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Physics Physics

physics Watch Physics

Tuesday 18 November

Computational Physics

A Self-Adjusting FEM-BEM Coupling Scheme for the Nonlinear Poisson-Boltzmann Equation

Mauricio Guerrero-Montero, Michal Bosy and Christopher D. Cooper

The Poisson-Boltzmann equation is widely used to model molecular electrostatics; however, it is usually solved in linearised form because the sinh nonlinearity is challenging, limiting its applicability in highly charged systems such as nucleic acids. This work presents a solution method for the nonlinear Poisson-Boltzmann equation based on a coupled finite/boundary element scheme that automatically finds an optimal relaxation parameter, ensuring fast and reliable convergence of the nonlinear solver without user intervention. We validated our solver against APBS for a spherical cavity, and used RNA-based structures to perform a thorough study of the different algorithmic choices, and to test our implementation. We found that the best alternative to solve the Poisson-Boltzmann equation was using a Newton-Raphson method where the nonlinearity was gradually introduced with a cubic approximation in the first iteration. Newton-Raphson was also the best method to find the optimal relaxation factor, reducing the number of iterations by 40%. Including other optimisation techniques, we were able to obtain a 1.37x speed-up with respect to the best hand-picked relaxation factor for 1HC8 (molecule with highest charge in our tests), avoiding any trial-and-error process to find the relaxation factor.
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Biological Physics · Cross-listed from nlin.PS

Microscopic Variability Alters Macroscopic Rotation Speed in Stochastic Spiral Waves

Jolien Kamphuis, Desmond Kabus, Hermen Jan Hupkes et al. (+1 more)

We present a general theory for noise-induced corrections to the angular velocity of spiral waves. Stochasticity produces two second-order effects: an instantaneous term from heterogeneity that always slows rotation, and an orbital-drift term from temporal fluctuations that can either accelerate or decelerate it. For our parameters, orbital drift is weaker, producing a net slowdown. Analytical predictions match Barkley-model simulations with temporal noise. Examination of additional noise types in silico confirms angular velocity slowing. This mechanism provides a robust route by which stochasticity reshapes spiral dynamics in excitable media, with direct implications for arrhythmias and neural wave propagation.
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All authors Jolien Kamphuis, Desmond Kabus, Hermen Jan Hupkes, Tim De Coster

Physics Education

Enabling Blind and Visually Impaired Individuals to Pursue Careers in Science

Ludovic Petitdemange and Salomé Nashed

Blind and Visually Impaired (BVI) Individuals face significant challenges in science due to the discipline's reliance on visual elements such as graphs, diagrams, and laboratory work. Traditional learning materials, such as Braille and large-print textbooks, are often scarce or delayed, while practical experiments are rarely adapted for accessibility. Additionally, mainstream educators lack the training to effectively support BVI students, and Teachers for the Visually Impaired (TVIs) often lack scientific expertise. As a result, BVI individuals remain underrepresented in scientific jobs, reinforcing a cycle of exclusion. However, technological advancements and inclusive initiatives are opening new opportunities. Outreach programs aim to make science engaging and accessible for BVI individuals through multi-sensory learning experiences. Hands-on involvement in these activities fosters confidence and interest in scientific careers. Beyond sparking interest, equipping BVI students with the right tools and skills is crucial for their academic success. Early exposure to assistive technologies enables BVI students to navigate scientific studies independently. Artificial Intelligence (AI) tools further enhance accessibility by converting visual data into descriptive text and providing interactive assistance. Several learning sessions demonstrated the effectiveness of these interventions, with participants successfully integrating into university-level science programs. Educating BVI and their teachers on these tools and good pratices is the aim of our project AccesSciencesDV. Research careers offer promising opportunities for BVI, especially in computational fields. By leveraging coding, data analysis, and AI-driven tools, BVI researchers can conduct high-level scientific work without relying on direct visual observations. The presence of BVI scientists enriches research environments.
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Optics · Cross-listed from cs.RO

Robot joint characterisation and control using a magneto-optical rotary encoder

Yunlong Guo, John Canning, Zenon Chaczko et al. (+1 more)

A robust and compact magneto-optical rotary encoder for the characterisation of robotic rotary joints is demonstrated. The system employs magnetic field-induced optical attenuation in a double-pass configuration using rotating nonuniform magnets around an optical circulator operating in reflection. The encoder tracks continuous 360{\deg} rotation with rotation sweep rates from {\nu} = 135 {\deg}/s to {\nu} = 370 {\deg}/s, and an angular resolution of {\Delta}{\theta} = 0.3{\deg}. This offers a low-cost and reliable alternative to conventional robot rotation encoders while maintaining competitive performance.
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All authors Yunlong Guo, John Canning, Zenon Chaczko, Gang-Ding Peng

Chemical Physics

Catalytic Resonance Theory: Kinetics and Frequency Response of Light-Promoted Catalysis

Paul J Dauenhauer

The illumination of catalytic surfaces with a continuous or pulsed stream of photons dynamically modulates surface chemistry for faster rates, higher conversion, or product selectivity control. To establish fundamental principles of dynamic photon-modulated catalysis, the photocatalytic conversion of a generic surface reaction was simulated to understand the kinetic implications of an independent stream of photons promoting surface product desorption. Simulations were conducted via the microkinetic model and kinetic Monte Carlo methods for the scenarios of a Poisson distribution, constant spacing between photons, and coordinated on/off pulsing of photon sources. The time-averaged photocatalytic rate at differential conditions for varying photon flux and temperatures indicated three kinetic regimes described by product thermal desorption control, surface reaction control, and an intermediate kinetic regime with a zero slope Arrhenius plot, consistent with a degree of rate control dominated by the photon arrival frequency (i.e., per-site photon flux). The maximum photocatalytic rate occurred orders of magnitude above the Sabatier limit at the resonance frequency, identified as the photon arrival frequency that matched the surface reaction rate constant. Non-equilibrium photocatalytic conversion occurred when the rate of photon-promoted product desorption was comparable to the rate of thermal product desorption. Results indicated the general utility of photon-promoted catalysis, with negligible benefit for rate enhancement and quantum yield with photon source pulsing or chopping due to the inherent dynamic nature of photons from light sources.
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Space Physics

Nowcasting of Aviation Radiation Using Geospace Environment Properties: A Machine Learning Approach

Sanjib K C, Viacheslav M Sadykov and Dustin Kempton

Radiation exposure at aviation altitudes presents significant health risks to aircrews due to the cumulative effects of ionizing radiation. Physics-based models estimate radiation levels based on geophysical and atmospheric parameters, but often struggle to capture the highly dynamic and complex nature of the radiation environment, limiting their real-time predictive capabilities. To address this challenge, we investigate machine learning (ML) methods to enhance real-time radiation nowcasting. Leveraging newly compiled ML-ready datasets, publicly available at https://dmlab.cs.gsu.edu/rdp/, we train supervised models capable of capturing both linear and non-linear relationships between Geospace conditions and atmospheric radiation levels. Our experiments demonstrate that the XGBoost model achieves approximately 10 percent improvement in prediction accuracy over the considered physics-based model. Furthermore, feature importance analysis reveals that certain Geospace properties, specifically solar polar fields, solar wind properties, and neutron monitor data, are impacting the nowcast of the radiation levels at flight altitudes. These findings suggest meaningful physical relationships between the near-Earth space environment and atmospheric radiation, and highlight the potential of ML-based approaches for operational space weather applications.
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Fluid Dynamics

Micro-Macro Simulation of Shallow Water Moment Equations

Vilém Rožek

Shallow flows are governed by the Navier-Stokes equations. They are commonly modelled using the shallow water equations, a great simplification of the Navier-Stokes equations, which often yields inaccurate results. For that reason, a model called shallow water moment equations has been developed. It uses more equations and variables than the shallow water equations. While this model is significantly more accurate, it is also computationally more expensive. To speed up computations, the micro-macro method may be used. The micro-macro method switches between two models of varying levels of detail allowing for larger stable time steps. In this paper we formulate the micro-macro method for shallow water moment equations. We perform a theoretical runtime analysis of the method and present a series of results for a dam break test and a wave transport test. The micro-macro method achieves a significant speed-up while retaining a sufficient level of accuracy.
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Biological Physics

