Complementary Detector Research Articles

High-Energy Neutrino Astronomy—Baikal-GVD Neutrino Telescope in Lake Baikal

High-energy neutrino astronomy is a fascinating new field of research, rapidly developing over recent years. It opens a new observation window on the most violent processes in the universe, fitting very well to the concept of multi-messenger astronomy. This may be exemplified by the recent discovery of the high-energy neutrino emissions from the γ-ray loud blazar TXS 0506+056. Constraining astrophysical neutrino fluxes can also help to understand the long-standing mystery of the origin of the ultra-high energy cosmic rays. Astronomical studies of high-energy neutrinos are carried out by large-scale next-generation neutrino telescopes located in different regions of the world, forming a global network of complementary detectors. The Baikal-GVD, being currently the largest neutrino telescope in the Northern Hemisphere and still growing up, is an important constituent of this network. This paper briefly reviews working principles, analysis methods, and some selected results of the Baikal-GVD neutrino telescope.

In Situ Atomic Force Microscopy Depth-Corrected Three-Dimensional Focused Ion Beam Based Time-of-Flight Secondary Ion Mass Spectroscopy: Spatial Resolution, Surface Roughness, Oxidation.

Atomic force microscopy (AFM) is a well-known tool for studying surface roughness and to collect depth information about features on the top atomic layers of samples. By combining secondary ion mass spectroscopy (SIMS) with focused ion beam (FIB) milling in a scanning electron microscope (SEM), chemical information of sputtered structures can be visualized and located with high lateral and depth resolution. In this paper, a high vacuum (HV) compatible AFM was installed in a TESCAN FIB-SEM instrument that was equipped with a time-of-flight SIMS (ToF-SIMS) detector. To calibrate the sputtering rate and measure the induced roughness caused by the ToF-SIMS analysis, subsequent AFM measurements were performed on an inorganic multilayer vertical cavity surface-emitting laser sample. Normalized sputtering rates were used to aid the accurate three-dimensional reconstruction of the sputtered volume's chemical composition. Achievable resolution, surface roughness during sputtering, and surface oxidation issues were analyzed. The integration of complementary detectors opens up the ability to determine the sample properties as well as to understand the influence of the Ga+ ion sputtering method on the sample surface during the analysis.

A selective comprehensive reversed-phase×reversed-phase 2D-liquid chromatography approach with multiple complementary detectors as advanced generic method for the quality control of synthetic and therapeutic peptides.

There is a huge, still increasing market for synthetic and therapeutic peptides. Their quality control is commonly based on a generic reversed-phase liquid chromatography (RPLC) method with C18 stationary phase and acetonitrile gradient with 0.1% trifluoroacetic acid in the mobile phase. It performs exceptionally well for a wide variety of impurities, yet structurally closely related impurities with similar sequences, not resolved in preparative RPLC, may easily coelute in the corresponding QC run as well. To address this problem an advanced generic 2D-LC impurity profiling method was developed in this work. It employs a selective comprehensive (high resolution sampling) RP×RP 2D-LC separation using a 100×2.1mm ID column with the common acidic generic gradient in the first dimension, while RPLC under basic pH on a short 30×3mm ID column is used in the second dimension. Recording data with a UV detector at 215nm after 1D separation provides the common generic 1D chromatogram. However, after the 2D separation a flow splitter enabled recording of the signals of complementary detectors comprising a diode array detector (DAD) in-line with a charged aerosol detector (CAD) and a quadrupole-time-of-flight (QTOF) mass spectrometer (MS) with an electrospray ionization (ESI) source. Generic conditions of this 2D-LC method have been established through optimization of 2D stationary and mobile phase considering different pH values and buffer concentrations. The orthogonal separation principle has been documented by a number of therapeutic peptides including Exenatide, Octreotide, Cyclosporine A and Oxytocin as well as some other proprietary synthetic peptides. The information density can be further enhanced by using the QTOF-MS detector by data independent acquisition with SWATH. Through this sequential window acquisition of all theoretical fragment ion mass spectra it became possible to collect MS/MS data comprehensively in the high-resolution sampling window, thus enabling the extraction of 2D-EICs from fragment ions and the generation of 2D-contour plots of all product ions. Using Oxytocin as an example for an important therapeutic peptide, the ability of this advanced generic sRP-UV×RP-DAD-CAD-ESI-QTOF-MS/MS method with SWATH for peptide quality control is discussed.

