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At least 235 records · Page 13

The Mechanism of Electron Injection and Acceleration in Transrelativistic Reconnection

Electron acceleration during magnetic reconnection is thought to play a key role in time-variable high-energy emission from astrophysical systems. By means of particle-in-cell simulations of transrelativistic reconnection, we investigate electron injection and acceleration mechanisms in low-β electron–proton plasmas. We set up a diversity of density and field structures (e.g., X-points and plasmoids) by varying the guide field strength and choosing whether to trigger reconnection or let it spontaneously evolve. We show that the number of X-points and plasmoids controls the efficiency of electron acceleration, with more X-points leading to a higher efficiency. Using on-the-fly acceleration diagnostics, we also show that the nonideal electric fields associated with X-points play a critical role in the first stages of electron acceleration. As a further diagnostic, we include two populations of test particles that selectively experience only certain components of electric fields. We find that the out-of-plane component of the parallel electric field determines the hardness of the high-energy tail of the electron energy distribution. These results further our understanding of electron acceleration in this regime of magnetic reconnection and have implications for realistic models of black hole accretion flows.

79 ASTRONOMY AND ASTROPHYSICS↗

Operational experience with the new CMS DAQ-Expert

The data acquisition (DAQ) system of the Compact Muon Solenoid (CMS) at CERN reads out the detector at the level-1 trigger accept rate of 100 kHz, assembles events with a bandwidth of 200 GB/s, provides these events to the high level-trigger running on a farm of about 30k cores and records the accepted events. Comprising custom-built and cutting edge commercial hardware and several 1000 instances of software applications, the DAQ system is complex in itself and failures cannot be completely excluded. Moreover, problems in the readout of the detectors,in the first level trigger system or in the high level trigger may provoke anomalous behaviour of the DAQ systemwhich sometimes cannot easily be differentiated from a problem in the DAQ system itself. In order to achieve high data taking efficiency with operators from the entire collaboration and without relying too heavily on the on-call experts, an expert system, the DAQ-Expert, has been developed that can pinpoint the source of most failures and give advice to the shift crew on how to recover in the quickest way. The DAQ-Expert constantly analyzes monitoring data from the DAQ system and the high level trigger by making use of logic modules written in Java that encapsulate the expert knowledge about potential operational problems. The results of the reasoning are presented to the operator in a web-based dashboard, may trigger sound alerts in the control room and are archived for post-mortem analysis - presented in a web-based timeline browser. We present the design of the DAQ-Expert and report on the operational experience since 2017, when it was first put into production.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Trigger-based Incremental Data Processing with Unified Sync and Async Model

In recent years, more and more applications in the cloud have needs to process large-scale on-line datasets, which evolve over time as new entries are added and existing entries are modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient processing with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications' developers, which further significantly restricts their usabilities. In this study, we propose a trigger-based incremental computing framework in the cloud, called Domino, with both synchronous and asynchronous mechanisms to coordinate parallel triggers. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and extensive evaluation results confirm that it can deliver sufficient performance, and also is easy to use for incremental applications in large-scale distributed computing.

97 MATHEMATICS AND COMPUTING↗

A machine learning based approach to online electron reconstruction at CLAS12

Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a particle passing through several detector components to identify the particle’s type. Of particular interest to electro-production Nuclear Physics experiments such as CLAS12 is electron identification which is used to trigger data recording. A machine learning approach was developed for CLAS12 to reconstruct and identify electrons by combining raw signals at the data acquisition level from several detector components. Here, this approach achieves an electron identification purity above 75% whilst retaining an efficiency close to 100%. The machine learning tools are capable of running at high rates exceeding the data acquisition rates and will allow electron reconstruction in real-time. This work enhances online analyses and monitoring and can contribute to improved triggering at CLAS12. This machine learning driven approach will also be crucial for experiments aiming to transition to streaming readout operations where online reconstruction will be a key component of the data taking paradigm.