Wind as Driver of Bird and Bat Abundance, Flight Direction, Altitude, and Speed on the North Atlantic Shelf

Abigale Snortland, Jeff Clerc, Cris Hein et al. (+1 more)

Quantifying the collision risk of birds and bats with offshore wind turbines requires an understanding of the drivers of flying animal behavior at offshore wind sites. An omnidirectional S-band radar system was deployed on a research barge on the Northeastern Shelf of the United States (40.9 deg N, 70.79 deg W) and collected data for a 5-week window during the 2024 autumn bird and bat migration. The barge also supported two profiling lidar systems that measured the wind speed and direction. This study presents a first methodological approach for analyzing radar and lidar data together, providing a framework for future analyses of offshore bird and bat movements that can be used to improve collision risk models. Coupling the radar animal tracks with measured wind speed profiles revealed that wind is a driver of animal presence, flight direction, flight height, and flight speed. Further, a hierarchical clustering methodology was developed to investigate behavior by approximate animal size. For example, smaller animals had concentrated flight direction distributions aligned with the wind and flew at a variety of altitudes, whereas bigger animals flew in a wide variety of directions but were concentrated at low altitudes. Our results provide the first insights into animal behavior at offshore wind sites with paired radar and lidar data.
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All authors Abigale Snortland, Jeff Clerc, Cris Hein, Emma Cotter

Medical Physics

Clinical Validation and Prospective Deployment of an Automated Deep Learning-Based Coronary Segmentation and Cardiac Toxicity Risk Prediction System

Christian V. Guthier, Christopher E Kehayias, Cosmin Ciausu et al. (+15 more)

Importance: Coronary algorithm for cardiac sub structures and prospective real-time surveillance of cardiac dose exposure. Methods: Retro and prospective study to validate AI auto-segmentation. A 3D UNet was trained on 560 thoracic CT scans from a single institution (2003-2014) and validated internally (n=70). External validation was performed in 283 patients treated at an independent institution (2005-2020). Clinical implementation comprised (1) retrospective analysis of 3,399 lung cancer patients treated in 2014-2022 and (2) prospective surveillance of 1,386 consecutive patients in 2023. Geometric accuracy, concordance of dose-volume parameters; association of AI-derived substructure metrics with outcome; temporal dose trends; and the proportion of patients exceeding prespecified risk. Results: Median (inter-quartile range) Dice/ASSD were 0.95 (0.94-0.96)/1.1 mm for the heart and 0.87 (0.82-0.90)/1.9 mm for the LAD; the median absolute difference between AI and manual LAD V15 was 1%. AI-derived LAD V15 remained independently associated with MACE (sub distribution hazard ratio [HR], 1.03%; 95% CI, 1.01-1.05) and ACM (adjusted HR, 1.02; 95% CI, 1.00-1.03), internally and externally. Retrospective deployment showed a 32% relative decline in median LAD V15 from 2014 to 2022 (12% to 8%) and identified high- risk doses in 1,086 of 3,399 patients (32%). Prospective surveillance flagged 264 contemporary patients (19%) for potential cardiology referral. Conclusions: A validated AI system accurately segments cardiac substructures, reproduces dose-outcome relationships, enables large-scale surveillance, and point-of-care alerts for high-risk patients. Automated cardiac dose monitoring could facilitate adoption of coronary-sparing therapy and follow-up.
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All authors Christian V. Guthier, Christopher E Kehayias, Cosmin Ciausu, Jordan O. Gasho, John He, Maria Oorloff, Samuel C. Zhang, Danielle S. Bitterman, Jeremy S. Bredfeldt, Kelly Fitzgerald, Benjamin H. Kann, David E. Kozono, Jennifer Steers, Marion Tonneau, Anju Nohria, Hugo J.W.L. Aerts, Katelyn M. Atkins, Raymond H. Mak

Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Third-Body Stabilization of Supercritical CO2 in CO Oxidation: Development and Application of a ReaxFF Force Field for the CO/O/CO2 System

Emdadul Haque Chowdhury, Masoud Aryanpour, Yun Kyung Shin et al. (+3 more)

Supercritical CO2 (scCO2) plays a crucial role as a solvent in separation processes, advanced power cycles, and materials processing. Nonetheless, the atomistic comprehension of how the dense scCO2 matrix influences the fundamental reaction of carbon monoxide (CO) is still insufficiently explored. Experimental studies and molecular dynamics (MD) simulations frequently fail to detect the highly reactive, transient intermediates, such as atomic oxygen (O), that drive these reactions. To address this issue, we have developed a novel ReaxFF reactive force field for the CO2/CO/O system. The force field parameters were calibrated using density functional theory and second-order Moller-Plesset calculations to model CO2 crystal properties, intermolecular interactions, bond dissociation curves, and reaction energy barriers. The force field reproduces the cohesive energy of the CO2 crystal, the pressure characteristics of bulk scCO2, the equation-of-state behavior over a wide pressure-density range, the pressure dependence of the C-O bond length under compression, and the structural properties of liquid and scCO2, as documented by experiments, ab-initio MD, and prominent non-reactive models. The force field was subsequently applied to study the CO + O -> CO2 reaction. In a dilute environment, the reaction is inefficient as the newly formed CO2 rapidly dissociates due to excess kinetic and potential energy acquired from the exothermic reaction. Conversely, in a dense scCO2 environment, the surrounding matrix acts as an efficient third body, stabilizing the emerging CO2 product via molecular collisions. Statistical analysis confirms an average excess energy dissipation of 133.9 +/- 3.6 kcal/mol over 112.4 +/- 17.9 ps. Kinetic energy decomposition reveals that approximately 92% of the excess kinetic energy is stored in internal (rotational and vibrational) degrees of freedom.
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All authors Emdadul Haque Chowdhury, Masoud Aryanpour, Yun Kyung Shin, Bladimir Ramos-Alvarado, Matthias Ihme, Adri van Duin

Computational Physics

Reconstruction of three-dimensional shapes of normal and disease-related erythrocytes from partial observations using multi-fidelity neural networks

Haizhou Wen, He Li and Zhen Li

Reconstruction of 3D erythrocyte or red blood cell (RBC) morphology from partial observations, such as microscope images, is essential for understanding the physiology of RBC aging and the pathology of various RBC disorders. In this study, we propose a multi-fidelity neural network (MFNN) approach to fuse high-fidelity cross-sections of an RBC, with a morphologically similar low-fidelity reference 3D RBC shape to recover its full 3D surface. The MFNN predictor combines a convolutional neural network trained on low-fidelity reference RBC data with a feedforward neural network that captures nonlinear morphological correlations, and augments training with surface area and volume constraints for regularization in the low-fidelity branch. This approach is theoretically grounded by a topological homeomorphism between a sphere and 3D RBC surfaces, with training data generated by dissipative particle dynamics simulations of stomatocyte-discocyte-echinocyte transformation. Benchmarking across diverse RBC shapes observed in normal and aged populations, our results show that the MFNN predictor can reconstruct complex RBC morphologies with over 95% coordinate accuracy when provided with at least two orthogonal cross-sections. It is observed that informative oblique cross-sections intersecting spicule tips of echinocytes improve both local and global feature reconstruction, highlighting the value of feature-aware sampling. Our study further evaluates the influence of sampling strategies, shape dissimilarity, and noise, showing enhanced robustness under physically constrained training. Altogether, these results demonstrate the capability of MFNN to reconstruct the 3D shape of normal and aged RBCs from partial cross-sections as observed in conventional microscope images, which could facilitate the quantitative analysis of RBC morphological parameters in normal and disease-related RBC samples.
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Chemical Physics

