: We develop a spectral element lattice Boltzmann method (SELBM) with the flux bounce-back (FBB) scheme, to enable accurate simulations of single-phase fluid dynamics in unstructured mesh. We adopt an Eulerian description of the streaming process in place of the perfect shift in the regular LBM. The spectral element method is used to spatially discretize the convective term, while the strong stabili… ▽ More We develop a spectral element lattice Boltzmann method (SELBM) with the flux bounce-back (FBB) scheme, to enable accurate simulations of single-phase fluid dynamics in unstructured mesh. We adopt an Eulerian description of the streaming process in place of the perfect shift in the regular LBM. The spectral element method is used to spatially discretize the convective term, while the strong stability-preserving Runge-Kutta (SSPRK) method is used for time integration. To increase stability, we investigate the use of an explicit filter, particularly in the context of the sensitive double shear layer problem. The results indicate that by using the high-order polynomial, we can effectively eliminate the small vortices around the neck region. We introduce the flux bounce-back scheme to enable the current scheme to handle complex boundaries. The proposed scheme and flux boundary method are validated through benchmark simulations, including the unsteady Couette flow and the planar Poiseuille flow. Further validation is provided through the Taylor-Green vortex problem, demonstrating the accuracy and convergence of the scheme for isotropic turbulence. Finally, we consider a fully developed turbulent flow within a cylindrical pipe and correctly predict the turbulent boundary layer profile. △ Less

Submitted 7 December, 2025; v1 submitted 23 September, 2025; originally announced September 2025.

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations

Authors: JingChun Wang, Meenu Upadhyay, Eric D. Boittier, Kham Lek Chaton, Valerii Andreichev, Mike Devereux, Shimoni Patel, Sena Aydin, Kai Töpfer, Markus Meuwly

Abstract: Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail… ▽ More Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel- and neural network-based approaches for intramolecular degrees of freedom are combined with distributed charge models for long range electrostatics to describe the interaction energies of condensed phase systems. The main focus is on illustrative examples ranging from pure liquids (dichloromethane, water) to chemically and structurally heterogeneous systems (eutectic liquids, CO on amorphous solid water), reactions (Menshutkin), and spectroscopy (triatomic probes for protein dynamics). For all examples, small to medium-sized clusters are used to represent and improve the total interaction energy compared with reference quantum chemical calculations. Although remarkable accuracy can be achieved for some systems (chemical accuracy for dichloromethane and water), it is clear that more realistic models are required for van der Waals contributions and improved water models need to be used for more quantitative simulations of heterogeneous chemical and biological systems. △ Less

Submitted 15 September, 2025; originally announced September 2025.

A comprehensive study on the characterization of lyzed blood samples using dual-wavelength photoacoustics

Authors: Subhadip Paul, Hari Shankar Patel, Vatsala Misra, Ravi Rani, Amaresh K. Sahoo, Ratan K. Saha

Abstract: Anemia is a global health concern, prompting the need for rapid and accurate diagnostic tools, especially for vulnerable populations. Estimating the blood lysis level (LL) and oxygen saturation (SO2) are essential not only for anemia but also for other hemolytic conditions. This study explores the potential of photoacoustic (PA) spectroscopy as a quantitative tool for evaluating hemolysis in anemi… ▽ More Anemia is a global health concern, prompting the need for rapid and accurate diagnostic tools, especially for vulnerable populations. Estimating the blood lysis level (LL) and oxygen saturation (SO2) are essential not only for anemia but also for other hemolytic conditions. This study explores the potential of photoacoustic (PA) spectroscopy as a quantitative tool for evaluating hemolysis in anemia diagnosis. In vitro PA measurements on human blood samples were validated through computational modeling using the discrete dipole approximation, Monte Carlo, and k-Wave acoustic simulations. The quantitative values of blood hematocrit (H), LL and SO2 have been estimated using simulated and experimental PA signals. The wavelength pairs 700-905 nm and 700-1000 nm have been found to be optimal for the simultaneous estimation of these parameters and provided H and SO2 estimations with accuracy approximately > 90% up to LL=14% and for LL = 0-30%, respectively. The correlation coefficient between the actual and evaluated lysis levels has been computed to be \approx 0.90. Further investigation is needed to enhance the robustness and clinical applicability of the developed method under an in vivo setting when both the H and LL levels are not known. △ Less

Submitted 1 September, 2025; originally announced September 2025.

Quantum Seniority-based Subspace Expansion: Linear Combinations of Short-Circuit Unitary Transformations for the Electronic Structure Problem

Authors: Smik Patel, Praveen Jayakumar, Rick Huang, Tao Zeng, Artur F. Izmaylov

Abstract: Quantum SENiority-based Subspace Expansion (Q-SENSE) is a hybrid quantum-classical algorithm that interpolates between the Variational Quantum Eigensolver (VQE) and Configuration Interaction (CI) methods. It constructs Hamiltonian matrix elements on a quantum device and solves the resulting eigenvalue problem classically. Unlike other expansion-based methods -- such as Quantum Subspace Expansion (… ▽ More Quantum SENiority-based Subspace Expansion (Q-SENSE) is a hybrid quantum-classical algorithm that interpolates between the Variational Quantum Eigensolver (VQE) and Configuration Interaction (CI) methods. It constructs Hamiltonian matrix elements on a quantum device and solves the resulting eigenvalue problem classically. Unlike other expansion-based methods -- such as Quantum Subspace Expansion (QSE), Quantum Krylov Algorithms, and the Non-Orthogonal Quantum Eigensolver -- Q-SENSE introduces seniority operators as artificial symmetries to construct orthogonal basis states. This seniority-symmetry-based approach reduces one of the primary limitations of VQE on near-term quantum hardware -- circuit depth -- at the cost of measuring additional matrix elements. The artificial symmetries also reduce the number of Hamiltonian terms that must be measured, as only a small fraction of the terms couple basis states in different seniority subspaces. With all these merits, Q-SENSE offers a scalable and resource-efficient route to quantum advantage on near-term quantum devices and in the early fault-tolerant regime. △ Less

Submitted 7 December, 2025; v1 submitted 31 August, 2025; originally announced September 2025.

Favorable modifications of Scrape-Off Layer (SOL) heat flux width through pulsed fuelling in ADITYA-U Tokamak

Authors: SK Injamul Hoque, Harshita Raj, Ritu Dey, Soumitra Banerjee, Komal, Kaushlender Singh, Suman Dolui, Ankit Kumar, Ashok Kumawat, Bharat Hegde, Sharvil Patel, Kiran Patel, Rohit Kumar, Suman Aich, Pramila Gautam, Umesh Nagora, Asha N Adhiya, K. A. Jadeja, K. M. Patel, Ankit Patel, R. L. Tanna, Joydeep Ghosh

Abstract: Enhancement of the scrape-off layer (SOL) heat flux width has been observed in the ADITYA-U Tokamak following the injection of short fuel gas pulses. A notable reduction in parallel heat flux near the last closed flux surface (LCFS) is observed after each pulse. Comparative analysis indicates that pulsed fuelling is more effective in mitigating heat flux with improved core confinement than continu… ▽ More Enhancement of the scrape-off layer (SOL) heat flux width has been observed in the ADITYA-U Tokamak following the injection of short fuel gas pulses. A notable reduction in parallel heat flux near the last closed flux surface (LCFS) is observed after each pulse. Comparative analysis indicates that pulsed fuelling is more effective in mitigating heat flux with improved core confinement than continuous gas feeding via real-time density control. Analytical and simulation works are also carried out for validation of experimental results. The analytical model shows that SOL width modification cannot be attributed solely to the decrease of temperature due to gas pulse injection; cross-field plasma diffusion also needs to increase. Simulations with the UEDGE code suggest that an increase in both the cross-field diffusion coefficient and inward pinch velocity is necessary to replicate the experimentally observed broadening of the heat flux SOL width. These findings provide insights into efficient SOL heat flux control strategies for future fusion devices. △ Less

Submitted 1 August, 2025; originally announced August 2025.