Artificial intelligence↗

Study on the characteristics of a two gap capillary discharge

The paper presents a new two-gap capillary (TGC) discharge structure. The prominent innovation is the introduction of the middle electrode, which divides the capillary into the trigger gap and the main gap. The discharge circuit of the TGC comprises the trigger circuit and the main circuit. The two circuits are used for the pre-ionization of the trigger gap and providing energy of 450 J for the main gap arc discharging, respectively. When the discharge initiates, the trigger gap is pre-ionized under high voltage pulse produced by trigger circuit, and meanwhile, the weakly ionized plasma is generated. The main circuit then maintains the expansion of the plasma, which is called soft capillary discharge. Afterwards, the main gap is shorted and discharges under a relatively low voltage. With the optimization of the circuit parameter, both the energy deposition ratio in main gap and the degree of plasma ionization are enhanced. The efficiency of the energy deposition is almost twice higher compared with that of the conventional capillary structure. The life performance test indicates that the erosion of the middle electrode and the trigger gap carbonization are the key factors that limit the life performance of the TGC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Real-Time Anomaly Detection for Searches Beyond the Standard Model in the ProtoDUNE Horizontal Drift Detector

This paper summarizes work conducted throughout a SULI internship at Fermi National Accelerator Laboratory focused on building an unsupervised machine learning model for real-time anomaly detection in ProtoDUNE Horizontal Drift. Using simulated data, we trained an autoencoder model on a pure cosmic dataset, and evaluated it on both cosmic and neutrino events—making the model an anomaly detector. The goal was to make a model which matches or exceeds the current ADC Simple Window trigger algorithm so that our model can perform at the same rate but provide sensitivity to potential beyond-the-Standard-Model (BSM) signatures. In the end, we were able to construct a model which slightly exceeds the capabilities of the ADC Simple Window while remaining completely unsupervised, achieving 31.9 ± 0.2% (26.6 ± 0.2%) ν efficiency at 5 Hz (2 Hz), a 3.6 (3.2) percentage point increase. Additionally, 17.5 ± 0.3% (18.3 ± 0.3%) of the events that passed the autoencoder at 5 Hz (2 Hz) were missed by the current trigger algorithm. Future work will investigate alternative normalization methods, including quantile transformation, and evaluate the model on ProtoDUNE-HD detector-glitch data if that data becomes available.

Wilson, C. [Cincinnati U., RWC]↗

Benefits and pathologies associated with the inflammatory response

Adult skeletal muscle regenerates completely after a damage, thanks to the satellite cells, or muscle stem cells (MuSCs), that implement the adult myogenic program. This program is sustained by both robust intrinsic mechanisms and extrinsic cues coming from the close neighborhood of MuSCs during muscle regeneration. Among the various cell types present in the regenerating muscle, immune cells, and particularly macrophages, exert numerous functions and provide sequential transient niches to support the myogenic program. The adequate orchestration of the delivery of these cues ensures efficient muscle regeneration and full functional recovery. The situation is very different in muscular dystrophies where asynchronous and permanent microinjuries occur, triggering contradictory regenerating cues at the same time in a specific area, that lead to chronic inflammation and fibrogenesis. Here we review the beneficial effects that leukocytes, and particularly macrophages, exert on their neighboring cells during skeletal muscle regeneration after an acute injury. Then, the more complicated (and less beneficial) roles of leukocytes during muscular dystrophies are presented. Finally, we discuss how the inflammatory compartment may be a target to improve muscle regeneration in both acute muscle injury and muscle diseases.

60 APPLIED LIFE SCIENCES↗

A reduced model for edge localized mode control by supersonic molecular beam injection and pellet injection

We develop a diffusive, bistable, tri-unstable cellular automata (CA) model to study the dynamics of H-mode pedestal with edge localized modes (ELMs) and their control by supersonic molecular beam injection (SMBI) and pellet injection (PI). It is shown that the new CA model can reproduce the key features of H-mode pedestals with various types of ELM, including Type-I ELM. SMBI and PI are modeled as additional grain injections into pedestal with varying degrees of injected materials and profiles. It is found that H-mode pedestal responds to SMBI differently depending on the baseline fueling. If the baseline fueling is large enough to allow Type-I ELM, SMBI enhances large transport avalanches caused by ballooning instabilities. These avalanches prevent the total pedestal current from reaching the boundary for peeling instability. On the other hand, if the baseline fueling is low to avoid Type-I ELM, SMBI enhances small scale avalanches, which prevent the pedestal from growing to profiles globally vulnerable to ballooning instabilities. These imply that SMBI can mitigate different types of ELM by converting them to more benign types. From CA modeling of pellet injection, it is shown that Type-I ELM can be triggered by pellet injection with sufficient strength and depth. Scanning the frequency of pellet injection, it is found that a maximum efficiency of pellet pacing is achieved when the injection frequency is approximately ten times the natural frequency of Type-I ELM.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Discrimination between individual dust and bioparticles using aerosol time-of-flight mass spectrometry