Resolving the Nature of the Lowest-Frequency Raman Mode of Liquid Water

Florian Pabst, Harald Forbert and Dominik Marx

The lowest-frequency Raman mode of water, observed through depolarized light scattering or optical Kerr effect techniques, is routinely used to track dynamic changes in water molecules near ions or biomolecules. Yet, the microscopic origin of this mode and its relation to dielectric relaxation still remains debated for pure water with conflicting interpretations attributing it to either translational or rotational molecular motions. In this study, we compute the low-frequency Raman spectrum in the GHz to THz range using ab initio simulations, achieving excellent agreement with experimental data. Detailed decomposition analysis reveals that the rotational and translational contributions are equally important, while strong negative orientational cross-correlations as well as internal field effects significantly modify the rotational component, making it distinct from expectations inferred from dielectric spectroscopy.
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Plasma Physics · Cross-listed from astro-ph.SR

Proton specific entropy as a proxy for the $O^{7+}/O^{6+}$ charge state ratio over heliocentric distance

Jack D. Collard, Tamar Ervin, Ryan M. Dewey et al. (+10 more)

While the fast solar wind has well-established origins in coronal holes, the source of the slow solar wind remains uncertain. Compositional metrics, such as heavy ion charge state ratios are set in the lower corona, providing insights into solar wind source regions. However, prior to the launch of Solar Orbiter, in situ measurements of heavy ion charge state were limited to distances of 1 AU and beyond. We investigate proton specific entropy as a proxy for the oxygen charge state ratio ($O^{7+}/O^{6+}$),which generally becomes frozen-in below ~1.8 Rsun, leveraging observations from Solar Orbiter's Heavy Ion Sensor and Proton and Alphas Sensor covering 0.28 to 1 AU. Our analysis confirms a strong anti-correlation between specific entropy and the oxygen charge state ratio that persists over a broad range of distances in the inner heliosphere. We categorize observed solar wind into fast solar wind, slow Alfvenic solar wind, and slow solar wind, identifying clear distinctions in specific entropy values and charge state ratios across these types. The work demonstrates the potential to use proton specific entropy as a classifier of solar wind source regions throughout the heliosphere. By establishing the $S_p$-$O^{7+}/O^{6+}$ relationship and quantifying its radial dependence, the specific entropy can be used as a quantity to identify the solar wind source region in the absence of in-situ charge state measurements. This motivates future studies as to the applicability of this proxy to near-Sun observations (such as Parker Solar Probe) and throughout the inner heliosphere.
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All authors Jack D. Collard, Tamar Ervin, Ryan M. Dewey, Yeimy J. Rivera, Aidan J. Nakhleh, Jean-Baptiste Dakeyo, Samuel T. Badman, Trevor A. Bowen, John W. Bonnell, Nicholeen M. Viall, Susan T. Lepri, Jim M. Raines, Stuart D. Bale

Fluid Dynamics

Computational and Experimental Comparison of CLF5605 and roamx-0201 Martian Helicopter Rotor Airfoils

Lidia Caros, Witold J. F. Koning, Takayuki Nagata et al. (+7 more)

This study compares aerodynamic performance of the CLF5605 rotor airfoil – which flew on Ingenuity from 2021 to 2024 – with that of a new optimized roamx-0201 airfoil designed for Martian conditions at NASA Ames. Specifically, performance is studied at a Reynolds number of 20,000 and a Mach number of 0.60, across a range of angles of attack, using three independent state-of-the-art methodologies: implicit large eddy simulations (ILES) using NASA's OVERFLOW solver, direct numerical simulations (DNS) using the high-order GPU-accelerated PyFR solver, and experimental testing in the Mars Wind Tunnel at Tohoku University. Discrepancies between results obtain using the various methodologies are analyzed and explained. Across all methodologies it can be seen that the roamx-0201 airfoil is able to achieve a given lift with less drag compared to the CLF5605 airfoil. Moreover, OVERFLOW and PyFR results show that the roamx-0201 airfoil has superior stall characteristics, and can achieve a maximum lift ~20% higher than that achieved by the CLF5605 airfoil. The work provides a strong body of evidence to support further studies into use of rotors based on the optimized roamx-0201 airfoil for future Mars helicopter missions.
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All authors Lidia Caros, Witold J. F. Koning, Takayuki Nagata, Keisuke Asai, Oliver Buxton, Natalia Perez Perez, Ethan A. Romander, Taku Nonomura, Haley V. Cummings, Peter Vincent

Computational Physics

PAS-Net: Physics-informed Adaptive Scale Deep Operator Network

Changhong Mou, Yeyu Zhang, Xuewen Zhu et al. (+1 more)

Nonlinear physical phenomena often show complex multiscale interactions; motivated by the principles of multiscale modeling in scientific computing, we propose PAS-Net, a physics-informed Adaptive-Scale Deep Operator Network for learning solution operators of nonlinear and singularly perturbed evolution PDEs with small parameters and localized features. Specifically, PAS-Net augments the trunk input in the physics informed Deep Operator Network (PI-DeepONet) with a prescribed (or learnable) locally rescaled coordinate transformation centered at reference points. This addition introduces a multiscale feature embedding that acts as an architecture-independent preconditioner which improves the representation of localized, stiff, and multiscale dynamics. From an optimization perspective, the adaptive-scale embedding in PAS-Net modifies the geometry of the Neural Tangent Kernel (NTK) associated with the neural network by increasing its smallest eigenvalue, which in turn improves spectral conditioning and accelerates gradient-based convergence. We further show that this adaptive-scale mechanism explicitly accelerates neural network training in approximating functions with steep transitions and strong asymptotic behavior, and we provide a rigorous proof of this function-approximation result within the finite-dimensional NTK matrix framework. We test the proposed PAS-Net on three different problems: (i) the one-dimensional viscous Burgers equation, (ii) a nonlinear diffusion-reaction system with sharp spatial gradients, and (iii) a two-dimensional eikonal equation. The numerical results show that PAS-Net consistently achieves higher accuracy and faster convergence than the standard DeepONet and PI-DeepONet models under a similar training cost.
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All authors Changhong Mou, Yeyu Zhang, Xuewen Zhu, Qiao Zhuang

Biological Physics

Asymptotics of Protein Number Distribution in Stochastic Gene Expression Models under Burst Approximation

Yuntao Lu and Yunxin Zhang

The burst approximation is a widely used technique to simplify stochastic gene expression models. However, the dynamics and analytical properties of the protein number distribution in gene expression models under the burst approximation are barely studied. In this study, we propose and systematically analyze surrogate models with multiple gene states and arbitrary burst size distributions. An analytical time-dependent solution to the chemical master equation is derived and then exploited in two directions. Theoretically, several fine properties of the protein number distribution are established using functional analysis. For geometrically distributed burst sizes, the distribution is dominated by a scaled negative binomial distribution, and is light-tailed in certain parameter regimes. Computationally, we develop efficient algorithms in three settings, enabling fast calculation of the protein number distribution. Furthermore, the approximation error relative to full gene expression models is estimated in terms of low-order moments of the distribution, thereby clarifying the validity of the burst approximation.
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Accelerator Physics

Redis-Based Streaming Architecture for Accelerator Beam Instrumentation DAQ Systems

S. Joshi and D. Steinkamp

The Fermilab Acceleraor Division, Beam Instrumentation Department, is always adopting modern and current software methodologies for complex DAQ architectures. This paper highlights the Redis Adapter (RA) as the key software component enabling high performance, modular communication between digitizers and distributed control systems by leveraging Redis and containerization. The RA provides a unified, efficient interface between Redis based data streams and consumer systems. In the legacy architecture, digitized data flowed through the custom, UDP based Distributed Data Communication Protocol in the middle layer. In the current system, DDCP remains the ingestion path, while the RA serves as the decoupling layer. The proposed system replaces old VME digitizers with a SOM-based digitizer that communicates with Redis using the RA. The RA acts as both a performance-critical bridge and a protocol-agnostic adapter, ensuring compatibility with legacy control frameworks while enabling future scalability and modularity. This restructuring of the middle layer also helps the system achieve high throughput, reduce latency, and simplify the data path. Finally, we will demonstrate how RA is utilized in our two core products to deliver both legacy compatibility and future flexibility.
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Optics

Membrane stepping optimization in Modulation Based Imaging

Paola Perion, Clara Magnin, Fulvia Arfelli et al. (+3 more)