Identification and Characterization of a New Disruption Regime in ADITYA-U Tokamak

Authors: Soumitra Banerjee, Harshita Raj, Sk Injamul Hoque, Komal Yadav, Sharvil Patel, Ankit Kumar, Kaushlender Singh, Ashok Kumawat, Bharat Hegde, Subhojit Bose, Priyanka Verma, Kumudini Tahiliani, Asha Adhiya, Manoj Kumar, Rohit Kumar, Malay Bikash Chowdhuri, Nilam Ramaiya, Ananya Kundu, Suman Aich, Suman Dolui, K. A. Jadeja, K. M. Patel, Ankit Patel, Rakesh L. Tanna, Joydeep Ghosh

Abstract: Disruptions continue to pose a significant challenge to the stable operation and future design of tokamak reactors. A comprehensive statistical investigation carried out on the ADITYA-U tokamak has led to the observation and characterization of a novel disruption regime. In contrast to the conventional Locked Mode Disruption (LMD), the newly identified disruption exhibits a distinctive two-phase e… ▽ More Disruptions continue to pose a significant challenge to the stable operation and future design of tokamak reactors. A comprehensive statistical investigation carried out on the ADITYA-U tokamak has led to the observation and characterization of a novel disruption regime. In contrast to the conventional Locked Mode Disruption (LMD), the newly identified disruption exhibits a distinctive two-phase evolution: an initial phase characterized by a steady rise in mode frequency with a nonlinearly saturated amplitude, followed by a sudden frequency collapse accompanied by a pronounced increase in amplitude. This behaviour signifies the onset of the precursor phase on a significantly shorter timescale. Clear empirical thresholds have been identified to distinguish this disruption type from conventional LMD events, including edge safety factor, current decay coefficient, current quench (CQ) time, and CQ rate. The newly identified disruption regime is predominantly governed by the (m/n = 2/1) drift-tearing mode (DTM), which, in contrast to typical disruptions in the ADITYA-U tokamak that involve both m/n = 2/1 and 3/1 modes, consistently manifests as the sole dominant instability. Initiated by core temperature hollowing, the growth of this mode is significantly enhanced by a synergistic interplay between a strongly localized pressure gradient and the pronounced steepening of the current density profile in the vicinity of the mode rational surface. △ Less

Submitted 23 July, 2025; originally announced July 2025.

Planted Solutions in Quantum Chemistry: Generating Non-Trivial Hamiltonians with Known Ground States

Authors: Linjun Wang, Joshua T. Cantin, Smik Patel, Ignacio Loaiza, Rick Huang, Artur F. Izmaylov

Abstract: Generating large, non-trivial quantum chemistry test problems with known ground-state solutions remains a core challenge for benchmarking electronic structure methods. Inspired by planted-solution techniques from combinatorial optimization, we introduce four classes of Hamiltonians with embedded, retrievable ground states. These Hamiltonians mimic realistic electronic structure problems, support a… ▽ More Generating large, non-trivial quantum chemistry test problems with known ground-state solutions remains a core challenge for benchmarking electronic structure methods. Inspired by planted-solution techniques from combinatorial optimization, we introduce four classes of Hamiltonians with embedded, retrievable ground states. These Hamiltonians mimic realistic electronic structure problems, support adjustable complexity, and are derived from reference systems. Crucially, their ground-state energies can be computed exactly, provided the construction parameters are known. To obscure this structure and control perceived complexity, we introduce techniques such as killer operators, balance operators, and random orbital rotations. We showcase this framework using examples based on homogeneous catalysts of industrial relevance and validate tunable difficulty through density matrix renormalization group (DMRG) convergence behavior. Beyond enabling scalable, ground-truth benchmark generation, our approach offers a controlled setting to explore the limitations of electronic structure methods and investigate how Hamiltonian structure influences ground state solution difficulty. △ Less

Submitted 22 September, 2025; v1 submitted 20 July, 2025; originally announced July 2025.

Comments: 15 pages, 6 figures

Temperature-Dependent Characterization of Large-Area Superconducting Microwire Array with Single-Photon Sensitivity in the Near-Infrared

Authors: Christina Wang, Cristián Peña, Si Xie, Emanuel Knehr, Boris Korzh, Jamie Luskin, Sahil Patel, Matthew Shaw, Valentina Vega

Abstract: Superconducting nanowire single photon detectors (SNSPDs) are a leading detector technology for time-resolved single-photon counting from the ultraviolet to the near-infrared regime. The recent advancement in single-photon sensitivity in micrometer-scale superconducting wires opens up promising opportunities to develop large area SNSPDs with applications in low background dark matter detection exp… ▽ More Superconducting nanowire single photon detectors (SNSPDs) are a leading detector technology for time-resolved single-photon counting from the ultraviolet to the near-infrared regime. The recent advancement in single-photon sensitivity in micrometer-scale superconducting wires opens up promising opportunities to develop large area SNSPDs with applications in low background dark matter detection experiments. We present the first detailed temperature-dependent study of a 4-channel $1\times1$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array, including the internal detection efficiency, dark count rate, and importantly the coincident dark counts across pixels. The detector shows saturated internal detection efficiency for photon wavelengths ranging from 635 nm to 1650 nm, time jitter of about 160 ps for 1060 nm photons, and a low dark count rate of about $10^{-2}$ Hz. Additionally, the coincidences of dark count rate across pixels are studied for the first time in detail, where we observed an excess of correlated dark counts, which has important implications for low background dark matter experiments. The results presented is the first step towards characterizing and developing SMSPD array systems and associated background for low background dark matter detection experiments. △ Less

Submitted 4 June, 2025; originally announced June 2025.

Report number: FERMILAB-PUB-25-0262-ETD

Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy

Authors: Yuzhen Ding, Jason Holmes, Hongying Feng, Martin Bues, Lisa A. McGee, Jean-Claude M. Rwigema, Nathan Y. Yu, Terence S. Sio, Sameer R. Keole, William W. Wong, Steven E. Schild, Jonathan B. Ashman, Sujay A. Vora, Daniel J. Ma, Samir H. Patel, Wei Liu

Abstract: Purpose: Intensity-modulated proton therapy (IMPT) offers precise tumor coverage while sparing organs at risk (OARs) in head and neck (H&N) cancer. However, its sensitivity to anatomical changes requires frequent adaptation through online adaptive radiation therapy (oART), which depends on fast, accurate dose calculation via Monte Carlo (MC) simulations. Reducing particle count accelerates MC but… ▽ More Purpose: Intensity-modulated proton therapy (IMPT) offers precise tumor coverage while sparing organs at risk (OARs) in head and neck (H&N) cancer. However, its sensitivity to anatomical changes requires frequent adaptation through online adaptive radiation therapy (oART), which depends on fast, accurate dose calculation via Monte Carlo (MC) simulations. Reducing particle count accelerates MC but degrades accuracy. To address this, denoising low-statistics MC dose maps is proposed to enable fast, high-quality dose generation. Methods: We developed a diffusion transformer-based denoising framework. IMPT plans and 3D CT images from 80 H&N patients were used to generate noisy and high-statistics dose maps using MCsquare (1 min and 10 min per plan, respectively). Data were standardized into uniform chunks with zero-padding, normalized, and transformed into quasi-Gaussian distributions. Testing was done on 10 H&N, 10 lung, 10 breast, and 10 prostate cancer cases, preprocessed identically. The model was trained with noisy dose maps and CT images as input and high-statistics dose maps as ground truth, using a combined loss of mean square error (MSE), residual loss, and regional MAE (focusing on top/bottom 10% dose voxels). Performance was assessed via MAE, 3D Gamma passing rate, and DVH indices. Results: The model achieved MAEs of 0.195 (H&N), 0.120 (lung), 0.172 (breast), and 0.376 Gy[RBE] (prostate). 3D Gamma passing rates exceeded 92% (3%/2mm) across all sites. DVH indices for clinical target volumes (CTVs) and OARs closely matched the ground truth. Conclusion: A diffusion transformer-based denoising framework was developed and, though trained only on H&N data, generalizes well across multiple disease sites. △ Less

Submitted 4 June, 2025; originally announced June 2025.

