Tagging Efficiency Research Articles

New graph-neural-network flavor tagger for Belle II and measurement of sin 2ϕ1 in B0→J/ψKS0 decays

We present GFlaT, a new algorithm that uses a graph-neural-network to determine the flavor of neutral B mesons produced in ϒ(4S) decays. It improves previous algorithms by using the information from all charged final-state particles and the relations between them. We evaluate its performance using B decays to flavor-specific hadronic final states reconstructed in a 362 fb−1 sample of electron-positron collisions collected at the ϒ(4S) resonance with the Belle II detector at the SuperKEKB collider. We achieve an effective tagging efficiency of (37.40±0.43±0.36%), where the first uncertainty is statistical and the second systematic, which is 18% better than the previous Belle II algorithm. Demonstrating the algorithm, we use B0→J/ψKS0 decays to measure the mixing-induced and direct CP violation parameters, S=(0.724±0.035±0.009) and C=(−0.035±0.026±0.029). Published by the American Physical Society 2024

Jet-Origin Identification and Its Application at an Electron-Positron Higgs Factory.

To enhance the scientific discovery power of high-energy collider experiments, we propose and realize the concept of jet-origin identification that categorizes jets into five quark species (b,c,s,u,d), five antiquarks (b[over ¯],c[over ¯],s[over ¯],u[over ¯],d[over ¯]), and the gluon. Using state-of-the-art algorithms and simulated νν[over ¯]H,H→jj events at 240GeV center-of-mass energy at the electron-positron Higgs factory, the jet-origin identification simultaneously reaches jet flavor tagging efficiencies ranging from 67% to 92% for bottom, charm, and strange quarks and jet charge flip rates of 7%-24% for all quark species. We apply the jet-origin identification to Higgs rare and exotic decay measurements at the nominal luminosity of the Circular Electron Positron Collider and conclude that the upper limits on the branching ratios of H→ss[over ¯],uu[over ¯],dd[over ¯] and H→sb,db,uc,ds can be determined to 2×10^{-4} to 1×10^{-3} at 95%confidence level. The derived upper limit for H→ss[over ¯] decay is approximately 3 times the prediction of the standard model.

Improving the beam quality of the low-energy muon beamline at Paul Scherrer Institute: Characterization of ultrathin carbon foils

The low-energy muon (LEM) beamline at the Paul Scherrer Institute currently stands as the world’s only facility providing a continuous beam of low-energy muons with keV energies for conducting muon spin rotation experiments on a nanometer depth scale in heterostructures and near a sample’s surface. As such, optimizing the beam quality to reach its full potential is of paramount importance. One of the ongoing efforts is dedicated to improving the already applied technique of single muon tagging through the detection of secondary electrons emerging from an ultrathin carbon foil. In this work, we present the results from installing a thinner foil with a nominal thickness of 0.5 μg cm−2 and compare its performance to that of the previously installed foil with a nominal thickness of 2.0 μg cm−2. Our findings indicate improved beam quality, characterized by smaller beam spots, reduced energy loss and straggling of the muons, and enhanced tagging efficiency. Additionally, we introduce a method utilizing blue laser irradiation for cleaning the carbon foil, further improving and maintaining its characteristics. Published by the American Physical Society 2024

Development of a phoswich detector utilizing bismuth germanate crystal scintillator and polyethylene naphthalate scintillation film

In this study, we present the development of a phoswich detector system employing a Bismuth Germanate (BGO) crystal scintillator in combination with a Polyethylene naphthalate (PEN) film plastic scintillator. Phoswich detectors can contribute to various applications, including radiation monitoring, medical imaging, and nuclear physics experiments, due to their ability to discriminate between different types of radiation. The thin PEN film is sensitive to α and β radiation, while the BGO scintillator exhibits good γ radiation detection efficiency. The incident radiation type and its energy can be determined by analyzing the difference in decay times between these two scintillators. This paper presents an analysis of the detector’s response to α, β, and γ radiation, and the particle tagging efficiencies are to be over 99.9%, 92%, and 99.8%, respectively.