Modulation-based imaging (MoBI) is an X-ray phase-contrast technique that uses an intensity modulator (or membrane) in the beam. Although MoBI can be performed in a single shot, multiple exposures are typically needed to improve the quality of the result. The membrane is typically moved using a regular stepping pattern for convenience; however, the impact of the membrane movement scheme on image quality has not been fully investigated yet. In this work, we explore optimized movement strategies aiming at improving MoBI performance. An experimental study tested optimization schemes based on global and local standard deviation metrics, and compared them with regular and random stepping motions. The results demonstrated superior contrast-to-noise ratio and reduced angular sensitivity in the optimized approaches compared to conventional stepping. These results were consistent across different membrane types, with honeycomb membranes showing the highest compatibility with the optimization procedure. Noise power spectrum analysis further validated the advantages of the optimized motion strategies. Overall, the results demonstrate that an optimized membrane movement can significantly improve MoBI image quality without increasing experimental complexity.
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All authors Paola Perion, Clara Magnin, Fulvia Arfelli, Bertrand Faure, Ralf Hendrik Menk, Emmanuel Brun

Classical Physics · Cross-listed from gr-qc

The Physics of Black Holes and Their Environments: Consequences for Gravitational Wave Science

Vitor Cardoso, Shauvik Biswas and Subhodeep Sarkar

Ten short years ago, we had the rare privilege of witnessing the onset of a renaissance in science: humanity finally succeeded in its arduous quest to directly detect gravitational waves. This breakthrough did not occur in a vacuum: it was the natural culmination of decades of research dedicated towards understanding the nature of gravitation based on Einstein's General Theory of Relativity. It is a story of false starts, perseverance, and remarkable insights, propelled as much by technological progress as by human curiosity. We now proudly live in the new golden age of gravitational physics. The detection of gravitational wave signals from the merger of binary black holes and neutron stars are becoming routine. Coupled with our theoretical understanding of phenomena in the strong gravity regime, black hole physics has become a precision science. The purpose of these lecture notes is to help the reader understand the language and framework of this rapidly evolving subject, and to develop the ability to interpret, think, and discuss ideas that lie at the confluence of gravitational wave astronomy and black hole physics. It is our hope that these notes will prepare students and colleagues for the next revolution when gravitational wave events become commonplace and we begin to observe unexpected features in the signal, indicating either surprising astrophysical scenarios or a strong need to modify the theoretical description of gravitational interactions. We provide first principles analysis of black hole and gravitational wave physics, and sometimes a very personal interpretation of results. We share with the readers a number of notebooks that will allow them to reproduce some of the most important results in the field, and could even help in carrying out state-of-the-art research. We also include a few original results that we think are helpful in understanding the broader picture.
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Data Analysis and Statistics · Cross-listed from hep-ph

How to pick the best anomaly detector?

Marie Hein, Gregor Kasieczka, Michael Krämer et al. (+3 more)

Anomaly detection has the potential to discover new physics in unexplored regions of the data. However, choosing the best anomaly detector for a given data set in a model-agnostic way is an important challenge which has hitherto largely been neglected. In this paper, we introduce the data-driven ARGOS metric, which has a sound theoretical foundation and is empirically shown to robustly select the most sensitive anomaly detection model given the data. Focusing on weakly-supervised, classifier-based anomaly detection methods, we show that the ARGOS metric outperforms other model selection metrics previously used in the literature, in particular the binary cross-entropy loss. We explore several realistic applications, including hyperparameter tuning as well as architecture and feature selection, and in all cases we demonstrate that ARGOS is robust to the noisy conditions of anomaly detection.
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All authors Marie Hein, Gregor Kasieczka, Michael Krämer, Louis Moureaux, Alexander Mück, David Shih

Physics and Society

Reviewing definition of resilience in different disciplines with a focus on disaster restructure systems

Saviz Saei and Nazanin Tajik

A key principle in resilience thinking is Embracing Change because change is, indeed, inevitable. In the face of a growing number of disasters, natural and human-made disasters, our critical infrastructures (CIs) are being challenged like never before. This recent trend has sparked a wave of interest among both practitioners and researchers in understanding and delving deeper into the concept of resilience across multiple disciplines. This paper provides an accessible review of these new insights, exploring various frameworks, guidebooks, and methodologies that define resilience through the lens of ecology, engineering, psychology, social science, community, and disaster management during crisis.
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Chemical Physics · Cross-listed from quant-ph

From Random Determinants to the Ground State

Hao Zhang and Matthew Otten

Accurate quantum many-body calculations often depend on reliable reference states or good human-designed ansätze, yet these sources of knowledge can become unreliable in hard problems like strongly correlated systems. We introduce the Trimmed Configuration Interaction (TrimCI) method, a prior-knowledge-free algorithm that builds accurate ground states directly from random Slater determinants. TrimCI iteratively expands the variational space and trims away unimportant states, allowing a random initial core to self-refine into an accurate approximation of exact ground state. Across challenging benchmarks, TrimCI achieves state-of-the-art accuracy with strikingly efficiency gains of several orders of magnitude. For [4Fe-4S] cluster, it matches recent quantum computing results with $10^6$-fold fewer determinants and CPU-hours. For the nitrogenase P-cluster, it matches selected-CI accuracy using $10^5$-fold fewer determinants. For $8\times8$ Hubbard model, it recovers over $99\%$ of the ground-state energy using only $10^{-28}$ of the Hilbert space. In some regimes, TrimCI attains orders-of-magnitude higher accuracy than AFQMC method. These results demonstrate that high-accuracy many-body ground states can be discovered directly from random determinants, establishing TrimCI as a prior-knowledge-free, accurate and highly efficient framework for quantum many-body systems. The compact explicit wavefunctions it produces further enable direct and rapid evaluation of observables.
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Atomic Physics

Static Laboratory-Frame Polarization of a Trapped Molecular Ion for CP-Violation Searches

Fabian Wolf

Today's most sensitive experiments for detecting CP-violating permanent electric dipole moments (EDM) rely on molecular spectroscopy. The high sensitivity arises from large internal electric fields that interact with the constituents of the molecule. For molecular ions it has long been assumed that experiments with static polarization from dc electric fields are infeasible, as the ion's charge would either shift it to a field free region or eject it from the trap. This constraint appears to make single ion quantum-logic clocks, among the most precise measurement devices available, incompatible with EDM measurements. Here, we demonstrate that, under typical trapping conditions, heavy molecular ions with small $\Omega/\Lambda$-doubling can be polarized by a static electric field while remaining confined in the Paul trap. This effect arises from a cancellation between the electrostatic force and the trap's ponderomotive force, resulting in an equilibrium position where the ion experiences a dc electric field component. Dynamic decoupling allows to implement co-magnetometry schemes in a single molecule and provides long interrogation times. This will allow the operation of a quantum-logic molecular radio-frequency clock in static electric fields, providing sensitivity to EDMs. Working with only a few ions and non-destructive detection techniques opens the door to the use of highly sensitive, rare, and radioactive molecular species as well as quantum-enhanced metrology schemes to achieve unprecedented levels of accuracy and precision.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Automated Prediction of Thermodynamic Properties via Bayesian Free-Energy Reconstruction from Molecular Dynamics

Ekaterina Spirande, Timofei Miryashkin, Andrei Kolmakov et al. (+1 more)

Accurate free-energy calculations are essential for predicting thermodynamic properties and phase stability, but existing methods are limited: phonon-based approaches neglect anharmonicity and liquids, while molecular dynamics (MD) is computationally demanding, neglects low-temperature quantum effects, and often requires manual planning and post-processing of simulations. We present a unified workflow that reconstructs the Helmholtz free-energy surface from MD data using Gaussian Process Regression (GPR), augmented with zero-point energy corrections from harmonic/quasi-harmonic theory. The framework propagates statistical uncertainties, mitigates finite-size effects, and employs active learning to optimize sampling in the volume-temperature space. It applies seamlessly to both crystalline and liquid phases. We demonstrate the methodology by computing heat capacities, thermal expansion, isothermal and adiabatic bulk moduli, and melting properties for nine elemental FCC and BCC metals using 20 classical and machine-learned interatomic potentials, with all predictions accompanied by quantified confidence intervals. Automated, general, and uncertainty-aware, the workflow advances high-throughput thermodynamics and provides a systematic benchmark for interatomic potentials.
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All authors Ekaterina Spirande, Timofei Miryashkin, Andrei Kolmakov, Alexander Shapeev

