Detailed analysis of chemical corrosion of ultra-thin wires used in drift chamber detectors

Ultra-thin metallic anode and cathode wires are frequently employed in low-mass gaseous detectors for precision experiments, where the amount of material crossed by charged particles must be minimised. We present here the results of an analysis of the mechanical stress and chemical corrosion effects observed in 40 and 50 μm diameter silver plated aluminum wires mounted within the volume of the MEG II drift chamber, which caused the breakage of about one hundred wires (over a total of ≈ 12000). This analysis is based on the careful inspection of the broken wires by means of optical and electronic microscopes and on a detailed recording of all breaking incidents. We present a simple empirical model which relates the number of broken wires to their exposure time to atmospheric relative humidity and to their mechanical tension, which is necessary for mechanical stability in the presence of electrostatic fields of several kV/cm. Finally we discuss how wire breakages can be avoided or at least strongly reduced by operating in controlled atmosphere during the mounting stages of the wires within the drift chamber and by choosing a 25 % thicker wire diameter, which has very small effects on the detector resolution and efficiency and can be obtained by using a safer fabrication technique.

Detection Efficiency of a NaI (Tl) Gamma Spectrometry System for Measurement of Low Level Radioactivity

Qualitative analysis of radionuclides requires the use of reliable gamma-ray detection system. The NaI(Tl) detector has been widely used and still one of the most used detectors today. It is therefore imperative to validate the reliability of the 5x5 cm2 NaI(Tl) gamma spectrometry system used in carrying out gamma-ray analysis of soil samples in the Radiation and Health Laboratory, Federal University of Agriculture Abeokuta, Nigeria. The gamma ray spectrometer is housed in a 5 cm thick cylindrical lead shield. Calibration was executed using standard materials produced under the auspices of the International Atomic Energy Agency (IAEA). Resolution and detection limit (LD) of the detector were determined using full width at half the maximum of the energy peak of 137Cs and background signal level of the reference materials respectively. Counting efficiencies of the detector was calculated using energies of 1460 keV, 1764keV and 2615 keV for 40K, 226Ra and 232Th respectively. Secondary samples, RGMIX1 and RGMIX2 were formulated and counted to calculate activity concentrations using the NaI(Tl) detector. Resolution of the detector was calculated to be 7.8% of 137Cs, which is good for a NaI(Tl) detector. The counting efficiency of the detector is seen to depend on the gamma ray energy. The results from this work shows that the detector system is suitable gamma spectrometry, and will give quality measurements when used for quantitative determination of radionuclides in environmental samples. The efficiency and resolution of the NaI(Tl) detector should also be determined using photon energies obtained from other radioactive sources.

Langevin dynamics simulation of protein dynamics in nanopores at microsecond timescales

With rapid advancement in the fields of nanopore analysis of protein, it has become imperative to develop modeling framework for understanding the protein dynamics in nanopores. Such modeling framework should include the effects of electro-osmosis, as it plays significant role during protein translocation in confinement. Currently, the molecular dynamics simulations that include the hydrodynamic effects are limited to a timescale of few 100 ns. These simulations give insight about important events like protein unfolding which occurs in this timescale. But many electrophoresis experiments are limited by a detector resolution of approximately 2.5 microseconds. Analytical theory has been used to interpret protein dynamics at such large timescale. There is a need for molecular modeling of more complex environment and protein shapes which cannot be accounted for by analytical theory. We have developed a framework to study globular protein dynamics in nanopores by using langevin dynamics on a rigid body model of protein and the hydrodynamics is accounted by analytical theory for simple cylindrical nanopore geometry. This framework has been applied to study the dynamics of Ubiquitin translocation in SiNx nanopore by Nir et al. They have reported 7 times decrease in average dwell time of the protein inside the nanopore in response to a small change in pH from 7.0 to 7.2 and the modification of protein charge was attributed for such drastic change. Closer examination using our simulation revealed that the electro-osmotic effects originating due to very small change in the surface electrostatic potential of the nanopore could lead to such a drastic change in protein dynamics.