RNO-G detection perspectives of binary neutron star mergers

AbstractThe Radio Neutrino Observatory Greenland (RNO-G) is currently being deployed and it is currently gathering data. As a precursor and complementary detector to the future radio array of IceCube-Gen2 in Antarctica, it will explore mainly the Northern sky via in-ice radio detection technique. The total array configuration includes 35 radio stations and will be fully completed within three years from now. The antennas will register the radio signals produced by the Askaryan effect in cascades generated in ice by neutrinos. RNO-G’s scientific purpose is to detect UHE neutrinos at energies above 10 PeV. Due to the attenuation length of radio waves in ice (order of 1 km) the radio detection allows to address neutrino energies above several PeV. The detector will reach unprecedented sensitivity in the scale from tens of PeV up to EeV. Models predict GRBs induced by binary neutron star mergers as likely transient sources of such highly energetic neutrinos. The current study of NS-NS mergers will therefore possibly be complemented by future RNO-G detections through multimessenger temporal and spatial coincidence, including an alert system. In this presentation, we will describe the instrument capabilities and explore the possibility of detection of such sources with RNO-G.

Proposal Complementary Action Detection

Temporal action detection not only requires correct classification but also needs to detect the start and end times of each action accurately. However, traditional approaches always employ sliding windows or actionness to predict the actions, and it is different to train to model with sliding windows or actionness by end-to-end means. In this article, we attempt a different idea to detect the actions end-to-end, which can calculate the probabilities of actions directly through one network as one part of the results. We present PCAD, a novel proposal complementary action detector to deal with video streams under continuous, untrimmed conditions. Our approach first uses a simple fully 3D convolutional network to encode the video streams and then generates candidate temporal proposals for activities by using anchor segments. To generate more precise proposals, we also design a boundary proposal network to offer some complementary information for the candidate proposals. Finally, we learn an efficient classifier to classify the generated proposals into different activities and refine their temporal boundaries at the same time. Our model can achieve end-to-end training by jointly optimizing classification loss and regression loss. When evaluating on the THUMOS’14 detection benchmark, PCAD achieves state-of-the-art performance in high-speed models.

Characterization of a Molecular Breast Imaging system in Limited Angle Tomography using a dedicated breast phantom

This work reports on the Limited Angle Tomography (LAT) studies of a novel Molecular Breast Imaging (MBI) system with radionuclides, made by two asymmetric SPECT-like detector heads with two different optics. MBI has a central role to play in detecting the small breast tumours, especially for dense breasts, due to its specificity in detecting tumours even in dense breasts. For this purpose, an innovative device has been developed at ISS in Rome, composed of a dual detector setup which combines images from two complementary detectors at the opposite sides of the compressed breast, and that allows spot compression and LAT for a tumour 3-D reconstruction. Our study tries to demonstrate the feasibility of using this novel system in LAT , where a small detector head with pinhole collimator is able to rotate around the breast phantom, to diagnose the small tumours and to improve the sensitivity and efficiency. Detector heads and phantom, LAT setup and first preliminary results, are presented.

A novel zinc finger protein-based amperometric biosensor for miRNA determination.

This paper reports a simple electrochemical strategy for the determination of microRNAs (miRNAs) using a commercial His-Tag-Zinc finger protein (His-Tag-ZFP) that binds preferably (but non-sequence specifically) RNA hybrids over ssRNAs, ssDNAs, and dsDNAs. The strategy involves the use of magnetic beads (His-Tag-Isolation-MBs) as solid support to capture the conjugate formed in homogenous solution between His-Tag-ZFP and the dsRNA homohybrid formed between the target miRNA (miR-21 selected as a model) and a biotinylated synthetic complementary RNA detector probe (b-RNA-Dp) further conjugated with a streptavidin-horseradish peroxidase (Strep-HRP) conjugate. The electrochemical detection is carried out by amperometry at disposable screen-printed carbon electrodes (SPCEs) (- 0.20 V vs Ag pseudo-reference electrode) upon magnetic capture of the resultant magnetic bioconjugates and H2O2 addition in the presence of hydroquinone (HQ). The as-prepared biosensor exhibits a dynamic concentration range from 3.0 to 100 nM and a detection limit (LOD) of 0.91 nM for miR-21 in just ~ 2 h. An acceptable discrimination was achieved between the target miRNA and other non-target nucleic acids (ssDNA, dsDNA, ssRNA, DNA-RNA, miR-122, miR-205, and single central- or terminal-base mismatched sequences). The biosensor was applied to the analysis of miR-21 from total RNA (RNAt) extracted from epithelial non-tumorigenic and adenocarcinoma breast cells without target amplification, pre-concentration, or reverse transcription steps. The versatility of the methodology due to the ZFP's non-sequence-specific binding behavior makes it easily extendable to determine any target RNA only by modifying the biotinylated detector probe.