Computational Physics · Cross-listed from math.NA

Derivative of the truncated singular value and eigen decomposition

Jan Naumann

Recently developed applications in the field of machine learning and computational physics rely on automatic differentiation techniques, that require stable and efficient linear algebra gradient computations. This technical note provides a comprehensive and detailed discussion of the derivative of the truncated singular and eigenvalue decomposition. It summarizes previous work and builds on them with an extensive description of how to derive the relevant terms. A main focus is correctly expressing the derivative in terms of the truncated part, despite lacking knowledge of the full decomposition.
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Plasma Physics

Real-time time-dependent density functional theory for high-energy density physics

Alina Kononov, Minh Nguyen and Andrew D. Baczewski

Electronic response properties of high-energy density (HED) systems influence planetary structure, drive evolution of fusion targets, and underpin diagnostics in laboratory astrophysics. Real-time time-dependent density functional theory (TDDFT) offers a versatile modeling framework capable of accurately predicting the dynamic response of HED materials – including free-free, bound-free, and bound-bound contributions without requiring ad hoc state partitioning; capturing both collective and non-collective behavior; and applicable within the linear-response regime and beyond. We review the theoretical formalism of real-time TDDFT as applied to HED systems, provide a practical tutorial for computing relevant response properties (dynamic structure factors, conductivity, and stopping power), and comment on avenues for further development of this powerful computational method in service of HED science.
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Chemical Physics

Recent advances in stimulus-assisted nanoprecipitation for nanoparticle synthesis

Mingbo Li, Junhao Cai and Yawen Gao

Nanoprecipitation, the rapid solvent-displacement route to nanoscale phase separation, has matured from a simple batch operation into a versatile platform for nanomaterial synthesis. This review synthesizes recent progress in stimulus-assisted nanoprecipitation, wherein externally applied triggers (ultrasonic, electrical, supergravity, thermal, chemical, and photonic/other stimuli) are integrated with contemporary mixing technologies (batch, flash, microfluidic, membrane and high-shear reactors) to decouple and selectively control over nucleation, growth kinetics, and assembly processes. These methods allow for the precise tuning of the size, morphology, stability and functionality of nanoparticles (NPs), thereby broadening their applications in drug delivery, catalysis and materials science. We distill mechanistic principles by which each stimulus alters local supersaturation, chain mobility, interfacial instabilities, or droplet/film microreactor dynamics, and compare advantages and limitations by surveying research works from recent years. We also explore the potential development trends of multiscale coupling models, design rules for stimulus-compatible continuous reactors, and adoption of data-driven optimization frameworks to expand the capabilities of nanoprecipitation for advanced nanomaterial design.
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Applied Physics · Cross-listed from quant-ph

Measuring Reactive-Load Impedance with Transmission-Line Resonators Beyond the Perturbative Limit

Xuanjing Chu, Jinho Park, Jesse Balgley et al. (+8 more)

We develop an analytic framework to extract circuit parameters and loss tangent from superconducting transmission-line resonators terminated by reactive loads, extending analysis beyond the perturbative regime. The formulation yields closed-form relations between resonant frequency, participation ratio, and internal quality factor, removing the need for full-wave simulations. We validate the framework through circuit simulations, finite-element modeling, and experimental measurements of van der Waals parallel-plate capacitors, using it to extract the dielectric constant and loss tangent of hexagonal boron nitride. Statistical analysis across multiple reference resonators, together with multimode self-calibration, demonstrates consistent and reproducible extraction of both capacitance and loss tangent in close agreement with literature values. In addition to parameter extraction, the analytic relations provide practical design guidelines for maximizing energy participation ratio in the load and improving the precision of resonator-based material metrology.
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All authors Xuanjing Chu, Jinho Park, Jesse Balgley, Sean Clemons, Ted S. Chung, Kenji Watanabe, Takashi Taniguchi, Leonardo Ranzani, Martin V. Gustafsson, Kin Chung Fong, James Hone

Instrumentation and Detectors

Characterization of a CsI(Tl) Scintillator Coupled to a SiPM at Cryogenic Temperatures

M. Mirzakhani, R. Mahapatra and M. Platt

We report on the scintillation characterization of a 1 cm^3 CsI(Tl) crystal coupled to a 6 x 6 mm^2 SiPM read out with a custom transimpedance amplifier at cryogenic temperatures. The crystal was prepared with optical-grade surfaces and enclosed in a multi layer shielding system to suppress ambient light and background radiation. The detector response was studied at temperatures ranging from 100 K to room temperature. A figure of merit, defined as the ratio of the 59.5 keV peak position to the baseline resolution, showed a maximum at 175 K, indicating optimal photon detection efficiency. Pulse shape analysis demonstrated temperature-dependent variations in scintillation decay. At 175 K, the baseline resolution was sigma ~ 1 keV, corresponding to an effective threshold of ~ 3 keV. These results confirm the capability of the CsI(Tl) SiPM system to operate at low thresholds for rare-event searches and low-energy particle detection.
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Instrumentation and Detectors · Cross-listed from hep-ex

Initial performance results of the JUNO detector

Angel Abusleme, Thomas Adam, Kai Adamowicz et al. (+1136 more)

