LTCC Strip Electrode Arrays for Gas Electron Multiplier Detectors

The Low Temperature Cofired Ceramic (LTCC) technology has proven to be highly suitable for 3D microstructures manufacturing in electronic devices due to its excellent electrical and mechanical properties. In this paper, a novel idea of implementing the LTCC structures into high-energy particle detectors technology is proposed. It can be applied in High Energy Physics (HEP) laboratories, where such sophisticated sensors are constantly exposed to particles of the TeV energy range for many years. The most advanced applications of the concept are based on dedicated gas amplifier systems coupled with readout microstructures. Typically, the readout microstructures are made in the Printed Circuit Boards (PCB) technology and processed in a sophisticated and patent-protected way. This article presents the manufacturing process and parameters of the novel microstructures made in the LTCC technology. The structures were implemented into the high-energy particle detector, and the first results are presented.

The influences of radiation effects on DC/RF performances of Lg=22 nm gate-all-around nanosheet field-effect transistor

In this paper, we carried out detailed TCAD simulations to investigate the radiation effects, e.g., total ionizing dose (TID) and single-event effects (SEEs), on direct current (DC) and radio frequency (RF) characteristics of the gate-all-around (GAA) nanosheet field-effect transistor (FET). The simulation model used is composed of 7-layer stacked GAA nanosheet FET with Lg=22 nm, which was implemented in this study. The open current and the drain-induced barrier lowering of the device are ~ 3mA/μm and 47mV/V, respectively. The results indicate that the TID have little influence on the DC and RF characteristics when the transistor is working in an open state. During the SEEs simulation, we considered three incident directions for the high energy particle, including the lateral direction of the channels, the vertical direction of the channels and the top of the channels. The influence of the particle injecting along the lateral and vertical directions of the channels shows stronger relation with the distance from the incident point compared to the influence of the particle from the top. Besides, the general influence of the particle injecting along the lateral directions of the channels is higher than the other two directions. The total injected charge of the particle injecting along the lateral direction, along the vertical direction and from the top are 3 fC, 1.4 fC and 2.1 fC, respectively. As compared to the FinFET, the GAA nanosheet has superior RF performances and less sensitivity to TID effect. This work can provide a guideline for the GAA nanosheet devices in aerospace and avionic RF applications.

Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences

We show that density models describing multiple observables with (i) hard boundaries and (ii) dependence on external parameters may be created using an auto-regressive Gaussian mixture model. The model is designed to capture how observable spectra are deformed by hypothesis variations, and is made more expressive by projecting data onto a configurable latent space. It may be used as a statistical model for scientific discovery in interpreting experimental observations, for example when constraining the parameters of a physical model or tuning simulation parameters according to calibration data. The model may also be sampled for use within a Monte Carlo simulation chain, or used to estimate likelihood ratios for event classification. The method is demonstrated on simulated high-energy particle physics data considering the anomalous electroweak production of a $Z$ boson in association with a dijet system at the Large Hadron Collider, and the accuracy of inference is tested using a realistic toy example. The developed methods are domain agnostic; they may be used within any field to perform simulation or inference where a dataset consisting of many real-valued observables has conditional dependence on external parameters.

An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane

In scientific domains such as high-energy particle physics and genomics, the quantity of high-speed data traffic generated may far exceed the storage throughput and be unable to be in time stored in the current node. Cooperating and utilizing multiple storage nodes on the forwarding path provides an opportunity for high-speed data storage. This paper proposes the use of flow entries to dynamically split traffic among selected neighbor nodes to sequentially amortize excess traffic. We propose a neighbor selection mechanism based on the Local Name Mapping and Resolution System, in which the node weights are computed by combing the link bandwidth and node storage capability, and determining whether to split traffic by comparing normalized weight values with thresholds. To dynamically offload traffic among multiple targets, the cooperative storage strategy implemented in a programmable data plane is presented using the relative weights and ID suffix matching. Evaluation shows that our proposed schema is more efficient compared with end-to-end transmission and ECMP in terms of bandwidth usage and transfer time, and is beneficial in big science.