Critical review of patient outcome study in head and neck cancer radiotherapy

Authors: Jingyuan Chen, Yunze Yang, Chenbin Liu, Hongying Feng, Jason M. Holmes, Lian Zhang, Steven J. Frank, Charles B. Simone II, Daniel J. Ma, Samir H. Patel, Wei Liu

Abstract: Rapid technological advances in radiation therapy have significantly improved dose delivery and tumor control for head and neck cancers. However, treatment-related toxicities caused by high-dose exposure to critical structures remain a significant clinical challenge, underscoring the need for accurate prediction of clinical outcomes-encompassing both tumor control and adverse events (AEs). This re… ▽ More Rapid technological advances in radiation therapy have significantly improved dose delivery and tumor control for head and neck cancers. However, treatment-related toxicities caused by high-dose exposure to critical structures remain a significant clinical challenge, underscoring the need for accurate prediction of clinical outcomes-encompassing both tumor control and adverse events (AEs). This review critically evaluates the evolution of data-driven approaches in predicting patient outcomes in head and neck cancer patients treated with radiation therapy, from traditional dose-volume constraints to cutting-edge artificial intelligence (AI) and causal inference framework. The integration of linear energy transfer in patient outcomes study, which has uncovered critical mechanisms behind unexpected toxicity, was also introduced for proton therapy. Three transformative methodological advances are reviewed: radiomics, AI-based algorithms, and causal inference frameworks. While radiomics has enabled quantitative characterization of medical images, AI models have demonstrated superior capability than traditional models. However, the field faces significant challenges in translating statistical correlations from real-world data into interventional clinical insights. We highlight that how causal inference methods can bridge this gap by providing a rigorous framework for identifying treatment effects. Looking ahead, we envision that combining these complementary approaches, especially the interventional prediction models, will enable more personalized treatment strategies, ultimately improving both tumor control and quality of life for head and neck cancer patients treated with radiation therapy. △ Less

Submitted 19 March, 2025; originally announced March 2025.

Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions

Authors: Kai Töpfer, Jingchun Wang, Shimoni Patel, Markus Meuwly

Abstract: Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutectic mixtures consisting of water, acetamid… ▽ More Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutectic mixtures consisting of water, acetamide and KSCN. Two water models are considered: TIP3P which is consistent with the CGenFF energy function and TIP4P which - as a water model - is superior to TIP4P. Both fitted models, {\bf M2$^{\rm TIP3P}$} and {\bf M2$^{\rm TIP4P}$}, yield favourable thermodynamic, structural, spectroscopic and transport properties from extensive molecular dynamics simulations. In particular, the slow and fast decay times from 2-dimensional infrared spectroscopy and the viscosity for water-rich mixtures are described realistically and consistent with experiments. On the other hand, including the co-solvent (acetamide) in the present case is expected to further improve the computed viscosity for low-water content. It is concluded that such a cluster-based approach is a promising and generalizable route for routine parametrization of heterogeneous, electrostatically dominated systems. △ Less

Submitted 28 February, 2025; originally announced February 2025.

Comments: arXiv admin note: substantial text overlap with arXiv:2408.07638

Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology

Authors: Peilong Wang, Zhengliang Liu, Yiwei Li, Jason Holmes, Peng Shu, Lian Zhang, Xiang Li, Quanzheng Li, Brady S. Laughlin, Diego Santos Toesca, Sujay A. Vora, Samir H. Patel, Terence T. Sio, Tianming Liu, Wei Liu

Abstract: Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplored. Purpose: This study aims to investigate whether fine-tuning LLMs with dom… ▽ More Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplored. Purpose: This study aims to investigate whether fine-tuning LLMs with domain knowledge can improve the performance on Task (1) treatment regimen generation, Task (2) treatment modality selection (photon, proton, electron, or brachytherapy), and Task (3) ICD-10 code prediction in radiation oncology. Methods: Data for 15,724 patient cases were extracted. Cases where patients had a single diagnostic record, and a clearly identifiable primary treatment plan were selected for preprocessing and manual annotation to have 7,903 cases of the patient diagnosis, treatment plan, treatment modality, and ICD-10 code. Each case was used to construct a pair consisting of patient diagnostics details and an answer (treatment regimen, treatment modality, or ICD-10 code respectively) for the supervised fine-tuning of these three tasks. Open source LLaMA2-7B and Mistral-7B models were utilized for the fine-tuning with the Low-Rank Approximations method. Accuracy and ROUGE-1 score were reported for the fine-tuned models and original models. Clinical evaluation was performed on Task (1) by radiation oncologists, while precision, recall, and F-1 score were evaluated for Task (2) and (3). One-sided Wilcoxon signed-rank tests were used to statistically analyze the results. Results: Fine-tuned LLMs outperformed original LLMs across all tasks with p-value <= 0.001. Clinical evaluation demonstrated that over 60% of the fine-tuned LLMs-generated treatment regimens were clinically acceptable. Precision, recall, and F1-score showed improved performance of fine-tuned LLMs. △ Less

Submitted 28 January, 2025; originally announced January 2025.

Evaluating The Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology

Authors: Meiyun Cao, Shaw Hu, Jason Sharp, Edward Clouser, Jason Holmes, Linda L. Lam, Xiaoning Ding, Diego Santos Toesca, Wendy S. Lindholm, Samir H. Patel, Sujay A. Vora, Peilong Wang, Wei Liu

Abstract: Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Materials and Methods: A total of 607 CT simulation orders for patients were collected from the Aria database at our institution. A locally hosted Llama 3.1 405B model, accessed via the Application Programming Interface (API) service, wa… ▽ More Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Materials and Methods: A total of 607 CT simulation orders for patients were collected from the Aria database at our institution. A locally hosted Llama 3.1 405B model, accessed via the Application Programming Interface (API) service, was used to extract keywords from the CT simulation orders and generate summaries. The downloaded CT simulation orders were categorized into seven groups based on treatment modalities and disease sites. For each group, a customized instruction prompt was developed collaboratively with therapists to guide the Llama 3.1 405B model in generating summaries. The ground truth for the corresponding summaries was manually derived by carefully reviewing each CT simulation order and subsequently verified by therapists. The accuracy of the LLM-generated summaries was evaluated by therapists using the verified ground truth as a reference. Results: About 98% of the LLM-generated summaries aligned with the manually generated ground truth in terms of accuracy. Our evaluations showed an improved consistency in format and enhanced readability of the LLM-generated summaries compared to the corresponding therapists-generated summaries. This automated approach demonstrated a consistent performance across all groups, regardless of modality or disease site. Conclusions: This study demonstrated the high precision and consistency of the Llama 3.1 405B model in extracting keywords and summarizing CT simulation orders, suggesting that LLMs have great potential to help with this task, reduce the workload of therapists and improve workflow efficiency. △ Less

Submitted 27 January, 2025; originally announced January 2025.

A multi-purpose reciprocating probe drive system for studying the effect of gas-puffs on edge plasma dynamics in the ADITYA-U tokamak

Authors: Kaushlender Singh, Bharat Hegde, Ashok K. Kumawat, Ankit Kumar, M. S. Khan, Suman Dolui, Injamul Hoque, Tanmay Macwan, Sharvil Patel, Abha Kanik, Komal Yadav, Soumitra Banerjee, Harshita Raj, Devilal Kumawat, Pramila Gautam, Rohit Kumar, Suman Aich, Laxmikanta Pradhan, Ankit Patel, Kalpesh Galodiya, Abhijeet Kumar, Shwetang Pandya, K. M. Patel, K. A. Jadeja, D. C. Raval , et al. (2 additional authors not shown)

Abstract: This article reports the development of a versatile high-speed reciprocating drive system (HRDS) with interchangeable probe heads to characterize the edge plasma region of ADITYA-U tokamak. This reciprocating probe drive system consisting of Langmuir and magnetic probe heads, is designed, fabricated, installed, and operated for studying the extent of fuel/impurity gas propagation and its influence… ▽ More This article reports the development of a versatile high-speed reciprocating drive system (HRDS) with interchangeable probe heads to characterize the edge plasma region of ADITYA-U tokamak. This reciprocating probe drive system consisting of Langmuir and magnetic probe heads, is designed, fabricated, installed, and operated for studying the extent of fuel/impurity gas propagation and its influence on plasma dynamics in the far-edge region inside the last closed magnetic flux surface (LCFS). The HRDS is driven by a highly accurate, easy-to-control, dynamic, brushless, permanently excited synchronous servo motor operated by a PXI-commanded controller. The system is remotely operated and allows for precise control of the speed, acceleration, and distance traveled of the probe head on a shot-to-shot basis, facilitating seamless control of operations according to experimental requirements. Using this system, consisting of a linear array of Langmuir probes, measurements of plasma density, temperature, potential, and their fluctuations revealed that the fuel gas-puff impact these mean and fluctuating parameters up to three to four cm inside the LCFS. Attaching an array of magnetic probes to this system led to measurements of magnetic fluctuations inside the LCFS. The HRDS system is fully operational and serves as an important diagnostic tool for ADITYA-U tokamak. △ Less

Submitted 8 January, 2025; originally announced January 2025.