Ice nucleating particles (INPs) impact cloud properties and precipitation processes through their ability to trigger cloud glaciation. Dust and bioparticles are two important sources of INPs that have markedly different atmospheric loadings and ice nucleating efficiencies. In situ identification of the sources of INPs in clouds has been accomplished using single particle mass spectrometry (SPMS). However, external mixtures of dust and bioparticles present a unique challenge as they have overlapping mass spectral ion signatures, complicating their unambiguous identification. This study presents a detailed discussion of dust and bioparticle SPMS signatures, uniting data from a broad array of studies. As emphasized, the ion signals from both dust and bioparticles are highly sensitive to ionization conditions. To understand the observed variations, we characterize the mass spectral dependence of distinct dust and bioparticle samples using total positive ion intensity (TPII) as an indicator of the laser pulse energy each particle encountered. Through this analysis, a broad range of characteristic biogenic low intensity ion peaks that may be useful to distinguish bioparticles from dust became apparent and are highlighted. Insights informed by this analysis were utilized to identify bioparticles in ambient SPMS data. Ambient particles exhibiting both dust and characteristic biogenic spectral fingerprints were excluded from the bioparticle classification. Although bioparticles only made up 0.2% of all sampled particles, their abundance was moderately correlated with INP concentrations measured at 15 °C.

54 ENVIRONMENTAL SCIENCES↗

Expansion of the Fast Neutron Hodoscope at TREAT to Support Fuel Safety Experiments

In 2024 the Fuel Motion Monitoring System (FMMS), or Hodoscope, at the Transient Reactor Test Facility, is being expanded from 96 viewing channels to 192 viewing channels, effectively doubling the FMMS field of view. Here, this increase in capability will allow the FMMS to support larger scale fuel tests, encompassing height-of-core test devices, test devices with multi-pin fuel assemblies, and test devices with recirculating coolant flow. Work supporting the expansion included refurbishing 96 additional proton recoil scintillator (PRS) detectors, doubling the data acquisition system (DAS) installed architecture, improving time synchronization in the DAS, and new research to measure the PRS detector energy-dependent, fast-neutron detection efficiency. In addition, laboratory activities have produced an improved benchtop testing capability for assessing the DAS, time synchronization, and external start triggering, along with an updated capability to scan PRS detectors to develop a preliminary flat-field normalization prior to deployment to TREAT.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