Long-Term Sex- and Genotype-Specific Effects of 56Fe Irradiation on Wild-Type and APPswe/PS1dE9 Transgenic Mice

Space radiation presents a substantial threat to travel beyond Earth. Relatively low doses of high-energy particle radiation cause physiological and behavioral impairments in rodents and may pose risks to human spaceflight. There is evidence that 56Fe irradiation, a significant component of space radiation, may be more harmful to males than to females and worsen Alzheimer’s disease pathology in genetically vulnerable models. Yet, research on the long-term, sex- and genotype-specific effects of 56Fe irradiation is lacking. Here, we irradiated 4-month-old male and female, wild-type and Alzheimer’s-like APP/PS1 mice with 0, 0.10, or 0.50 Gy of 56Fe ions (1GeV/u). Mice underwent microPET scans before and 7.5 months after irradiation, a battery of behavioral tests at 11 months of age and were sacrificed for pathological and biochemical analyses at 12 months of age. 56Fe irradiation worsened amyloid-beta (Aβ) pathology, gliosis, neuroinflammation and spatial memory, but improved motor coordination, in male transgenic mice and worsened fear memory in wild-type males. Although sham-irradiated female APP/PS1 mice had more cerebral Aβ and gliosis than sham-irradiated male transgenics, female mice of both genotypes were relatively spared from radiation effects 8 months later. These results provide evidence for sex-specific, long-term CNS effects of space radiation.

Charged Particle Tracking via Edge-Classifying Interaction Networks

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high-energy particle physics. In particular, particle tracking data are naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges, given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN’s excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.

A GeV-TeV particle component and the barrier of cosmic-ray sea in the Central Molecular Zone

Cosmic rays are important probe of a number of fundamental physical problems such as the acceleration of high and very high energy particles in extreme astrophysical environments. The Galactic center is widely anticipated to be an important cosmic-ray source and the observations of some Imaging Atmospheric Cherenkov Telescopes did successfully reveal a component of TeV-PeV cosmic rays in the vicinity of the Galactic center. Here we report the identification of GeV-TeV cosmic rays in the central molecular zone with the γ-ray observations of the Fermi Large Area Telescope, whose spectrum and spatial gradient are consistent with that measured by the Imaging Atmospheric Cherenkov Telescopes but the corresponding cosmic-ray energy density is substantially lower than the so-called cosmic-ray sea component, suggesting the presence of a high energy particle accelerator at the Galactic center and the existence of a barrier that can effectively suppress the penetration of the particles from the cosmic-ray sea to the central molecular zone.

Quantum imprints of gravitational shockwaves

Gravitational shockwaves are simple exact solutions of Einstein equations representing the fields of ultrarelativistic sources and idealized gravitational waves (shocks). Historically, much work has focused on shockwaves in the context of possible black hole formation in high energy particle collisions, yet they remain at the forefront of research even today. Representing hard modes in the bulk, shocks give rise to the gravitational memory effect at the classical level and implant supertranslation (BMS) hair onto a classical spacetime at the quantum level. The aim of this paper is to further our understanding of the ‘information content’ of such supertranslations. Namely, we show that, contrary to the several claims in the literature, a gravitational shockwave does leave a quantum imprint on the vacuum state of a test quantum field and that this imprint is accessible to local observers carrying Unruh-DeWitt (UDW) detectors in this spacetime.

The HotSpot Code as a Tool to Improve Risk Analysis During Emergencies: Predicting I-131 and CS-137 Dispersion in the Fukushima Nuclear Accident

Conventional and non-conventional emergencies are among the most important safety and security concerns of the new millennium. Nuclear power and research plants, high-energy particle accelerators, radioactive substances for industrial and medical uses are all considered credible sources of threats both in warfare and in terror scenarios. Estimates of potential radiation releases of radioactive contamination related to these threats are therefore essential in order to prepare and respond to such scenarios. The goal of this paper is to demonstrate that computational modeling codes to simulate transport of radioactivity are extremely valuable to assess expected radiation levels and to improve risk analysis during emergencies helping the emergency planner and the first responders in the first hours of an occurring emergency.