Stabilization of sawteeth instability by short gas pulse injection in ADITYA-U tokamak

Authors: Suman Dolui, Kaushlender Singh, Bharat Hegde, T. Macwan, SK Injamul Hoque, Umesh Nagora, Jaya Kumar A., S. Purohit, A. N. Adhiya, K. A. Jadeja, Harshita Raj, Ankit Kumar, Ashok K. Kumawat, Suman Aich, Rohit Kumar, K. M. Patel, P. Gautam, Sharvil Patel, N. Yadava, N. Ramaiya, M. K. Gupta, S. K. Pathak, M. B. Chowdhuri, S. Sharma, A. Kuley , et al. (6 additional authors not shown)

Abstract: Experiments on ADITYA-U tokamak show a marked enhancement in the sawtooth period by application of short gas puffs of fuel that cause a modification of the radial density profile. A consequent suppression of the trapped electron modes (TEMs) then leads to an increase in the core electron temperature. This slows down the heat propagation following a sawtooth crash, causing a delay in achieving the… ▽ More Experiments on ADITYA-U tokamak show a marked enhancement in the sawtooth period by application of short gas puffs of fuel that cause a modification of the radial density profile. A consequent suppression of the trapped electron modes (TEMs) then leads to an increase in the core electron temperature. This slows down the heat propagation following a sawtooth crash, causing a delay in achieving the critical temperature gradient inside the q = 1 surface required for the next sawtooth crash to happen. The overall scenario has strong similarities with the behavior of sawtooth under electron cyclotron resonance heating (ECRH). Our findings suggest an alternate, simpler technique for sawtooth control that may be usefully employed in small/medium-sized tokamaks that do not have an ECRH or any other auxiliary heating facility. △ Less

Submitted 15 August, 2025; v1 submitted 3 January, 2025; originally announced January 2025.

Precise Determination of Excited State Rotational Constants and Black-Body Thermometry in Coulomb Crystals of Ca$^+$ and CaH$^+$

Authors: Swapnil Patel, Kenneth R. Brown

Abstract: We present high-resolution rovibronic spectroscopy of calcium monohydride molecular ions (CaH$^+$) co-trapped in a Coulomb crystal with calcium ions ($^{40}$Ca$^+$), focusing on rotational transitions in the $|X^1Σ^+, ν" = 0> \rightarrow |A^1Σ^+, ν' = 2>$ manifold. By resolving individual P and R branch transitions with record precision and using Fortrat analysis, we extract key spectroscopic cons… ▽ More We present high-resolution rovibronic spectroscopy of calcium monohydride molecular ions (CaH$^+$) co-trapped in a Coulomb crystal with calcium ions ($^{40}$Ca$^+$), focusing on rotational transitions in the $|X^1Σ^+, ν" = 0> \rightarrow |A^1Σ^+, ν' = 2>$ manifold. By resolving individual P and R branch transitions with record precision and using Fortrat analysis, we extract key spectroscopic constants for the excited state $|A^1Σ^+, ν' = 2>$, specifically, the band origin, the rotational constant, and the centrifugal correction. Additionally, we demonstrate the application of high-resolution rotational spectroscopy of CaH$^+$ presented here as an in-situ probe of local environmental temperature. We correlate the relative amplitudes of the observed transitions to the underlying thermalized ground-state rotational population distribution and extract the black-body radiation (BBR) temperature. △ Less

Submitted 30 November, 2024; originally announced December 2024.

Journal ref: J. Phys. Chem. A (2025) 129 16 3624

Enabling Clinical Use of Linear Energy Transfer in Proton Therapy for Head and Neck Cancer -- A Review of Implications for Treatment Planning and Adverse Events Study

Authors: Jingyuan Chen, Yunze Yang, Hongying Feng, Chenbin Liu, Lian Zhang, Jason M. Holmes, Zhengliang Liu, Haibo Lin, Tianming Liu, Charles B. Simone II, Nancy Y. Lee, Steven E. Frank, Daniel J. Ma, Samir H. Patel, Wei Liu

Abstract: Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay be… ▽ More Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay between dose, linear energy transfer (LET), and biological endpoints. Lack of heterogeneity control or the understanding of the complex interplay may result in unexpected adverse events and suboptimal patient outcomes. On the other hand, expanding our knowledge of variable tumor RBE and LET optimization may provide a better management strategy for radioresistant tumors. This review examines recent advancements in LET calculation methods, including analytical models and Monte Carlo simulations. The integration of LET into plan evaluation is assessed to enhance plan quality control. LET-guided robust optimization demonstrates promise in minimizing high-LET exposure to organs at risk, thereby reducing the risk of adverse events. Dosimetric seed spot analysis is discussed to show its importance in revealing the true LET-related effect upon the adverse event initialization by finding the lesion origins and eliminating the confounding factors from the biological processes. Dose-LET volume histograms (DLVH) are discussed as effective tools for correlating physical dose and LET with clinical outcomes, enabling the derivation of clinically relevant dose-LET volume constraints without reliance on uncertain RBE models. Based on DLVH, the dose-LET volume constraints (DLVC)-guided robust optimization is introduced to upgrade conventional dose-volume constraints-based robust optimization, which optimizes the joint distribution of dose and LET simultaneously. △ Less

Submitted 6 October, 2024; originally announced October 2024.

Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling

Authors: Shivam Barwey, Riccardo Balin, Bethany Lusch, Saumil Patel, Ramesh Balakrishnan, Pinaki Pal, Romit Maulik, Venkatram Vishwanath

Abstract: This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is… ▽ More This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). This concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National Laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer. △ Less

Submitted 2 October, 2024; originally announced October 2024.

High Energy Particle Detection with Large Area Superconducting Microwire Array

Authors: Cristián Peña, Christina Wang, Si Xie, Adolf Bornheim, Matías Barría, Claudio San Martín, Valentina Vega, Artur Apresyan, Emanuel Knehr, Boris Korzh, Jamie Luskin, Lautaro Narváez, Sahil Patel, Matthew Shaw, Maria Spiropulu

Abstract: We present the first detailed study of an 8-channel $2\times2$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV proton beam and 8 GeV electron and pion beam at the Fermilab Test Beam Facility. The SMSPD detection efficiency was measured for the first time for protons, electrons, and pions, enabled by the use of a silicon tracking telescope that provide… ▽ More We present the first detailed study of an 8-channel $2\times2$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV proton beam and 8 GeV electron and pion beam at the Fermilab Test Beam Facility. The SMSPD detection efficiency was measured for the first time for protons, electrons, and pions, enabled by the use of a silicon tracking telescope that provided precise spatial resolution of 30 $μ$m for 120 GeV protons and 130 $μ$m for 8 GeV electrons and pions. The result demonstrated consistent detection efficiency across pixels and at different bias currents. Time resolution of 1.15 ns was measured for the first time for SMSPD with proton, electron, and pions, enabled by the use of an MCP-PMT which provided a ps-level reference time stamp. The results presented is the first step towards developing SMSPD array systems optimized for high energy particle detection and identification for future accelerator-based experiments. △ Less

Submitted 6 March, 2025; v1 submitted 30 September, 2024; originally announced October 2024.

Report number: JINST 20 P03001

Journal ref: Journal of Instrumentation, Volume 20, March 2025

PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence

Authors: Saavan Patel, Philip Canoza, Adhiraj Datar, Steven Lu, Chirag Garg, Sayeef Salahuddin

Abstract: New computing paradigms are required to solve the most challenging computational problems where no exact polynomial time solution exists.Probabilistic Ising Accelerators has gained promise on these problems with the ability to model complex probability distributions and find ground states of intractable problems. In this context, we have demonstrated the Parallel Asynchronous Stochastic Sampler (P… ▽ More New computing paradigms are required to solve the most challenging computational problems where no exact polynomial time solution exists.Probabilistic Ising Accelerators has gained promise on these problems with the ability to model complex probability distributions and find ground states of intractable problems. In this context, we have demonstrated the Parallel Asynchronous Stochastic Sampler (PASS), the first fully on-chip integrated, asynchronous, probabilistic accelerator that takes advantage of the intrinsic fine-grained parallelism of the Ising Model and built in state of the art 14nm CMOS FinFET technology. We have demonstrated broad applicability of this accelerator on problems ranging from Combinatorial Optimization, Neural Simulation, to Machine Learning along with up to $23,000$x energy to solution improvement compared to CPUs on probabilistic problems. △ Less

Submitted 16 September, 2024; originally announced September 2024.