In situ monitoring of the stopped muon flux at Mu2e

The specific object of Mu2e search is the neutrino-less coherent $\mu^-\rightarrow e^-$ conversion in the field of an aluminum nucleus and the signal is a monoenergetic electron of energy $ \approx 104.97$ MeV. This process is forbidden in the Standard Model but allowed in many of its extensions: with minimal changes to include neutrino masses and oscillation, the Branching Ratio of this process, or the similarly interesting $\mu\rightarrow e \gamma$ decay, is expected to be of the order of $\mathcal{O}(10^{-54})$. Values like these are below any currently achievable experimental sensitivity, and the observation of a signal would be unambiguous evidence of New Physics. The upper limit on the muon conversion was set by SINDRUM II at $7\times10^{-13}$ (90\% C.L.) and the goal of the Mu2e collaboration is an improvement of 4 order of magnitudes.\\ The Mu2e experiment can be conceptually divided into three stages: interaction of the primary proton pulse with the tungsten target a nd production of $\pi$ and $\mu$; collection and transport of the produced particles down to the aluminum stopping target; interaction with the stopping target and measurement of the output particles. The measurement of the stopped muon flux is of cardinal importance for the normalization of the Mu2e results. The baseline design includes two detectors developed to measure this flux, counting $\gamma$ emitted by the muonic atoms: HPGe and LaBr$_3$(Ce). The studies performed by the Mu2e Collaboration show that these two systems can be reliably used to determine the overall normalization.\\ The number of stopped muons is proportional to the number of protons on target, which itself depends on the extraction system for the proton beam. The Mu2e beam delivery system will use a resonant extraction to create the proton pulses and this method is characterized by intensity fluctuations on the time scale of milliseconds. These fluctuations, aside from the proportional fluctuation of t he number of muons, have a non-trivial impact on the overall! performance of the apparatus. For example, higher intensity proton bunches translate into higher veto rates by the Cosmic Rays Veto system, which is triggered by secondary particles generated by the proton interaction. Another example is the effect of the instantaneous luminosity on the reconstruction efficiency: higher intensity translates into higher detector occupancy, which in turn reduces the reconstruction efficiency.\\ The two cited detectors encounter limitations when trying to monitor the flux at the millisecond timescales: HPGe is by construction a slow detector while the rate of emission of the $\gamma (1809\textrm{ keV})$ the LaBr$_3$(Ce) will measure is too low. As of today, no system allows to monitor these fluctuations and the goal of this Thesis is to try filling this gap. The method we developed to monitor the fluctuations of the stopped muon flux, and by extension, the fluctuation in the proton pulses intensity is based on counting the number of muons capt ured in the stopping target by counting the number of protons produced in the process of the nuclear muon capture. This is one of the possible processes a stopped muon can undergo and it can lead to the ejection of charged particles.\\ The reconstruction algorithms were developed by the Mu2e collaboration with the explicit (and sometimes implicit) aim to reconstruct tracks of electrons in an energy range of a conversion electron. The protons we are interested in behave quite differently from these electrons and the cardinal task of this study has been to tailor the reconstruction routines to these particles. This has been done by analyzing simulated events consisting of a single proton to optimize the procedure and understand the features of these tracks. A major difference with respect to the electrons is the non-relativistic nature of $100\div300$ MeV$/c$ protons: accounting for the low velocity is both a challenge and a unique signature of the tracks.\\ The use of a pulse d beam and the need to allow produced $\pi$ to decay and $\m! u$ to int! eract in the stopping target forces the definition of a Mu2e \textit{event}: an event is comprised by everything which happens in a $1.7\ \mu$s window between the incidence two consecutive proton pulses. This means that the reconstruction will be performed in an environment crowded by many different particles. The next step of this study has been to estimate the performance of the method in a fully-fledged Mu2e simulated event and to study what kind of selections would improve it.\\ Our results are satisfactory since the number of reconstructed protons is significant (few per event), and show that a monitor on the timescale of milliseconds is indeed possible.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Magnetogenesis in a Collisionless Plasma: From Weibel Instability to Turbulent Dynamo

We report on a first-principles numerical and theoretical study of plasma dynamo in a fully kinetic framework. By applying an external mechanical force to an initially unmagnetized plasma, we develop a self-consistent treatment of the generation of “seed” magnetic fields, the formation of turbulence, and the inductive amplification of fields by the fluctuation dynamo. Driven large-scale motions in an unmagnetized, weakly collisional plasma are subject to strong phase mixing, which leads to the development of thermal pressure anisotropy. This anisotropy triggers the Weibel instability, which produces filamentary “seed” magnetic fields on plasma-kinetic scales. The plasma is thereby magnetized, enabling efficient stretching and folding of the fields by the plasma motions and the development of Larmor-scale kinetic instabilities such as the firehose and mirror. The scattering of particles off the associated microscale magnetic fluctuations provides an effective viscosity, regulating the field morphology and turbulence. During this process, the seed field is further amplified by the fluctuation dynamo until energy equipartition with the turbulent flow is reached. By demonstrating that equipartition magnetic fields can be generated from an initially unmagnetized plasma through large-scale turbulent flows, this work has important implications for the origin and amplification of magnetic fields in the intracluster and intergalactic mediums.