The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m$^2$ of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper presents the performance results of the detector, extensively studied during the commissioning of the water phase, the subsequent liquid scintillator filling phase, and the first physics runs. The liquid scintillator achieved an attenuation length of 20.6 m at 430 nm, while the high coverage PMT system and scintillator together yielded about 1785 photoelectrons per MeV of energy deposit at the detector centre, measured using the 2.223 MeV $\gamma$ from neutron captures on hydrogen with an Am-C calibration source. The reconstructed energy resolution is 3.4% for two 0.511 MeV $\gamma$ at the detector centre and 2.9% for the 0.93 MeV quenched Po-214 alpha decays from natural radioactive sources. The energy nonlinearity is calibrated to better than 1%. Intrinsic contaminations of U-238 and Th-232 in the liquid scintillator are below 10$^{-16}$ g/g, assuming secular equilibrium. The water Cherenkov detector achieves a muon detection efficiency better than 99.9% for muons traversing the liquid scintillator volume. During the initial science runs, the data acquisition duty cycle exceeded 97.8%, demonstrating the excellent stability and readiness of JUNO for high-precision neutrino physics.
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All authors Angel Abusleme, Thomas Adam, Kai Adamowicz, David Adey, Shakeel Ahmad, Rizwan Ahmed, Timo Ahola, Sebastiano Aiello, Fengpeng An, Guangpeng An, Costas Andreopoulos, Giuseppe Andronico, João Pedro Athayde Marcondes de André, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, Didier Auguste, Margherita Buizza Avanzini, Andrej Babic, Jingzhi Bai, Weidong Bai, Nikita Balashov, Roberto Barbera, Andrea Barresi, Davide Basilico, Eric Baussan, Beatrice Bellantonio, Marco Bellato, JeanLuc Beney, Marco Beretta, Antonio Bergnoli, Enrico Bernieri, Nikita Bessonov, David Biaré, Daniel Bick, Lukas Bieger, Svetlana Biktemerova, Thilo Birkenfeld, David Blum, Simon Blyth, Sara Boarin, Manuel Boehles, Anastasia Bolshakova, Mathieu Bongrand, Aurélie Bonhomme, Clément Bordereau, Matteo Borghesi, Augusto Brigatti, Timothee Brugiere, Riccardo Brugnera, Riccardo Bruno, Jonas Buchholz, Antonio Budano, Max Buesken, Mario Buscemi, Severino Bussino, Jose Busto, Ilya Butorov, Marcel Büchner, Anatael Cabrera, Barbara Caccianiga, Boshuai Cai, Hao Cai, Xiao Cai, Yanke Cai, Yi-zhou Cai, Zhiyan Cai, Stéphane Callier, Steven Calvez, Antonio Cammi, Agustin Campeny, Dechang Cai, Chuanya Cao, Dewen Cao, Guofu Cao, Jun Cao, Yaoqi Cao, Rossella Caruso, Aurelio Caslini, Cédric Cerna, Vanessa Cerrone, Daniele Cesini, Chi Chan, Jinfan Chang, Yun Chang, Milo Charavet, Tim Charissé, Auttakit Chatrabhuti, Chao Chen, Guoming Chen, Haitao Chen, Haotian Chen, Jiahui Chen, Jian Chen, Jing Chen, Junyou Chen, Lihao Chen, Mali Chen, Mingming Chen, Pingping Chen, Po-An Chen, Quanyou Chen, Shaomin Chen, Shenjian Chen, Shi Chen, Shiqiang Chen, Sisi Chen, Xin Chen, Xuan Chen, Xurong Chen, Yi-Wen Chen, Yiming Chen, Yixue Chen, Yu Chen, Ze Chen, Zelin Chen, Zhang Chen, Zhangming Chen, Zhiyuan Chen, Zhongchang Chen, Zikang Chen, Brian Cheng, Jie Cheng, Yaping Cheng, Yu Chin Cheng, Zhaokan Cheng, Alexander Chepurnov, Alexey Chetverikov, Davide Chiesa, Pietro Chimenti, Yen-Ting Chin, Pin-Jung Chiu, Po-Lin Chou, Ziliang Chu, Artem Chukanov, Neetu Raj Singh Chundawat, Anna Chuvashova, Gérard Claverie, Catia Clementi, Barbara Clerbaux, Claudio Coletta, Marta Colomer Molla, Flavio Dal Corso, Daniele Corti, Salvatore Costa, Simon Csakli, Chenyang Cui, Shanshan Cui, Lorenzo Vincenzo D'Auria, Olivia Dalager, Jaydeep Datta, Luis Delgadillo Franco, Jiawei Deng, Zhi Deng, Ziyan Deng, Wilfried Depnering, Hanna Didenko, Xiaoyu Ding, Xuefeng Ding, Yayun Ding, Bayu Dirgantara, Carsten Dittrich, Sergey Dmitrievsky, David Doerflinger, Tadeas Dohnal, Maria Dolgareva, Dmitry Dolzhikov, Chuanshi Dong, Haojie Dong, Jianmeng Dong, Lan Dong, Damien Dornic, Evgeny Doroshkevich, Wei Dou, Marcos Dracos, Olivier Drapier, Tobias Drohmann, Frédéric Druillole, Ran Du, Shuxian Du, Yujie Duan, Katherine Dugas, Stefano Dusini, Hongyue Duyang, Martin Dvorak, Jessica Eck, Timo Enqvist, Heike Enzmann, Andrea Fabbri, Ulrike Fahrendholz, Lukas Fajt, Donghua Fan, Lei Fan, Liangqianjin Fan, Can Fang, Jian Fang, Wenxing Fang, Marco Fargetta, Elia Stanescu Farilla, Anna Fatkina, Laurent Favart, Dmitry Fedoseev, Zhengyong Fei, Franz von Feilitzsch, Vladko Fekete, Li-Cheng Feng, Qichun Feng, Shaoting Feng, Giovanni Ferrante, Federico Ferraro, Daniela Fetzer, Giovanni Fiorentini, Andrey Formozov, Marcellin Fotzé, Amélie Fournier, Sabrina Franke, Arran Freegard, Florian Fritsch, Ying Fu, Haonan Gan, Feng Gao, Ruixuan Gao, Ruohan Gao, Shijiao Gao, Alberto Garfagnini, Gerard Gaudiot, Arsenii Gavrikov, Raphaël Gazzini, Christoph Genster, Diwash Ghimire, Marco Giammarchi, Agnese Giaz, Gianfranco Giordano, Nunzio Giudice, Franco Giuliani, Alexandre Goettel, Maxim Gonchar, Guanda Gong, Guanghua Gong, Hui Gong, Michel Gonin, Oleg Gorchakov, Yuri Gornushkin, Marco Grassi, Christian Grewing, Maxim Gromov, Vasily Gromov, Minhao Gu, Xiaofei Gu, Yu Gu, Mengyun Guan, Yuduo Guan, Nunzio Guardone, Rosa Maria Guizzetti, Cong Guo, Jingyuan Guo, Wanlei Guo, Yuhang Guo, Paul Hackspacher, Caren Hagner, Hechong Han, Ran Han, Xiao Han, Yang Han, Ziguo Han, Chuanhui Hao, Jiajun Hao, Vidhya Thara Hariharan, JinCheng He, Jinhong He, Miao He, Mingtao He, Wei He, Xinhai He, Ziou He, Tobias Heinz, Dominikus Hellgartner, Patrick Hellmuth, Shilin Heng, Yuekun Heng, Rafael Herrera, Daojin Hong, YuenKeung Hor, Shaojing Hou, Zhilong Hou, Fatima Houria, Yee Hsiung, Bei-Zhen Hu, Hang Hu, Hao Hu, Jianrun Hu, Jun Hu, Peng Hu, Tao Hu, Wei Hu, Yuxiang Hu, Zhuojun Hu, Chunhao Huang, Guihong Huang, Hanyu Huang, Hexiang