Kaon beam simulations employing conventional hadron beam concepts and the RF separation technique at the CERN M2 beamline for the future AMBER experiment

The AMBER-experiment [2, 1], located in the North Experimental Area at CERN, is the successor of the NA58/COMPASS [11] experiment which ran from 2002-2022. AMBER will start its data taking in 2023. The experiment is served by the M2 beamline, employing secondary and tertiary beams produced by 400 GeV c -1 protons from the CERN Super Proton Synchrotron (SPS) impacting the T6 target. For the second phase of their measurements, AMBER will require high-intensity kaon beams [6, 7]. This requirement for high-intensity beams implies a need for accurate particle identification allowing tagging particles of interest that would otherwise be lost for analysis. The beam particle identification is carried out using Cherenkov (CEDAR) detectors [5], whose tagging efficiency depends critically on the beam divergence. In this paper we investigate the beam parameters required, the performance achievable with the current layout of the beamline, as well as possible improvements.

TAGGING EFFICIENCY ESTIMATION IN EXPERIMENTS WITH A COHERENT BREMSSTRAHLUNG BEAM

The angular dependences of the coherent and incoherent parts of the coherent bremsstrahlung (CB) are different, thus the CB beam tagging efficiency differs from that of the bremsstrahlung. It has energy dependence close to the CB beam spectrum and depends on the CB peak energy. Thus, in the course of the cross section measurements, the systematic, measurements of the tagging efficiency should be usually produced. The paper shows that tagging efficiency can be estimated computationally by taking into account the background contribution to the detectors of the tagging system. This allows one to get cross sections simultaneously with the cross section asymmetry measurements in the case when the systematic tagging efficiency measurements were not performed.

Read Range Investigation of RFID Tag on Human Chest

Radio Frequency Identification (RFID) technology for automatic identification, tracking, and monitoring of human is in great demand by the healthcare industry. The existence of passive tag with proximity to human body in general, represents an intrinsic challenge because of the high losses of body tissues A commercial passive ultra-high frequency (UHF) RFID tag with 860 MHz-960 MHz operating frequency range is modelled to study and analyze the effect on the read range parameter by placing the RFID tag in proximity to human body model (the chest) with respect to the placement of pocket position of any shirt The body model of the chest is consisting of three layers which are skin, fat and muscle. The commercial RFID tag that has been used for this study was INLAY-9662. The measurement is done on the commercial tag prior to modelling and simulation in the CST. Furthermore, the simulation was carried on two conditions: (1) in free-space, and (2) in proximity to human body. In addition, 1 mm, 3 mm and 10 mm have been set as the gap between the modelled tag and the chest model to examine the impacted read range at different distances. The results obtained have shown some variations when the distances are varied from the human chest. The read range were achieved at 0.5 m, 1.3 m and 2.4 m for 1 mm, 3 mm and 10 mm respectively. This study has shown the capabilities of human body to reduce the efficiency of the read range whenever RFID tags are placed in proximity to human body.

Effect of heat treatment on the properties of amorphous ribbons in acoustic magnetic tags

Taking Fe–Ni–Co amorphous ribbons in acoustic magnetic tags as research object, the microstructure, composition and crystallization temperature of amorphous ribbons were analyzed by X-ray diffractometer (XRD), scanning electron microscopy (SEM) and differential scanning calorimetry (DSC). The manufacturing process and parameters (annealing temperature, tension and speed) of amorphous ribbons were systematically regulated and analyzed, including the influence on soft magnetic properties of amorphous ribbons by molding process. It indicates that non-magnetic annealing temperature (Ta) remains unchanged, the first magnetic annealing temperature (Tma1) is relatively higher, while second annealing temperature (Tma2), tension and speed are comparatively lower, amorphous ribbons emerge strong sensitivity and high reliability of soft magnetic properties. Meanwhile, combined with the actual experiment and orthogonal test analogy results, the best molding process parameters of amorphous ribbons are obtained: Ta, Tma1 and Tma2 are 375 °C, 315 °C and 255 °C respectively, tension is 8 N and speed is 4 m/min. In addition, the response demonstration of process is carried out under the condition that bias field value is 318.31 A/m and 366.06 A/m. The resonance frequencies of amorphous ribbons are 59.15 kHz and 58.55 kHz respectively, alongside a maximum vibration amplitude is 81, with the weakest and strongest response distance between transmitter and receiver are measured to be 150 cm and 160 cm, which are in line with the actual production process, indicating that the manufacturing process can be optimized to improve the production efficiency and service characteristics of acoustic magnetic tags.

Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging

AbstractThe recent physical realization of quantum computers with hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machine learning (QML), the study of machine learning algorithms running natively on quantum computers. In this work, QML methods are developed and applied to B meson flavor tagging, an important component of experiments which probe CP violation in order to better understand the matter‐antimatter asymmetry of the universe. One simulate boosted ensembles of quantum support vector machines (QSVMs) based on both conventional qubit‐based and continuous variable architectures, attaining effective tagging efficiencies of 28.0% and 29.2%, respectively, comparable with the leading published result of 30.0% using classical machine learning algorithms. The ensemble nature of the classifier is of particular importance, doubling the effective tagging efficiency of a single QSVM, which is find to be highly prone to overfitting. These results are obtained despite the constraint of working with QSVM architectures that are classically simulable, and it finds evidence that QSVMs beyond the simulable regime may be able to realize even higher performance, when sufficiently powerful quantum hardware is developed to execute them.

SsDNA is not superior to dsDNA as long HDR donors for CRISPR-mediated endogenous gene tagging in human diploid RPE1 and HCT116 cells

BackgroundRecent advances in CRISPR technology have enabled us to perform gene knock-in in various species and cell lines. CRISPR-mediated knock-in requires donor DNA which serves as a template for homology-directed repair (HDR). For knock-in of short sequences or base substitutions, ssDNA donors are frequently used among various other forms of HDR donors, such as linear dsDNA. However, partly due to the complexity of long ssDNA preparation, it remains unclear whether ssDNA is the optimal type of HDR donors for insertion of long transgenes such as fluorescent reporters in human cells.ResultsIn this study, we established a nuclease-based simple method for the preparation of long ssDNA with high yield and purity, and comprehensively compared the performance of ssDNA and dsDNA donors with 90 bases of homology arms for endogenous gene tagging with long transgenes in human diploid RPE1 and HCT116 cells. Quantification using flow cytometry revealed lower efficiency of endogenous fluorescent tagging with ssDNA donors than with dsDNA. By analyzing knock-in outcomes using long-read amplicon sequencing and a classification framework, a variety of mis-integration events were detected regardless of the donor type. Importantly, the ratio of precise insertion was lower with ssDNA donors than with dsDNA. Moreover, in off-target integration analyses using donors without homology arms, ssDNA and dsDNA were comparably prone to non-homologous integration.ConclusionsThese results indicate that ssDNA is not superior to dsDNA as long HDR donors with relatively short homology arms for gene knock-in in human RPE1 and HCT116 cells.

Probability of Atlantic Salmon Post-Smolts Encountering a Tidal Turbine Installation in Minas Passage, Bay of Fundy

Tidal stream energy is a renewable energy resource that might be developed to offset carbon emissions. A tidal energy demonstration (TED) area has been designated in Minas Passage, Bay of Fundy, for testing and installing marine hydrokinetic (MHK) turbines. Regulations require quantification of the potential for MHK turbine installations to harm local populations of marine animals. Here, we use acoustic telemetry to quantify the probability that post-smolt inner Bay of Fundy salmon encounter a turbine installation at the TED area. Previous work has quantified the detection efficiency of Innovasea HR acoustic tags as a function of the current speed and range from a moored HR2 receiver and also demonstrated that drifters carrying HR tags will be effectively detected when the drifter track crosses the array of HR2 receivers in Minas Passage. Salmon smolts were tagged and released in Gaspereau and Stewiacke Rivers, Nova Scotia, in order that the HR2 receiver array could monitor seaward migration of the post-smolts through Minas Passage and particularly through the TED area. Presently, we formulate and apply a method by which tag signals detected by the HR2 array can be used to estimate the expected number of times that a post-smolt would encounter a single near-surface MHK turbine installation during its seaward migration.