79 ASTRONOMY AND ASTROPHYSICS↗

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗

Real-Time Anomaly Detection for Beyond Standard Model Searches in ProtoDUNE Horizontal Drift

This paper summarizes work conducted throughout a SULI internship at Fermi National Accelerator Laboratory focused on building an unsupervised machine learning model for real-time anomaly detection in ProtoDUNE Horizontal Drift. Using simulated data, we trained an autoencoder model on a pure cosmic dataset, and evaluated it on both cosmic and neutrino events---making the model an anomaly detector. The goal was to make a model which matches or exceeds the current ADC Simple Window trigger algorithm so that our model can perform at the same rate but provide sensitivity to potential beyond-the-Standard-Model (BSM) signatures. In the end, we were able to construct a model which slightly exceeds the capabilities of the ADC Simple Window while remaining completely unsupervised, achieving $31.9 \pm 0.2$\% ($26.6 \pm 0.2$\%) $\nu$ efficiency at 5 Hz (2 Hz), a 3.6 (3.2) percentage point increase. Additionally, $17.5 \pm 0.3$\% ($18.3 \pm 0.3$\%) of the events that passed the autoencoder at 5 Hz (2 Hz) were missed by the current trigger algorithm. Future work will investigate alternative normalization methods, including quantile transformation, and evaluate the model on ProtoDUNE-HD detector-glitch data if that data becomes available.

Wilson, Cameron C. [Cincinnati U., RWC]↗

Real-Time Anomaly Detection for Beyond Standard Model Searches in ProtoDUNE Horizontal Drift

This paper summarizes work conducted throughout a SULI internship at Fermi National Accelerator Laboratory focused on building an unsupervised machine learning model for real-time anomaly detection in ProtoDUNE Horizontal Drift. Using simulated data, we trained an autoencoder model on a pure cosmic dataset, and evaluated it on both cosmic and neutrino events---making the model an anomaly detector. The goal was to make a model which matches or exceeds the current ADC Simple Window trigger algorithm so that our model can perform at the same rate but provide sensitivity to potential beyond-the-Standard-Model (BSM) signatures. In the end, we were able to construct a model which slightly exceeds the capabilities of the ADC Simple Window while remaining completely unsupervised, achieving $31.9 \pm 0.2$\% ($26.6 \pm 0.2$\%) $\nu$ efficiency at 5 Hz (2 Hz), a 3.6 (3.2) percentage point increase. Additionally, $17.5 \pm 0.3$\% ($18.3 \pm 0.3$\%) of the events that passed the autoencoder at 5 Hz (2 Hz) were missed by the current trigger algorithm. Future work will investigate alternative normalization methods, including quantile transformation, and evaluate the model on ProtoDUNE-HD detector-glitch data if that data becomes available.

Wilson, Cameron C. [Cincinnati U., RWC]↗

Unexpectedly Strong Auger Recombination in Halide Perovskites

The emergence of halide perovskites for photovoltaic applications has triggered great interest in these materials for solid-state light emission. Higher order electron–hole recombination processes can critically affect the efficiency of such devices. In the present work, the Auger recombination coefficients are computed in the prototypical halide perovskite, CH 3 NH 3 PbI 3 (MAPbI 3 ), using first-principles calculations. It is demonstrated that Auger recombination is responsible for the exceptionally high third-order recombination coefficient observed in experiment. The large Auger coefficient is attributed to a coincidental resonance between the bandgap and interband transitions to a complex of higher-lying conduction bands. It is also found that the distortions of PbI 6 octahedra contribute significantly to the high Auger coefficient, offering potential avenues for materials design.

36 MATERIALS SCIENCE↗

Metal–Organic Framework as a New Type of Magnetothermally‐Triggered On‐Demand Release Carrier

The development of external stimuli‐controlled payload systems has been sought after with increasing interest toward magnetothermally‐triggered drug release (MTDR) carriers due to their non‐invasive features. However, current MTDR carriers present several limitations, such as poor heating efficiency caused by the aggregation of iron oxide nanoparticles (IONPs) or the presence of antiferromagnetic phases which affect their efficiency. Herein, a novel MTDR carrier is developed using a controlled encapsulation method that fully fixes and confines IONPs of various sizes within the metal–organic frameworks (MOFs). This novel carrier preserves the MOF's morphology, porosity, and IONP segregation, while enhances heating efficiency through the oxidation of antiferromagnetic phases in IONPs during encapsulation. It also features a magnetothermally‐responsive nanobrush that is stimulated by an alternating magnetic field to enable on‐demand drug release. The novel carrier shows improved heating, which has potential applications as contrast agents and for combined chemo and magnetic hyperthermia therapy. It holds a great promise for magneto‐thermally modulated drug dosing at tumor sites, making it an exciting avenue for cancer treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