Huang, Jinhao Huang, Junlin Huang, Junting Huang, Kairui Huang, Kaixuan Huang, Qinhua Huang, Shengheng Huang, Tao Huang, Wenhao Huang, Xiaozhong Huang, Xin Huang, Xingtao Huang, Yongbo Huang, Lian-Chen Huang, Jiaqi Hui, Lei Huo, Cédric Huss, Safeer Hussain, Leonard Imbert, Antonio Insolia, Ara Ioannisian, Daniel Ioannisyan, Ammad Ul Islam, Roberto Isocrate, Adrienne Jacobi, Arshak Jafar, Beatrice Jelmini, Kuo-Lun Jen, Soeren Jetter, Xiangpan Ji, Xiaolu Ji, Xingzhao Ji, Huihui Jia, Junji Jia, Yi Jia, Cailian Jiang, Chengbo Jiang, Guangzheng Jiang, Junjie Jiang, Wei Jiang, Xiaoshan Jiang, Xiaozhao Jiang, Yijian Jiang, Yixuan Jiang, Yue Jiang, Ruyi Jin, Shuzhu Jin, Xiaoping Jing, Cécile Jollet, Liam Jones, Jari Joutsenvaara, Sirichok Jungthawan, Philipp Kampmann, Markus Kaiser, Leonidas Kalousis, Bowen Kan, Li Kang, Michael Karagounis, Rebin Karaparambil, Matej Karas, Narine Kazarian, Ali Khan, Amir Khan, Amina Khatun, Khanchai Khosonthongkee, Florian Kiel, Patrick Kinz, Denis Korablev, Konstantin Kouzakov, Alexey Krasnoperov, Zinovy Krumshteyn, Andre Kruth, Tim Kuhlbusch, Sergey Kuleshov, Sindhujha Kumaran, Chun-Hao Kuo, Nikolay Kutovskiy, Pasi Kuusiniemi, Loïc Labit, Tobias Lachenmaier, Haojing Lai, ToUyen LamThi, Philipp Landgraf, Cecilia Landini, Lorenzo Lastrucci, Fedor Lazarev, Sébastien Leblanc, Victor Lebrin, Matthieu Lecocq, Priscilla Lecomte, Frederic Lefevre, Liping Lei, Ruiting Lei, Xiangcui Lei, Rupert Leitner, Petr Lenskii, Jason Leung, Bo Li, Chao Li, Daozheng Li, Demin Li, Dian Li, Fei Li, Fule Li, Gaoshuang Li, Gaosong Li, Haitao Li, Hongjian Li, Huang Li, Huiling Li, Jiajun Li, Jiaqi Li, Jin Li, Kaijie Li, Meiou Li, Mengzhao Li, Min Li, Nan Li, Qingjiang Li, Quanlin Li, Ruhui Li, Rui Li, Shanfeng Li, Shuaijie Li, Shuo Li, Tao Li, Teng Li, Weidong Li, Weiguo Li, Xiaomei Li, Xiaonan Li, Xiwen Li, Xinying Li, Yang Li, Yawen Li, Yi Li, Yichen Li, Yifan Li, Yingke Li, Yuanxia Li, Yufeng Li, Zepeng Li, Zhaohan Li, Zhibing Li, Zhiwei Li, Zi-Ming Li, Ziyuan Li, Zonghai Li, An-An Liang, Jing Liang, Jingjing Liang, Jiajun Liao, Minghua Liao, Yilin Liao, Yuzhong Liao, Daniel Liebau, Ayut Limphirat, Sukit Limpijumnong, Bo-Chun Lin, Guey-Lin Lin, Jiming Lin, Shengxin Lin, Tao Lin, Xianhao Lin, Xingyi Lin, Yen-Hsun Lin, Jiacheng Ling, Jiajie Ling, Xin Ling, Ivano Lippi, Caimei Liu, Fang Liu, Fengcheng Liu, Gang Liu, Haidong Liu, Haotian Liu, Hongbang Liu, Hongjuan Liu, Hongtao Liu, Hongyang Liu, Hu Liu, Hui Liu, Jianglai Liu, Jianli Liu, Jiaxi Liu, Jin'gao Liu, Jinchang Liu, Jinyan Liu, Kainan Liu, Lei Liu, Libing Liu, Mengchao Liu, Menglan Liu, Min Liu, Qishan Liu, Qian Liu, Qin Liu, Runxuan Liu, Shenghui Liu, Shuangyu Liu, Shubing Liu, Shulin Liu, Wanjin Liu, Xiaowei Liu, Ximing Liu, Xinkang Liu, Xiwen Liu, Xuewei Liu, Yan Liu, Yankai Liu, Yin Liu, Yiqi Liu, Yuanyuan Liu, Yuexiang Liu, Yunzhe Liu, Zhen Liu, Zhipeng Liu, Zhuo Liu, Domenico Lo Presti, Salvatore Loffredo, Lorenzo Loi, Paolo Lombardi, Claudio Lombardo, Yongbing Long, Andrea Longhin, Fabio Longhitano, Kai Loo, Sebastian Lorenz, Selma Conforti Di Lorenzo, Chuan Lu, Fan Lu, Haoqi Lu, Jiashu Lu, Junguang Lu, Meishu Lu, Peizhi Lu, Shuxiang Lu, Xianghui Lu, Xianguo Lu, Xiaoxu Lu, Xiaoying Lu, Bayarto Lubsandorzhiev, Sultim Lubsandorzhiev, Livia Ludhova, Arslan Lukanov, Daibin Luo, Fengjiao Luo, Guang Luo, Jianyi Luo, Pengwei Luo, Shu Luo, Tao Luo, Wuming Luo, Xiaojie Luo, Xiaolan Luo, Zhipeng Lv, Vladimir Lyashuk, Bangzheng Ma, Bing Ma, Na Ma, Qiumei Ma, Si Ma, Wing Yan Ma, Xiaoyan Ma, Xubo Ma, Santi Maensiri, Jingyu Mai, Romain Maisonobe, Marco Malabarba, Yury Malyshkin, Roberto Carlos Mandujano, Fabio Mantovani, Xin Mao, Yajun Mao, Stefano M. Mari, Cristina Martellini, Agnese Martini, Jacques Martino, Johann Martyn, Matthias Mayer, Davit Mayilyan, Worawat Meevasana, Artur Meinusch, Qingru Meng, Yue Meng, Anita Meraviglia, Anselmo Meregaglia, Emanuela Meroni, David Meyhöfer, Jian Min, Lino Miramonti, Nikhil Mohan, Salvatore Monforte, Paolo Montini, Michele Montuschi, Nikolay Morozov, Iwan Morton-Blake, Xiangyi Mu, Pavithra Muralidharan, Lakshmi Murgod, Axel Müller, Thomas Mueller, Massimiliano Nastasi, Dmitry V. Naumov, Elena Naumova, Diana Navas-Nicolas, Igor Nemchenok, Elisabeth Neuerburg, VanHung Nguyen, Dennis Nielinger, Alexey Nikolaev, Feipeng Ning, Zhe Ning, Yujie Niu, Stepan Novikov, Hiroshi Nunokawa, Lothar Oberauer, Juan Pedro Ochoa-Ricoux, Samantha Cantu Olea, Sebastian Olivares, Alexander Olshevskiy, Domizia Orestano, Fausto Ortica, Rainer Othegraven, Yifei Pan, Alessandro Paoloni, Nina Parkalian, George Parker, Sergio Parmeggiano, Achilleas Patsias, Teerapat Payupol, Viktor Pec, Davide Pedretti, Yatian Pei, Luca Pelicci, Nicomede Pelliccia, Anguo Peng, Haiping Peng, Yu Peng, Zhaoyuan Peng, Elisa Percalli, Willy Perrin, Frédéric Perrot, Pierre-Alexandre Petitjean, Fabrizio Petrucci, Min Pi, Oliver Pilarczyk, Ruben Pompilio, Artyom Popov, Pascal Poussot, Stefano Pozzi, Wathan Pratumwan, Ezio Previtali, Sabrina Prummer, Fazhi Qi, Ming Qi, Xiaohui Qi, Sen Qian, Xiaohui Qian, Zhen Qian, Liqing Qin, Zhonghua Qin, Shoukang Qiu, Manhao Qu, Zhenning Qu, Muhammad Usman Rajput, Shivani Ramachandran, Gioacchino Ranucci, Neill Raper, Reem Rasheed, Thomas Raymond, Alessandra Re, Henning Rebber, Abdel Rebii, Bin Ren, Shanjun Ren, Yuhan Ren, Andrea Rendina, Cristobal Morales Reveco, Taras Rezinko, Barbara Ricci, Luis Felipe Piñeres Rico, Mariam Rifai, Markus Robens, Mathieu Roche, Narongkiat Rodphai, Fernanda de Faria Rodrigues, Lars Rohwer, Aldo Romani, Vincent Rompel, Bedřich Roskovec, Xiangdong Ruan, Xichao Ruan, Peter Rudakov, Saroj Rujirawat, Arseniy Rybnikov, Andrey Sadovsky, Sahar Safari, Giuseppe Salamanna, Deshan Sandanayake, Simone Sanfilippo, Anut Sangka, Nuanwan Sanguansak, Ujwal Santhosh, Giuseppe Sava, Utane Sawangwit, Julia Sawatzki, Michaela Schever, Jacky