Analysis and Evaluation of Tracker Tag Efficiency

Tracker tags, such as Apple’s AirTags and Tile’s trackers, are small and cheap devices for finding lost items. Tags use Bluetooth technology and community tracking to find the location of lost tags whenever a compatible smartphone passes nearby. When we lose a tag, several questions may arise, like what are the chances of finding it? Or, when will we receive a notification of its detection? Unfortunately, companies such as Apple or Tile do not provide technical information about the efficiency of their tags. Thus, this paper is aimed at answering those questions based on a methodological analysis and evaluation of tags’ efficiency. The main aspects that impact the efficiency of finding a lost tag are the detection range, community size, and human mobility. These aspects are evaluated in a campus scenario, showing that the probability of finding lost tags is very high: more than 98% of them were found, and the average detection time was about 1 hour.

Biomedical Text NER Tagging Tool with Web Interface for Generating BERT-Based Fine-Tuning Dataset

In this paper, a tagging tool is developed to streamline the process of locating tags for each term and manually selecting the target term. It directly extracts the terms to be tagged from sentences and displays it to the user. It also increases tagging efficiency by allowing users to reflect candidate categories in untagged terms. It is based on annotations automatically generated using machine learning. Subsequently, this architecture is fine-tuned using Bidirectional Encoder Representations from Transformers (BERT) to enable the tagging of terms that cannot be captured using Named-Entity Recognition (NER). The tagged text data extracted using the proposed tagging tool can be used as an additional training dataset. The tagging tool, which receives and saves new NE annotation input online, is added to the NER and RE web interfaces using BERT. Annotation information downloaded by the user includes the category (e.g., diseases, genes/proteins) and the list of words associated to the named entity selected by the user. The results reveal that the RE and NER results are improved using the proposed web service by collecting more NE annotation data and fine-tuning the model using generated datasets. Our application programming interfaces and demonstrations are available to the public at via the website link provided in this paper.

Invariant representation driven neural classifier for anti-QCD jet tagging

We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that, with a well-calibrated and powerful enough feature extractor, a well-trained mass-decorrelated supervised Standard Model neural jet classifier can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing data-augmented mass-invariance (and thus decoupling the dominant factor) not only facilitates background estimation, but also induces more substructure-aware representation learning. We are able to reach excellent tagging efficiencies for all the test signals considered. In the best case, we reach a background rejection rate of 51 and a significance improvement factor of 3.6 at 50% signal acceptance, with the jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.

Neutron tagging following atmospheric neutrino events in a water Cherenkov detector

We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimated to be 26%, with a mis-tag rate of 0.016 per neutrino event. The uncertainty of the tagging efficiency is estimated to be 9.0%. Measurement of the tagging efficiency with data from an Americium-Beryllium calibration agrees with this value within 10%. The tagging procedure was performed on 3,244.4 days of SK-IV atmospheric neutrino data, identifying 18,091 neutrons in 26,473 neutrino events. The fitted neutron capture lifetime was measured as 218±9 μs.

A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging.

Chinese natural language processing tasks often require the solution of Chinese word segmentation and POS tagging problems. Traditional Chinese word segmentation and POS tagging methods mainly use simple matching algorithms based on lexicons and rules. The simple matching or statistical analysis requires manual word segmentation followed by POS tagging, which leads to the inability to meet the practical requirements for label prediction accuracy. With the continuous development of deep learning technology, data-driven machine learning models provide new opportunities for automated Chinese word segmentation and POS tagging. Therefore, a data-driven automated Chinese word segmentation and POS tagging model is proposed in order to address the above problems. Firstly, the main idea and overall framework of the proposed automated model are outlined, and the tagging strategy and neural network language model used are described. Secondly, two main optimisations are made on the input side of the model: (1) the use of word2Vec for the representation of text features, thus representing the text as a distributed word vector; and (2) the use of an improved AlexNet for efficient encoding of long-range word, and the addition of an attention mechanism to the model. Finally, on the output side, an additional auxiliary loss function was designed to optimise the Chinese text based on its frequency. The experimental results show that the proposed model can significantly improve the accuracy and operational efficiency of Chinese word segmentation and POS tagging compared with other existing models, thus verifying its effectiveness and advancement.