Comments: 13 page main text, 5 main figures, 21 pages supplementary and methods, 7 supplementary figures, 2 supplementary tables

$E\times B$ shear suppression of microtearing based transport in spherical tokamaks

Authors: B. S. Patel, M. R. Hardman, D. Kennedy, M. Giacomin, D. Dickinson, C. M. Roach

Abstract: Electromagnetic microtearing modes (MTMs) have been observed in many different spherical tokamak regimes. Understanding how these and other electromagnetic modes nonlinearly saturate is likely critical in understanding the confinement of a high $β$ spherical tokamak (ST). Equilibrium $E\times B$ sheared flows have sometimes been found to significantly suppress low $β$ ion scale transport in both g… ▽ More Electromagnetic microtearing modes (MTMs) have been observed in many different spherical tokamak regimes. Understanding how these and other electromagnetic modes nonlinearly saturate is likely critical in understanding the confinement of a high $β$ spherical tokamak (ST). Equilibrium $E\times B$ sheared flows have sometimes been found to significantly suppress low $β$ ion scale transport in both gyrokinetic simulations and in experiment. This work aims to understand the conditions under which $E\times B$ sheared flow impacts on the saturation of MTM simulations. Two experimental regimes are examined from MAST and NSTX, on surfaces that have unstable MTMs. The MTM driven transport on a local flux surface in MAST is shown to be more resilient to suppression via $E\times B$ shear, compared to the case from NSTX where the MTM transport is found to be significantly suppressed. This difference in the response to flow shear is explained through the impact of magnetic shear, $\hat{s}$ on the MTM linear growth rate dependence on ballooning angle, $θ_0$. At low $\hat{s}$, the growth rate depends weakly on $θ_0$, but at higher $\hat{s}$, the MTM growth rate peaks at $θ_0 = 0$, with regions of stability at higher $θ_0$. Equilibrium $E\times B$ sheared flows act to advect the $θ_0$ of a mode in time, providing a mechanism which suppresses the transport from these modes when they become stable. The dependence of $γ^{MTM}$ on $θ_0$ is in qualitative agreement with a recent theory [M.R. Hardman et al (2023)] at low $β$ when $q\sim1$, but the agreement worsens at higher $q$ where the theory breaks down. This work highlights the important role of the safety factor profile in determining the impact of equilibrium $E\times B$ shear on the saturation level of MTM turbulence. △ Less

Submitted 12 September, 2024; originally announced September 2024.

Comments: 20 pages, 16 figures

Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks

Authors: Shivam Barwey, Pinaki Pal, Saumil Patel, Riccardo Balin, Bethany Lusch, Venkatram Vishwanath, Romit Maulik, Ramesh Balakrishnan

Abstract: A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizatio… ▽ More A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizations, a baseline GNN layer (termed a message passing layer, which updates local node properties) is modified to account for synchronization of coincident graph nodes, rendering compatibility with commonly used element-based mesh connectivities. The architecture is multiscale in nature, and is comprised of a combination of coarse-scale and fine-scale message passing layer sequences (termed processors) separated by a graph unpooling layer. The coarse-scale processor embeds a query element (alongside a set number of neighboring coarse elements) into a single latent graph representation using coarse-scale synchronized message passing over the element neighborhood, and the fine-scale processor leverages additional message passing operations on this latent graph to correct for interpolation errors. Demonstration studies are performed using hexahedral mesh-based data from Taylor-Green Vortex and backward-facing step flow simulations at Reynolds numbers of 1600 and 3200. Through analysis of both global and local errors, the results ultimately show how the GNN is able to produce accurate super-resolved fields compared to targets in both coarse-scale and multiscale model configurations. Reconstruction errors for fixed architectures were found to increase in proportion to the Reynolds number. Geometry extrapolation studies on a separate cavity flow configuration show promising cross-mesh capabilities of the super-resolution strategy. △ Less

Submitted 1 May, 2025; v1 submitted 12 September, 2024; originally announced September 2024.

MHD activity induced coherent mode excitation in the edge plasma region of ADITYA-U Tokamak

Authors: Kaushlender Singh, Suman Dolui, Bharat Hegde, Lavkesh Lachhvani, Sharvil Patel, Injamul Hoque, Ashok K. Kumawat, Ankit Kumar, Tanmay Macwan, Harshita Raj, Soumitra Banerjee, Komal Yadav, Abha Kanik, Pramila Gautam, Rohit Kumar, Suman Aich, Laxmikanta Pradhan, Ankit Patel, Kalpesh Galodiya, Daniel Raju, S. K. Jha, K. A. Jadeja, K. M. Patel, S. N. Pandya, M. B. Chaudhary , et al. (6 additional authors not shown)

Abstract: In this paper, we report the excitation of coherent density and potential fluctuations induced by magnetohydrodynamic (MHD) activity in the edge plasma region of ADITYA-U Tokamak. When the amplitude of the MHD mode, mainly the m/n = 2/1, increases beyond a threshold value of 0.3-0.4 %, coherent oscillations in the density and potential fluctuations are observed having the same frequency as that of… ▽ More In this paper, we report the excitation of coherent density and potential fluctuations induced by magnetohydrodynamic (MHD) activity in the edge plasma region of ADITYA-U Tokamak. When the amplitude of the MHD mode, mainly the m/n = 2/1, increases beyond a threshold value of 0.3-0.4 %, coherent oscillations in the density and potential fluctuations are observed having the same frequency as that of the MHD mode. The mode numbers of these MHD induced density and potential fluctuations are obtained by Langmuir probes placed at different radial, poloidal, and toroidal locations in the edge plasma region. Detailed analyses of these Langmuir probe measurements reveal that the coherent mode in edge potential fluctuation has a mode structure of m/n = 2/1 whereas the edge density fluctuation has an m/n = 1/1 structure. It is further observed that beyond the threshold, the coupled power fraction scales almost linearly with the magnitude of magnetic fluctuations. Furthermore, the rise rates of the coupled power fraction for coherent modes in density and potential fluctuations are also found to be dependent on the growth rate of magnetic fluctuations. The disparate mode structures of the excited modes in density and plasma potential fluctuations suggest that the underlying mechanism for their existence is most likely due to the excitation of the global high-frequency branch of zonal flows occurring through the coupling of even harmonics of potential to the odd harmonics of pressure due to 1/R dependence of the toroidal magnetic field. △ Less

Submitted 23 July, 2024; originally announced July 2024.

Integrated Mode-Hop-Free Tunable Lasers at 780 nm for Chip-Scale Classical and Quantum Photonic Applications

Authors: Joshua E. Castro, Eber Nolasco-Martinez, Paolo Pintus, Zeyu Zhang, Boqiang Shen, Theodore Morin, Lillian Thiel, Trevor J. Steiner, Nicholas Lewis, Sahil D. Patel, John E. Bowers, David M. Weld, Galan Moody

Abstract: In the last decade, remarkable advances in integrated photonic technologies have enabled table-top experiments and instrumentation to be scaled down to compact chips with significant reduction in size, weight, power consumption, and cost. Here, we demonstrate an integrated continuously tunable laser in a heterogeneous gallium arsenide-on-silicon nitride (GaAs-on-SiN) platform that emits in the far… ▽ More In the last decade, remarkable advances in integrated photonic technologies have enabled table-top experiments and instrumentation to be scaled down to compact chips with significant reduction in size, weight, power consumption, and cost. Here, we demonstrate an integrated continuously tunable laser in a heterogeneous gallium arsenide-on-silicon nitride (GaAs-on-SiN) platform that emits in the far-red radiation spectrum near 780 nm, with 20 nm tuning range, <6 kHz intrinsic linewidth, and a >40 dB side-mode suppression ratio. The GaAs optical gain regions are heterogeneously integrated with low-loss SiN waveguides. The narrow linewidth lasing is achieved with an extended cavity consisting of a resonator-based Vernier mirror and a phase shifter. Utilizing synchronous tuning of the integrated heaters, we show mode-hop-free wavelength tuning over a range larger than 100 GHz (200 pm). To demonstrate the potential of the device, we investigate two illustrative applications: (i) the linear characterization of a silicon nitride microresonator designed for entangled-photon pair generation, and (ii) the absorption spectroscopy and locking to the D1 and D2 transition lines of 87-Rb. The performance of the proposed integrated laser holds promise for a broader spectrum of both classical and quantum applications in the visible range, encompassing communication, control, sensing, and computing. △ Less

Submitted 22 July, 2024; originally announced July 2024.