Analysis-Specific Fast Simulation at the LHC with Deep Learning

We present a fast-simulation application based on a deep neural network, designed to create large analysis-specific datasets. Taking as an example the generation of W + jet events produced in $$\sqrt{s}=$$ s = 13 TeV proton–proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

Charge quantization and detector resolution

Charge quantization, or the absence thereof, is a central theme in quantum circuit theory, with dramatic consequences for the predicted circuit dynamics. Very recently, the question of whether or not charge should actually be described as quantized has enjoyed renewed widespread interest, with however seemingly contradictory propositions. Here, we intend to reconcile these different approaches, by arguing that ultimately, charge quantization is not an intrinsic system property, but instead depends on the spatial resolution of the charge detector. We show that the latter can be directly probed by unique geometric signatures in the correlations of the supercurrent. We illustrate these findings at the example of Josephson junction arrays in the superinductor regime, where the transported charge appears to be continuous. Finally, we comment on potential consequences of charge quantization beyond superconducting circuits.

Molten Pool Tracking Using a Superpixel-Based Approach in a Laser Metal Deposition Process

Nowadays, additive manufacturing technologies (AM) suffer from insufficient or lacking methodologies/techniques for quality control. This fact represents a key technological barrier preventing broader industrial adoption of AM, particularly in high-value applications where component failure cannot be accepted. This article presents a real-time melt pool segmentation and monitoring technique applicable to the direct laser metal deposition (LMD) process. An infrared camera with an InSb detector (resolution of 640 × 480, spectral range between 3 and 5 μm) was used. An algorithm, called gravitational superpixels, is presented. This algorithm can group pixels and generate superpixels based on a block generation technique that compares color similarity and temperature in infrared images. Besides, a color similarity correction is applied to reduce uncertainty in segmentation, as well as for eliminating the image background. The task of extracting edges is based on the law of universal gravitation. A quantitative and qualitative algorithm performance analysis, which uses standard metrics, is presented. The analysis demonstrates better versatility than reduction/feature extraction or image segmentation approaches by high-/low-pass filtering. The experimental validation was carried out, extracting and measuring the molten pool geometry and its thermal signature. Then, measures were compared against ground truth and against results obtained by other similar methods. The proposed gravitational superpixel method has higher precision and performance. Our proposal has a significant potential for monitoring industrial AM processes since it requires minimal modifications of commercially available industrial machines.

Porosity Analysis of Additive Manufactured Parts Using CAQ Technology

Components produced by additive technology are implemented in various spheres of industry, such as automotive or aerospace. This manufacturing process can lead to making highly optimized parts. There is not enough information about the quality of the parts produced by additive technologies, especially those made from metal powder. The research in this article deals with the porosity of components produced by additive technologies. The components used for the research were manufactured by the selective laser melting (SLM) method. The shape of these components is the same as the shape used for the tensile test. The investigated parts were printed with orientation in two directions, Z and XZ with respect to the machine platform. The printing strategy was “stripe”. The material used for printing of the parts was SS 316L-0407. The printing parameters were laser power of 200 W, scanning speed of 650 mm/s, and the thickness of the layer was 50 µm. A non-destructive method was used for the components’ porosity evaluation. The scanning was performed by CT machine METROTOM 1500. The radiation parameters used for getting 3D scans were voltage 180 kV, current 900 µA, detector resolution 1024 × 1024 px, voxel size 119.43 µm, number of projections 1050, and integration time 2000 ms. This entire measurement process responds to the computer aided quality (CAQ) technology. VG studio MAX 3.0 software was used to evaluate the obtained data. The porosity of the parts with Z and XZ orientation was also evaluated for parts’ thicknesses of 1, 2, and 3 mm, respectively. It has been proven by this experimental investigation that the printing direction of the part in the additive manufacturing process under question affects its porosity.

Exothermic dark matter for XENON1T excess

Motivated by the recent excess in the electron recoil from XENON1T experiment, we consider the possibility of exothermic dark matter, which is composed of two states with mass splitting. The heavier state down-scatters off the electron into the lighter state, making an appropriate recoil energy required for the Xenon excess even for the standard Maxwellian velocity distribution of dark matter. Accordingly, we determine the mass difference between two component states of dark matter to the peak electron recoil energy at about 2.5 keV up to the detector resolution, accounting for the recoil events over ER = 2 − 3 keV, which are most significant. We include the effects of the phase-space enhancement and the atomic excitation factor to calculate the required scattering cross section for the Xenon excess. We discuss the implications of dark matter interactions in the effective theory for exothermic dark matter and a massive Z′ mediator and provide microscopic models realizing the required dark matter and electron couplings to Z′.