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The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

Wide-bandgap mixed-halide perovskite photovoltaic modules show strong potential for portable chargers, building-integrated photovoltaics, agrivoltaics, and tandem systems, but large-area processing exacerbates crystallization heterogeneity, surface defects, and halide phase segregation. Conventional spin-coating passivation fails to deliver uniform interfacial control at scale. Here, an industrially inspired solution-soaking quenching technique is introduced, in which hot blade-coated wide-bandgap perovskite films ( ~ 30 cm 2 ) are immersed in cold SrI 2 /isopropanol. It enables rapid surface reconstruction and uniform surface passivation, enhances photoluminescence uniformity, improves crystallinity, reduces roughness, and stabilizes halides via gradient Sr 2+ incorporation. These effects mitigate tensile stress, optimize energy-level alignment, and suppress light-induced phase separation. Methylammonium-free wide-bandgap small-area (0.04 cm 2 ) devices achieve efficiencies up to 22.03%, while a 10.13 cm 2 module delivers 20.32% efficiency with excellent operational stability. The method is versatile across wide-bandgap perovskite compositions and enables practical applications including portable chargers, semitransparent modules (18.41% bifacial equivalent efficiency), and >27% efficient all-perovskite tandem windows. Fang et al. report a scalable solution-soaking quenching technique to enable uniform passivation of large-area wide-bandgap perovskite films, resulting in 10.13 cm 2 solar modules with 20.32% efficiency, prolonged lifespan, and applications in tandem solar windows, transparent agrivoltaics, and portable power systems.

Spinal cord injury is a disease with no complete cure. It typically results in loss of motor and sensory functions below the level of the lesion, leading to significant physical and psychological disorders. After spinal cord injury, the microenvironmental balance at the site of injury is disrupted, creating complex conditions that are not conducive to nerve regeneration and recovery of spinal cord function, such as hemorrhage and ischemia, glial scar formation, and nerve demyelination. As our knowledge of the spinal cord microenvironment has expanded, researchers have determined that therapeutic strategies that modulate the microenvironment represent a significant and efficacious avenue of investigation. This review summarizes the characteristics of microenvironmental changes at various stages after spinal cord injury. Additionally, it discusses novel strategies developed recently based on regulating the microenvironment after spinal cord injury to promote recovery. The investigation of a long-acting, targeted, and multitemporal combination therapy strategy to repair spinal cord injuries shows promise in terms of its developmental potential. Graphical Abstract A schematic overview of changes of microenvironment components after SCI and strategies for reconstructing microenvironment. NISCI Non-traumatic spinal cord injury, SCI Spinal cord injury TSCI Traumatic spinal cord injury. Created with biorender.com

This dissertation presents the first Deep-Neural-Network–based missing transverse momentum (pTmiss) estimator, called “DeepMET”. It utilizes all reconstructed particles in an event as input, and assigns an individual weight to each of them. The DeepMET estimator is the negative of the vector sum of the weighted transverse momenta of all input particles. Compared with the pTmiss estimators currently utilized by the CMS Collaboration, DeepMET is found to improve the pTmiss resolution by 10-20%, and is more resilient towards the effect of additional proton-proton interactions accompanying the interaction of interest. DeepMET is demonstrated to improve the resolution on the recoil measurement of the W boson and reduce the systematic uncertainties on the W mass measurement by a large fraction compared with other pTmiss estimators.

This paper presents performance measurements of a new readout electronics system based on silicon photomultipliers for the PHENIX electromagnetic calorimeter. Installation of the lead-scintillator Shashlik style calorimeter into the SeaQuest/SpinQuest spectrometer has been proposed to broaden the experiment's dark sector search program, an upgrade known as DarkQuest. The calorimeter and electronics system were subjected to testing and calibration at the Fermilab Test Beam Facility. Detailed studies of the energy response and resolution, as well as particle identification capabilities, were performed. The background rate in the actual experimental environment was also examined. The system is found to be well-suited for a dark sector search program on the Fermilab 120 GeV proton beamline.

The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton-proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the ground truth information from simulation and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

Results on the effects of ionizing radiation on the signal produced by plastic scintillating rods manufactured by Eljen Technology company are presented for various matrix materials, dopant concentrations, fluors (EJ-200 and EJ-260), anti-oxidant concentrations, scintillator thickness, doses, and dose rates. The light output before and after irradiation is measured using an alpha source and a photomultiplier tube, and the light transmission by a spectrophotometer. Assuming an exponential decrease in the light output with dose, the change in light output is quantified using the exponential dose constant \\(D\\). The \\(D\\) values are similar for primary and secondary doping concentrations of 1 and 2 times, and for antioxidant concentrations of 0, 1, and 2 times, the default manufacturer's concentration. The \\(D\\) value depends approximately linearly on the logarithm of the dose rate for dose rates between 2.2 Gy/hr and 70 Gy/hr for all materials. For EJ-200 polyvinyltoluene-based (PVT) scintillator, the dose constant is approximately linear in the logarithm of the dose rate up to 3400 Gy/hr, while for polystyrene-based (PS) scintillator or for both materials with EJ-260 fluors, it remains constant or decreases (depending on doping concentration) above about 100 Gy/hr. The results from rods of varying thickness and from the different fluors suggest damage to the initial light output is a larger effect than color center formation for scintillator thickness \\(\\leq1\\) cm. For the blue scintillator (EJ-200), the transmission measurements indicate damage to the fluors. We also find that while PVT is more resistant to radiation damage than PS at dose rates higher than about 100 Gy/hr for EJ-200 fluors, they show similar damage at lower dose rates and for EJ-260 fluors.

In many real-world applications, graph-structured data used for training and testing have differences in distribution, such as in high energy physics (HEP) where simulation data used for training may not match real experiments. Graph domain adaptation (GDA) is a method used to address these differences. However, current GDA primarily works by aligning the distributions of node representations output by a single graph neural network encoder shared across the training and testing domains, which may often yield sub-optimal solutions. This work examines different impacts of distribution shifts caused by either graph structure or node attributes and identifies a new type of shift, named conditional structure shift (CSS), which current GDA approaches are provably sub-optimal to deal with. A novel approach, called structural reweighting (StruRW), is proposed to address this issue and is tested on synthetic graphs, four benchmark datasets, and a new application in HEP. StruRW has shown significant performance improvement over the baselines in the settings with large graph structure shifts, and reasonable performance improvement when node attribute shift dominates.

Optimizing charged-particle track reconstruction algorithms is crucial for efficient event reconstruction in Large Hadron Collider (LHC) experiments due to their significant computational demands. Existing track reconstruction algorithms have been adapted to run on massively parallel coprocessors, such as graphics processing units (GPUs), to reduce processing time. Nevertheless, challenges remain in fully harnessing the computational capacity of coprocessors in a scalable and non-disruptive manner. This paper proposes an inference-as-a-service approach for particle tracking in high energy physics experiments. To evaluate the efficacy of this approach, two distinct tracking algorithms are tested: Patatrack, a rule-based algorithm, and Exa\\(.\\)TrkX, a machine learning-based algorithm. The as-a-service implementations show enhanced GPU utilization and can process requests from multiple CPU cores concurrently without increasing per-request latency. The impact of data transfer is minimal and insignificant compared to running on local coprocessors. This approach greatly improves the computational efficiency of charged particle tracking, providing a solution to the computing challenges anticipated in the High-Luminosity LHC era.