A difference-free conservative phase-field lattice Boltzmann method

Authors: Chunheng Zhao, Saumil Patel, Taehun Lee

Abstract: We propose an innovative difference-free scheme that combines the one-fluid lattice Boltzmann method (lBM) with the conservative phase-field (CPF) lBM to effectively solve large-scale two-phase fluid flow problems. The difference-free scheme enables the derivation of the derivative of the order parameter and the normal vector through the moments of the particle distribution function (PDF). We furt… ▽ More We propose an innovative difference-free scheme that combines the one-fluid lattice Boltzmann method (lBM) with the conservative phase-field (CPF) lBM to effectively solve large-scale two-phase fluid flow problems. The difference-free scheme enables the derivation of the derivative of the order parameter and the normal vector through the moments of the particle distribution function (PDF). We further incorporate the surface tension force in a continuous surface stress form into the momentum equations by modifying the equilibrium PDF to eliminate the divergence operator. Consequently, the entire computation process, executed without any inter-grid finite difference formulation, demonstrates improved efficiency, making it an ideal choice for high-performance computing applications. We conduct simulations of a single static droplet to evaluate the intensity of spurious currents and assess the accuracy of the scheme. We then introduce the density or viscosity ratio and apply an external body force to model the Rayleigh-Taylor instability and the behavior of a single rising bubble, respectively. Finally, we employ our method to study the phenomenon of a single bubble breaking up in a Taylor-Green vortex. The comparison between the difference-free scheme and the finite difference method demonstrates the scheme's capability to yield accurate results. Furthermore, based on the performance evaluation, the current scheme exhibits an impressive $47\% $ increase in efficiency compared to the previous method. △ Less

Submitted 16 July, 2024; originally announced July 2024.

Measurement of zero-frequency fluctuations generated by coupling between Alfven modes in the JET tokamak

Authors: Juan Ruiz Ruiz, Jeronimo Garcia, Michael Barnes, Mykola Dreval, Carine Giroud, Valerian H. Hall-Chen, Michael R. Hardman, Jon C. Hillesheim, Yevgen Kazakov, Samuele Mazzi, Felix I. Parra, Bhavin S. Patel, Alexander A. Schekochihin, Ziga Stancar, the JET Contributors, the EUROfusion Tokamak Exploitation Team

Abstract: We report the first experimental detection of a zero-frequency fluctuation that is pumped by an Alfvèn mode in a magnetically confined plasma. Core-localized bidirectional Alfvèn modes of frequency inside the toroidicity-induced gap (and its harmonics) exhibit three-wave coupling interactions with a zero-frequency fluctuation. The observation of the zero-frequency fluctuation is consistent with th… ▽ More We report the first experimental detection of a zero-frequency fluctuation that is pumped by an Alfvèn mode in a magnetically confined plasma. Core-localized bidirectional Alfvèn modes of frequency inside the toroidicity-induced gap (and its harmonics) exhibit three-wave coupling interactions with a zero-frequency fluctuation. The observation of the zero-frequency fluctuation is consistent with theoretical and numerical predictions of zonal modes pumped by Alfvén modes, and is correlated with an increase in the deep core ion temperature, temperature gradient, and confinement factor $H_{89,P}$. Despite the energetic particle transport induced by the Alfvèn eigenmodes, the generation of a zero-frequency fluctuation that can suppress the turbulence leads to an overall improvement of confinement. △ Less

Submitted 21 January, 2025; v1 submitted 1 July, 2024; originally announced July 2024.

Photoinduced Charge Transfer in Transition Metal Dichalcogenide Quantum Dots

Authors: Praveen Mishra, Arun Singh Patel, Sanjay Kumar Chauhan, Anirban Chakraborti

Abstract: In this paper, we have explored the charge transfer mechanism in transition metal dichalcogenide (TMDC) quantum dots (QDs) of molybdenum disulfide ($\rm{MoS_2}$) and tungsten disulfide ($\rm{WS_2}$). Rhodamine 6G (R6G), a dye from the rhodamine family, has been employed as the fluorescent molecule, with MoS$_2$ and WS$_2$ QDs acting as electron acceptors in the photo-induced charge transfer proces… ▽ More In this paper, we have explored the charge transfer mechanism in transition metal dichalcogenide (TMDC) quantum dots (QDs) of molybdenum disulfide ($\rm{MoS_2}$) and tungsten disulfide ($\rm{WS_2}$). Rhodamine 6G (R6G), a dye from the rhodamine family, has been employed as the fluorescent molecule, with MoS$_2$ and WS$_2$ QDs acting as electron acceptors in the photo-induced charge transfer process. The TMDC QDs were synthesized using a top-down approach and characterized through transmission electron microscopy (TEM), UV-Vis spectrophotometry, and fluorimetry. TEM images revealed well-dispersed particles measuring 2 nm in size. These QDs exhibit strong fluorescence emission when excited with light at wavelengths below 350 nm. Under light exposure, photons generate charges in the fluorescent dye molecules, and the TMDC QDs facilitate the charge transfer process. The charge transfer phenomenon was investigated using time-correlated single photon counting (TCSPC), a time-resolved fluorescence spectroscopic technique. The time-resolved fluorescence study indicated a change in the fluorescence (FL) lifetime of R6G molecules in the presence of QDs. The FL lifetime of R6G molecules without QDs was found to be 4.0 ns, which decreased to 1.9 ns and 3.8 ns in the presence of MoS$_2$ and WS$_2$ QDs, respectively. This reduction in FL lifetime suggests that the MoS$_2$ and WS$_2$ QDs provide an additional pathway for photo-generated electrons in the excited state of R6G molecules. This research can be extended to optoelectronic devices, where charge transfer is crucial for device efficiency and performance. △ Less

Submitted 19 September, 2024; v1 submitted 31 May, 2024; originally announced May 2024.

Comments: 7 pages, 10 figures

Adaptive Proton Therapy Using CBCT-Guided Digital Twins

Authors: Chih-Wei Chang, Zhen Tian, Richard L. J. Qiu, H. Scott McGinnis, Duncan Bohannon, Pretesh Patel, Yinan Wang, David S. Yu, Sagar A. Patel, Jun Zhou, Xiaofeng Yang

Abstract: This study aims to develop a digital twin (DT) framework to enhance adaptive proton stereotactic body radiation therapy (SBRT) for prostate cancer. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes. This study seeks to address these uncertain… ▽ More This study aims to develop a digital twin (DT) framework to enhance adaptive proton stereotactic body radiation therapy (SBRT) for prostate cancer. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes. This study seeks to address these uncertainties using DT concept, with the goal of improving treatment quality, potentially revolutionizing prostate radiotherapy to offer personalized treatment solutions. Our study presented a pioneering approach that leverages DT technology to enhance adaptive proton SBRT. The framework improves treatment plans by utilizing patient-specific CTV setup uncertainty, which is usually smaller than conventional clinical setups. This research contributes to the ongoing efforts to enhance the efficiency and efficacy of prostate radiotherapy, with ultimate goals of improving patient outcomes and life quality. △ Less

Submitted 17 May, 2024; v1 submitted 16 May, 2024; originally announced May 2024.

Journal ref: Physics in Medicine & Biology. 2025, Volume 70, Number 2

A quasi-linear model of electromagnetic turbulent transport and its application to flux-driven transport predictions for STEP

Authors: M. Giacomin, D. Dickinson, W. Dorland, N. R. Mandell, A. Bokshi, F. J. Casson, H. G. Dudding, D. Kennedy, B. S. Patel, C. M. Roach

Abstract: A quasi-linear reduced transport model is developed from a database of high-$β$ electromagnetic nonlinear gyrokinetic simulations performed with Spherical Tokamak for Energy Production (STEP) relevant parameters. The quasi-linear model is fully electromagnetic and accounts for the effect of equilibrium flow shear using a novel approach. Its flux predictions are shown to agree quantitatively with p… ▽ More A quasi-linear reduced transport model is developed from a database of high-$β$ electromagnetic nonlinear gyrokinetic simulations performed with Spherical Tokamak for Energy Production (STEP) relevant parameters. The quasi-linear model is fully electromagnetic and accounts for the effect of equilibrium flow shear using a novel approach. Its flux predictions are shown to agree quantitatively with predictions from local nonlinear gyrokinetic simulations across a broad range of STEP-relevant local equilibria. This reduced transport model is implemented in the T3D transport solver that is used to perform the first flux-driven simulations for STEP to account for transport from hybrid-KBM turbulence, which dominates over a wide region of the core plasma. Nonlinear gyrokinetic simulations of the final transport steady state from T3D return turbulent fluxes that are consistent with the reduced model, indicating that the quasi-linear model may also be appropriate for describing the transport steady state. Within the assumption considered here, our simulations support the existence of a transport steady state in STEP with a fusion power comparable to that in the burning flat-top of the conceptual design, but do not demonstrate how this state can be accessed. △ Less

Submitted 9 January, 2025; v1 submitted 26 April, 2024; originally announced April 2024.