Schuler, Cédric Schwab, Konstantin Schweizer, Dmitry Selivanov, Alexandr Selyunin, Andrea Serafini, Giulio Settanta, Mariangela Settimo, Muhammad Ikram Shahzad, Yanjun Shan, Junyu Shao, Zhuang Shao, Anurag Sharma, Vladislav Sharov, Arina Shaydurova, Wei Shen, Gang Shi, Hangyu Shi, Hexi Shi, Jingyan Shi, Yanan Shi, Yongjiu Shi, Yuan Shi, Dmitrii Shpotya, Yike Shu, Yuhan Shu, She Shuai, Vitaly Shutov, Andrey Sidorenkov, Randhir Singh, Apeksha Singhal, Chiara Sirignano, Jaruchit Siripak, Monica Sisti, Maciej Slupecki, Mikhail Smirnov, Oleg Smirnov, Thiago Sogo-Bezerra, Michael Soiron, Sergey Sokolov, Julanan Songwadhana, Boonrucksar Soonthornthum, Felix Sorgenfrei, Albert Sotnikov, Mario Spinetti, Warintorn Sreethawong, Achim Stahl, Luca Stanco, Korbinian Stangler, Konstantin Stankevich, Hans Steiger, Jochen Steinmann, Malte Stender, Tobias Sterr, David Stipp, Matthias Raphael Stock, Virginia Strati, Mikhail Strizh, Alexander Studenikin, Katharina von Sturm, Aoqi Su, Jun Su, Yuning Su, Gongxing Sun, Guangbao Sun, Hansheng Sun, Lijun Sun, Liting Sun, Mingxia Sun, Shifeng Sun, Xilei Sun, Yongzhao Sun, Yunhua Sun, Yuning Sun, Zhanxue Sun, Zhengyang Sun, Narumon Suwonjandee, Troy Swift, Michal Szelezniak, Dušan Štefánik, Fedor Šimkovic, Ondřej Šrámek, Dmitriy Taichenachev, Christophe De La Taille, Akira Takenaka, Hongnan Tan, Xiaohan Tan, Haozhong Tang, Jian Tang, Jingzhe Tang, Qiang Tang, Quan Tang, Xiao Tang, Jihua Tao, Eric Theisen, Minh Thuan Nguyen Thi, Renju Tian, Yuxin Tian, Alexander Tietzsch, Igor Tkachev, Tomas Tmej, Marco Danilo Claudio Torri, Francesco Tortorici, Konstantin Treskov, Andrea Triossi, Wladyslaw Trzaska, Andrei Tsaregorodtsev, Alexandros Tsagkarakis, Yu-Chen Tung, Cristina Tuve, Nikita Ushakov, Guillaume Vanroyen, Nikolaos Vassilopoulos, Carlo Venettacci, Giuseppe Verde, Maxim Vialkov, Benoit Viaud, Cornelius Moritz Vollbrecht, Vit Vorobel, Dmitriy Voronin, Lucia Votano, Stefan van Waasen, Stefan Wagner, Pablo Walker, Jiawei Wan, Andong Wang, Caishen Wang, Chung-Hsiang Wang, Cui Wang, Derun Wang, En Wang, Guoli Wang, Hanwen Wang, Hongxin Wang, Huanling Wang, Jiabin Wang, Jian Wang, Jun Wang, Ke Wang, Kunyu Wang, Lan Wang, Li Wang, Lu Wang, Meifen Wang, Meng Wang, Meng Wang, Mingyuan Wang, Qianchuan Wang, Ruiguang Wang, Sibo Wang, Siguang Wang, Tianhong Wang, Tong Wang, Wei Wang, Wei Wang, Wenshuai Wang, Wenwen Wang, Wenyuan Wang, Xi Wang, Xiangyue Wang, Xuesen Wang, Yangfu Wang, Yaoguang Wang, Yi Wang, Yi Wang, Yi Wang, Yifang Wang, Yuanqing Wang, Yuman Wang, Yuyi Wang, Zhe Wang, Zheng Wang, Zhigang Wang, Zhimin Wang, Zongyi Wang, Apimook Watcharangkool, Junya Wei, Jushang Wei, Lianghong Wei, Wei Wei, Wei Wei, Wenlu Wei, Yadong Wei, Yuehuan Wei, Zhengbao Wei, Marcel Weifels, Kaile Wen, Liangjian Wen, Yijie Wen, Jun Weng, Christopher Wiebusch, Rosmarie Wirth, Steven Chan-Fai Wong, Bjoern Wonsak, Baona Wu, Bi Wu, Chengxin Wu, Chia-Hao Wu, Chin-Wei Wu, Diru Wu, Fangliang Wu, Jianhua Wu, Qun Wu, Shuai Wu, Wenjie Wu, Yinhui Wu, Yiyang Wu, Zhaoxiang Wu, Zhi Wu, Zhongyi Wu, Michael Wurm, Jacques Wurtz, Christian Wysotzki, Yufei Xi, Jingkai Xia, Shishen Xian, Ziqian Xiang, Fei Xiao, Pengfei Xiao, Tianying Xiao, Xiang Xiao, Wan Xie, Wei-Jun Xie, Xiaochuan Xie, Yijun Xie, Yuguang Xie, Zhangquan Xie, Zhao Xin, Zhizhong Xing, Benda Xu, Cheng Xu, Chuang Xu, Donglian Xu, Fanrong Xu, Hangkun Xu, Jiayang Xu, Jie Xu, Jilei Xu, Jinghuan Xu, Lingyu Xu, Meihang Xu, Shiwen Xu, Xunjie Xu, Ya Xu, Yin Xu, Yu Xu, Dongyang Xue, Jingqin Xue, Baojun Yan, Liangping Yan, Qiyu Yan, Taylor Yan, Tian Yan, Wenqi Yan, Xiongbo Yan, Yupeng Yan, Anbo Yang, Caihong Yang, Changgen Yang, Chengfeng Yang, Dikun Yang, Dingyong Yang, Fengfan Yang, Haibo Yang, Huan Yang, Jie Yang, Jize Yang, Kaiwei Yang, Lei Yang, Mengting Yang, Pengfei Yang, Xiaoyu Yang, Xuhui Yang, Yi Yang, Yichen Yang, Yifan Yang, Yixiang Yang, Yujiao Yang, Yuzhen Yang, Zekun Yang, Haifeng Yao, Li Yao, Jiaxuan Ye, Mei Ye, Xingchen Ye, Ziping Ye, Ugur Yegin, Frédéric Yermia, Peihuai Yi, Rattikorn Yimnirun, Jilong Yin, Na Yin, Weiqing Yin, Xiangwei Yin, Xiaohao Yin, Sivaram Yogathasan, Zhengyun You, Boxiang Yu, Chiye Yu, Chunxu Yu, Guangyou Yu, Guojun Yu, Hongzhao Yu, Miao Yu, Peidong Yu, Simi Yu, Xianghui Yu, Yang Yu, Zeyuan Yu, Zezhong Yu, Cenxi Yuan, Chengzhuo Yuan, Zhaoyang Yuan, Zhenxiong Yuan, Ziyi Yuan, Baobiao Yue, Noman Zafar, André Zambanini, Jilberto Zamora, Matteo Zanetti, Vitalii Zavadskyi, Fanrui Zeng, Pan Zeng, Shan Zeng, Tingxuan Zeng, Yuda Zeng, Yujie Zeng, Liang Zhan, Aiqiang Zhang, Bin Zhang, Binting Zhang, Chengcai Zhang, Enze Zhang, Feiyang Zhang, Guoqing Zhang, Haiqiong Zhang, Han Zhang, Hangchang Zhang, Haosen Zhang, Honghao Zhang, Hongmei Zhang, Jialiang Zhang, Jiawen Zhang, Jie Zhang, Jin Zhang, Jingbo Zhang, Jinnan Zhang, Junwei Zhang, Kun Zhang, Lei Zhang, Mingxuan Zhang, Mohan Zhang, Peng Zhang, Ping Zhang, Qingmin Zhang, Rongping Zhang, Rui Zhang, Shaoping Zhang, Shiqi Zhang, Shu Zhang, Shuihan Zhang, Siyuan Zhang, Tao Zhang, Xiaofeng Zhang, Xiaomei Zhang, Xin Zhang, Xu Zhang, Xuan Zhang, Xuantong Zhang, Xuesong Zhang, Xueyao Zhang, Yan Zhang, Yibing Zhang, Yinhong Zhang, Yiyu Zhang, Yizhou Zhang, Yongjie Zhang, Yongpeng Zhang, Yu Zhang, Yuanyuan Zhang, Yue Zhang, Yueyuan Zhang, Yumei Zhang, Yuning Zhang, Zhenyu Zhang, Zhicheng Zhang, Zhijian Zhang, Zhixuan Zhang, Baoqi Zhao, Fengyi Zhao, Jie Zhao, Liang Zhao, Mei Zhao, Rong Zhao, Runze Zhao, Shujun Zhao, Tianchi Zhao, Tianhao Zhao, Yubin Zhao, Dongqin Zheng, Hua Zheng, Xiangyu Zheng, Yangheng Zheng, Weili Zhong, Weirong Zhong, Albert Zhou, Guorong Zhou, Li Zhou, Lishui Zhou, Min Zhou, Shun Zhou, Tong Zhou, Xiang Zhou, Xing Zhou, Yan Zhou, Haiwen Zhu, Jiang Zhu, Jingsen Zhu, Jingyu Zhu, Kangfu Zhu, Kejun Zhu, Zhihang Zhu, Ivan Zhutikov, Bo Zhuang, Honglin Zhuang, Liang Zong, Jiaheng Zou, Sebastian Zwickel, Jan Züfle