Tagging Efficiency of 99m Tc-SC Radiolabeled Alternative Gastric Emptying Meals: A Quantitative Study.

Objective The aim of this study was to know the tagging efficiencies of technetium-99m labeled sulfur colloid (99mTc-SC) with different meals. Materials and Methods Egg white sandwiches are the gold standard for gastric-emptying scan (GES); thus, an egg white omelet labeled with 99m Tc-SC is taken as a standard meal. For evaluation, we included four meals, bread and butter, instant oatmeal, idli, and chapatti, and all meals were prepared by labeling them with 99m Tc-SC. After preparation, food articles were chopped with the help of a metal fork and mixed in simulated gastric fluid. Four samples were taken simultaneously from each food article and analyzed for 1 to 4hours after agitation within the centrifuge. The samples were filtered and separated from the sediments and supernatants. We analyzed the activity in each sample before and after filtration. Results The mean values of labeling efficiency in per cent of various meals were obtained. There was no significant difference in labeling stability for egg whites, chapatti, and idli meals labeled with 99m Tc-SC from 1 to 4hours as their p -value (p>0.05) was insignificant. Conclusion Radiolabeled chapatti and idli with 99m Tc-SC show higher labeling stability, while oatmeal and bread and butter samples show relatively low stability. Thus, for GES, chapatti and idli labeled with 99m Tc-SC can be used as alternatives to eggs for vegetarian people or for those allergic to eggs.

A One-step strategy to target essential factors with auxin-inducible degron system in mouse embryonic stem cells.

The self-renewal and pluripotency of embryonic stem cells (ESCs) are conferred by networks including transcription factors and histone modifiers. The Auxin-inducible degron (AID) system can rapidly and reversibly degrade its target proteins and is becoming a powerful tool to explore novel function of key pluripotent and histone modifier genes in ESCs. However, the low biallelic tagging efficiency and a basal degradation level of the current AID systems deem it unsuitable to target key pluripotent genes with tightly controlled expression levels. Here, we develop a one-step strategy to successfully target and repress the endogenous pluripotent genes in mouse ESCs and replace their expression with AID fused transgenes. Therefore, this work provides an efficient way for employing the AID system to uncover novel function of essential pluripotent and chromatin modifier genes in ESCs.

A backing detector for order-keV neutrons

We have designed and tested a large-area (0.15 m2) neutron detector based on neutron capture on 6Li. The neutron detector design has been optimized for the purpose of tagging the scattering angle of keV-scale neutrons. These neutron detectors would be employed to calibrate the low-energy (<100 eV) nuclear recoil in detectors for dark matter and coherent elastic neutrino nucleus scattering (CEνNS). We describe the design, construction, and characterization of a prototype. The prototype is designed to have a tagging efficiency of ∼25% at the relevant O(keV) neutron energies, and with a mean capture time of ∼17μs. The prototype was characterized using a 252Cf neutron source and agreement with the simulation was observed within a few percent level.

Optical Fluorescence Imaging of Native Proteins Using a Fluorescent Probe with a Cell-Membrane-Permeable Carboxyl Group.

Although various methods for selective protein tagging have been established, their ap plications are limited by the low fluorescent tagging efficiency of specific terminal regions of the native proteins of interest (NPIs). In this study, the highly sensitive fluorescence imaging of single NPIs was demonstrated using a eukaryotic translation mechanism involving a free carboxyl group of a cell-permeable fluorescent dye. In living cells, the carboxyl group of cell-permeable fluorescent dyes reacted with the lysine residues of acceptor peptides (AP or AVI-Tag). Genetically encoded recognition demonstrated that the efficiency of fluorescence labeling was nearly 100%. Nickel-nitrilotriacetic acid (Ni-NTA) beads bound efficiently to a single NPI for detection in a cell without purification. Our labeling approach satisfied the necessary conditions for measuring fluorescently labeled NPI using universal carboxyl fluorescent dyes. This approach is expected to be useful for resolving complex biological/ecological issues and robust single-molecule analyses of dynamic processes, in addition to applications in ultra-sensitive NPIs detection using nanotechnology.