Journal ref: J. Plasma Phys. 91 (2025) E16

Flat-top plasma operational space of the STEP power plant

Authors: E. Tholerus, F. J. Casson, S. P. Marsden, T. Wilson, D. Brunetti, P. Fox, S. J. Freethy, T. C. Hender, S. S. Henderson, A. Hudoba, K. K. Kirov, F. Koechl, H. Meyer, S. I. Muldrew, C. Olde, B. S. Patel, C. M. Roach, S. Saarelma, G. Xia

Abstract: STEP is a spherical tokamak prototype power plant that is being designed to demonstrate net electric power. The design phase involves the exploitation of plasma models to optimise fusion performance subject to satisfying various physics and engineering constraints. A modelling workflow, including integrated core plasma modelling, MHD stability analysis, SOL and pedestal modelling, coil set and fre… ▽ More STEP is a spherical tokamak prototype power plant that is being designed to demonstrate net electric power. The design phase involves the exploitation of plasma models to optimise fusion performance subject to satisfying various physics and engineering constraints. A modelling workflow, including integrated core plasma modelling, MHD stability analysis, SOL and pedestal modelling, coil set and free boundary equilibrium solvers, and whole plant design, has been developed to specify the design parameters and to develop viable scenarios. The integrated core plasma model JETTO is used to develop individual flat-top operating points that satisfy imposed criteria for fusion power performance within operational constraints. Key plasma parameters such as normalised beta, Greenwald density fraction, auxiliary power and radiated power have been scanned to scope the operational space and to derive a collection of candidate non-inductive flat-top points. The assumed auxiliary heating and current drive is either from electron cyclotron systems only or a combination of electron cyclotron and electron Bernstein waves. At present stages of transport modelling, there is a large uncertainty in overall confinement for relevant parameter regimes. For each of the two auxiliary heating and current drive systems scenarios, two candidate flat-top points have been developed based on different confinement assumptions, totalling to four operating points. A lower confinement assumption generally suggests operating points in high-density, high auxiliary power regimes, whereas higher confinement would allow access to a broader parameter regime in density and power while maintaining target fusion power performance. △ Less

Submitted 14 March, 2024; originally announced March 2024.

Comments: 44 pages, 15 figures. Submitted to Nucl. Fusion (IOP)

On the importance of parallel magnetic-field fluctuations for electromagnetic instabilities in STEP

Authors: D. Kennedy, C. M. Roach, M. Giacomin, P. Ivanov, T. Adkins, F. Sheffield, T. G örler, A. Bokshi, D. Dickinson, H. G. Dudding, B. S. Patel

Abstract: [ABRIDGED] This paper discusses the importance of parallel perturbations of the magnetic-field in gyrokinetic simulations of electromagnetic instabilities and turbulence at mid-radius in the burning plasma phase of the conceptual high-$β$, reactor-scale, tight-aspect-ratio tokamak STEP. Previous studies have revealed the presence of unstable hybrid kinetic ballooning modes (hKBMs) at binormal scal… ▽ More [ABRIDGED] This paper discusses the importance of parallel perturbations of the magnetic-field in gyrokinetic simulations of electromagnetic instabilities and turbulence at mid-radius in the burning plasma phase of the conceptual high-$β$, reactor-scale, tight-aspect-ratio tokamak STEP. Previous studies have revealed the presence of unstable hybrid kinetic ballooning modes (hKBMs) at binormal scales approaching the ion Larmor radius. In this STEP plasma it was found that the hKBM requires the inclusion of parallel magnetic-field perturbations to be linearly unstable. Here, the extent to which the inclusion of fluctuations in the parallel magnetic-field can be relaxed is explored through gyrokinetic simulations. In particular, the frequently used MHD approximation (dropping $δ\! B_{\parallel}$ and setting the $\nabla B$ drift frequency equal to the curvature drift frequency) is discussed and simulations explore whether this approximation is useful for modelling STEP plasmas. It is shown that the MHD approximation can reproduce some of the linear properties of the full STEP gyrokinetic system, but is too stable at low $k_y$ and nonlinear simulations using the MHD approximation result in very different transport states. It is demonstrated that the MHD approximation is challenged by the high $β^{\prime}$ values in STEP, and that the approximation improves considerably at lower $β^{\prime}$. Furthermore, it is shown that the sensitivity of STEP to $δ\! B_{\parallel}$ fluctuations is primarily because the plasma sits close to marginality and it is shown that in slightly more strongly driven conditions the hKBM is unstable without $δ\! B_{\parallel}.$ Crucially, it is demonstrated that the state of large transport typically predicted by local electromagnetic gyrokinetic simulations of STEP plasmas is not solely due to $δ\! B_{\parallel}$ physics. △ Less

Submitted 20 May, 2024; v1 submitted 16 February, 2024; originally announced February 2024.

Comments: 29th IAEA FEC conference

Exactly solvable Hamiltonian fragments obtained from a direct sum of Lie algebras

Authors: Smik Patel, Artur F. Izmaylov

Abstract: Exactly solvable Hamiltonians are useful in the study of quantum many-body systems using quantum computers. In the variational quantum eigensolver, a decomposition of the target Hamiltonian into exactly solvable fragments can be used for evaluation of the energies via repeated quantum measurements. In this work, we apply more general classes of exactly solvable qubit Hamiltonians than previously c… ▽ More Exactly solvable Hamiltonians are useful in the study of quantum many-body systems using quantum computers. In the variational quantum eigensolver, a decomposition of the target Hamiltonian into exactly solvable fragments can be used for evaluation of the energies via repeated quantum measurements. In this work, we apply more general classes of exactly solvable qubit Hamiltonians than previously considered to address the Hamiltonian measurement problem. The most general exactly solvable Hamiltonians are defined by the condition that, within each simultaneous eigenspace of a set of Pauli symmetries, the Hamiltonian acts effectively as an element of a direct sum of so(N) Lie algebras, and can therefore be measured using a combination of unitaries in the associated Lie group, Clifford unitaries, and mid-circuit measurements. Application of such Hamiltonians to decomposing molecular electronic Hamiltonians via graph partitioning techniques shows a reduction in the total number of measurements required to estimate the expectation value compared with previously used exactly solvable qubit Hamiltonians. △ Less

Submitted 14 February, 2024; originally announced February 2024.

Radiatively Cooled Magnetic Reconnection Experiments Driven by Pulsed Power

Authors: R Datta, K Chandler, C E Myers, J P Chittenden, A J Crilly, C Aragon, D J Ampleford, J T Banasek, A Edens, W R Fox, S B Hansen, E C Harding, C A Jennings, H Ji, C C Kuranz, S V Lebedev, Q Looker, S G Patel, A J Porwitzky, G A Shipley, D A Uzdensky, D A Yager-Elorriaga, J D Hare

Abstract: We present evidence for strong radiative cooling in a pulsed-power-driven magnetic reconnection experiment. Two aluminum exploding wire arrays, driven by a 20 MA peak current, 300 ns rise time pulse from the Z machine (Sandia National Laboratories), generate strongly-driven plasma flows ($M_A \approx 7$) with anti-parallel magnetic fields, which form a reconnection layer ($S_L \approx 120$) at the… ▽ More We present evidence for strong radiative cooling in a pulsed-power-driven magnetic reconnection experiment. Two aluminum exploding wire arrays, driven by a 20 MA peak current, 300 ns rise time pulse from the Z machine (Sandia National Laboratories), generate strongly-driven plasma flows ($M_A \approx 7$) with anti-parallel magnetic fields, which form a reconnection layer ($S_L \approx 120$) at the mid-plane. The net cooling rate far exceeds the Alfvénic transit rate ($τ_{\text{cool}}^{-1}/τ_{\text{A}}^{-1} > 100$), leading to strong cooling of the reconnection layer. We determine the advected magnetic field and flow velocity using inductive probes positioned in the inflow to the layer, and inflow ion density and temperature from analysis of visible emission spectroscopy. A sharp decrease in X-ray emission from the reconnection layer, measured using filtered diodes and time-gated X-ray imaging, provides evidence for strong cooling of the reconnection layer after its initial formation. X-ray images also show localized hotspots, regions of strong X-ray emission, with velocities comparable to the expected outflow velocity from the reconnection layer. These hotspots are consistent with plasmoids observed in 3D radiative resistive magnetohydrodynamic simulations of the experiment. X-ray spectroscopy further indicates that the hotspots have a temperature (170 eV) much higher than the bulk layer ($\leq$ 75 eV) and inflow temperatures (about 2 eV), and that these hotspots generate the majority of the high-energy (> 1 keV) emission. △ Less

Submitted 31 January, 2024; originally announced January 2024.

Frequency-domain multiplexing of SNSPDs with tunable superconducting resonators

Authors: Sasha Sypkens, Lorenzo Minutolo, Sahil Patel, Emanuel Knehr, Alexander B. Walter, Henry G. Leduc, Lautaro Narváez, Ralph Chamberlin, Tracee Jamison-Hooks, Matthew D. Shaw, Peter K. Day, Boris Korzh

Abstract: This work culminates in a demonstration of an alternative Frequency Domain Multiplexing (FDM) scheme for Superconducting Nanowire Single-Photon Detectors (SNSPDs) using the Kinetic inductance Parametric UP-converter (KPUP) made out of NbTiN. There are multiple multiplexing architectures for SNSPDs that are already in use, but FDM could prove superior in applications where the operational bias curr… ▽ More This work culminates in a demonstration of an alternative Frequency Domain Multiplexing (FDM) scheme for Superconducting Nanowire Single-Photon Detectors (SNSPDs) using the Kinetic inductance Parametric UP-converter (KPUP) made out of NbTiN. There are multiple multiplexing architectures for SNSPDs that are already in use, but FDM could prove superior in applications where the operational bias currents are very low, especially for mid- and far-infrared SNSPDs. Previous FDM schemes integrated the SNSPD within the resonator, while in this work we use an external resonator, which gives more flexibility to optimize the SNSPD architecture. The KPUP is a DC-biased superconducting resonator in which a nanowire is used as its inductive element to enable sensitivity to current perturbations. When coupled to an SNSPD, the KPUP can be used to read out current pulses on the few $μ$A scale. The KPUP is made out of NbTiN, which has high non-linear kinetic inductance for increased sensitivity at higher current bias and high operating temperature. Meanwhile, the SNSPD is made from WSi, which is a popular material for broadband SNSPDs. To read out the KPUP and SNSPD array, a software-defined radio platform and a graphics processing unit are used. Frequency Domain Multiplexed SNSPDs have applications in astronomy, remote sensing, exoplanet science, dark matter detection, and quantum sensing. △ Less

Submitted 30 January, 2024; originally announced January 2024.

Improvements of readout signal integrity in mid-infrared superconducting nanowire single photon detectors

Authors: Sahil R. Patel, Marco Colangelo, Andrew D. Beyer, Gregor G. Taylor, Jason P. Allmaras, Emma E. Wollman, Matthew D. Shaw, Karl K. Berggren, Boris Korzh

Abstract: Superconducting nanowire single-photon detectors (SNSPDs) with high timing resolution and low background counts in the mid infrared (MIR) have the potential to open up numerous opportunities in fields such as exoplanet searches, direct dark matter detection, physical chemistry, and remote sensing. One challenge in pushing SNSPD sensitivity to the MIR is a decrease in the signal-to-noise ratio (SNR… ▽ More Superconducting nanowire single-photon detectors (SNSPDs) with high timing resolution and low background counts in the mid infrared (MIR) have the potential to open up numerous opportunities in fields such as exoplanet searches, direct dark matter detection, physical chemistry, and remote sensing. One challenge in pushing SNSPD sensitivity to the MIR is a decrease in the signal-to-noise ratio (SNR) of the readout signal as the critical currents become increasingly smaller. We overcome this trade-off with a new device architecture that employs impedance matching tapers and superconducting nanowire avalanche photodetectors to demonstrate increased SNR while maintaining saturated internal detection efficiency at 7.4 μm and getting close to saturation at 10.6 μm. This work provides a novel platform for pushing SNSPD sensitivity to longer wavelengths while improving the scalability of the readout electronics. △ Less

Submitted 28 January, 2024; originally announced January 2024.

Plasmoid formation and strong radiative cooling in a driven magnetic reconnection experiment

Authors: R. Datta, K. Chandler, C. E. Myers, J. P. Chittenden, A. J. Crilly, C. Aragon, D. J. Ampleford, J. T. Banasek, A. Edens, W. R. Fox, S. B. Hansen, E. C. Harding, C. A. Jennings, H. Ji, C. C. Kuranz, S. V. Lebedev, Q. Looker, S. G. Patel, A. Porwitzky, G. A. Shipley, D. A. Uzdensky, D. A. Yager-Elorriaga, J. D. Hare

Abstract: We present results from the first experimental study of strongly radiatively-cooled magnetic reconnection. Two exploding aluminum wire arrays, driven simultaneously by the Z machine ($I_{max} = 20 \, \text{MA}$, $t_{\text{rise}} = 300 \, \text{ns}$), generate a radiatively-cooled reconnection layer ($S_L \approx 120$) in which the total cooling rate exceeds the hydrodynamic transit rate (… ▽ More We present results from the first experimental study of strongly radiatively-cooled magnetic reconnection. Two exploding aluminum wire arrays, driven simultaneously by the Z machine ($I_{max} = 20 \, \text{MA}$, $t_{\text{rise}} = 300 \, \text{ns}$), generate a radiatively-cooled reconnection layer ($S_L \approx 120$) in which the total cooling rate exceeds the hydrodynamic transit rate ($τ_{\text{hydro}}/τ_{\text{cool}} > 100$). Measurements of X-ray emission from the reconnection layer using a filtered diode ($>1$ keV) show a narrow (50 ns FWHM) burst of emission at 220 ns after current start, consistent with the formation and subsequent rapid cooling of the reconnection layer. Time-gated X-ray images of the reconnection layer show fast-moving (up to 50 km/s) hotspots inside the layer, consistent with the presence of plasmoids observed in 3D resistive magnetohydrodynamic simulations. X-ray spectroscopy shows that these hotspots generate the majority of Al K-shell emission (at around 1.6 keV) prior to the onset of cooling, and exhibit temperatures of 170 eV, much greater than the temperature of the plasma inflows and the rest of the reconnection layer. △ Less

Submitted 9 January, 2024; originally announced January 2024.

A Theoretical Case Study of the Generalisation of Machine-learned Potentials

Authors: Yangshuai Wang, Shashwat Patel, Christoph Ortner

Abstract: Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challenge for their generalization to a broader range of systems outside the training set. Nevertheless, MLIPs have demonstrated impressive accuracy in predicting forces and energies in simulations involving intricate and compl… ▽ More Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challenge for their generalization to a broader range of systems outside the training set. Nevertheless, MLIPs have demonstrated impressive accuracy in predicting forces and energies in simulations involving intricate and complex structures. In this paper we aim to take steps towards rigorously explaining the excellent observed generalisation properties of MLIPs. Specifically, we offer a comprehensive theoretical and numerical investigation of the generalization of MLIPs in the context of dislocation simulations. We quantify precisely how the accuracy of such simulations is directly determined by a few key factors: the size of the training structures, the choice of training observations (e.g., energies, forces, virials), and the level of accuracy achieved in the fitting process. Notably, our study reveals the crucial role of fitting virials in ensuring the consistency of MLIPs for dislocation simulations. Our series of careful numerical experiments encompassing screw, edge, and mixed dislocations, supports existing best practices in the MLIPs literature but also provides new insights into the design of data sets and loss functions. △ Less

Submitted 2 November, 2023; originally announced November 2023.

Comments: arXiv admin note: text overlap with arXiv:2209.05366