Ultra-high energy neutrino searches and GW follow-up with the Pierre Auger Observatory

The surface detector array (SD) of the Pierre Auger Observatory is sensitive to neutrinos at energies in the 100 PeV to 100 EeV range. This sensitivity, together with its large acceptance, makes it a complementary detector to other neutrino telescopes, which have their peak sensitivities at lower energies. The neutrino-induced air showers that the SD of the Pierre Auger Observatory is sensitive to can be divided into those induced by interactions of neutrinos of any flavor deep in the atmosphere, and those induced by charged-current interactions of tau neutrinos in the Earth's crust. Both of these types can be efficiently distinguished from cosmic ray-induced air showers, provided that their zenith angles are larger than 60∘. As no neutrino candidates were found in the performed searches, we present limits on the diffuse all-flavor neutrino flux. Using these limits, we obtained constraints on cosmic-ray and neutrino production models. In the light of the recent observations of gravitational waves (GW), we also present the follow-up of LIGO/Virgo GW events. These include binary black hole merger events and also GW170817, the only binary neutron star merger ever observed directly.

Gas chromatography with tandem cold electron ionization mass spectrometric detection and vacuum ultraviolet detection for the comprehensive analysis of fentanyl analogues

Fentanyl and its derivatives are amongst the ever-growing list of emerging drugs which are impinging on current traditional analytical techniques employed in forensic laboratories. To avoid current regulations, fentanyl analogues are being illicitly synthesized by slight alterations of functional groups to the fentanyl skeleton leading to inaccurate identifications, thus posing the greatest challenge to laboratories. In this study, a novel analytical technique is presented in which gas chromatography (GC) is interfaced with both cold electron ionization mass spectrometric (cold EI MS) and vacuum ultraviolet (VUV) detection by the means of a flow splitter for the simultaneous qualitative and quantitative analysis of twenty-four fentanyl analogues, including seven sets of positional isomers. For most of the twenty-four analogues, enhanced molecular ions were obtained with at least 1% intensity relative to base peak. In addition to enhanced molecular ions, the GC-cold EI MS maintained fragmentation pathways observed by GCMS with classical electron ionization. For the most part, VUV detection resulted in unique VUV spectra for fentanyl analogues including positional isomers. The combination of these two complementary detectors in tandem with the high resolving power of the gas chromatograph, allows for higher confidence in analyte identification by the combination of retention times, cold EI mass spectra and VUV spectra. The preferred method for quantitation was based on VUV detection and offered excellent determination coefficients (R2≥0.999) for most analytes over two orders of magnitude dynamic range, without the need for deuterated internal standards. For both run-to-run and day-to-day repeatability studies, at moderate solute concentrations, the correct fentanyl related compound was identified in almost every instance from a library containing all the fentanyl analogues plus hundreds of other analytes.

Pedestrian Detection via Body Part Semantic and Contextual Information With DNN

Pedestrian detection has achieved great improve-ments in recent years, while complex occlusion handling and high-accurate localization are still the most important problems. To take advantage of the body part semantic information and the contextual information for pedestrian detection, we propose the part and context network (PCN) in this paper. A PCN is composed of three branches: the basic branch; the part branch; and the context branch. It specially utilizes two branches to detect the pedestrians through the body part semantic information and the contextual information, respectively. In the part branch, the semantic information of body parts can communicate with each other via long short-term memory (LSTM). In the context branch, we adopt a local competition mechanism (maxout) for adaptive context scale selection. By combining the outputs of all branches, we develop a strong complementary pedestrian detector with a lower miss rate and higher localization accuracy, especially for the occlusion pedestrian. The combination of the body part semantic information and the contextual information in pedestrian detection is fully explored in this paper. Comprehensive evaluations on three challenging pedestrian detection datasets (i.e., Caltech, INRIA and KITTI) well demonstrate the effectiveness of our proposed PCN. Code for PCN is publicly available on GitHub https://github.com/sunnyxiaohu/pcn_pedestrian .

Anomaly Detection Techniques Based on Kappa-Pruned Ensembles

Ensemble-based anomaly detection systems (ADSs), using Boolean combination, have been shown to reduce the false alarm rate over that of a single detector. However, the existing Boolean combination methods rely on an exponential number of combinations making them impractical, even for a small number of detectors. In this paper, we propose weighted pruning-based Boolean combination, an efficient approach for selecting and combining accurate and diverse anomaly detectors. It works in three phases. The first phase selects a subset of the available base diverse soft detectors by pruning all the redundant soft detectors based on a weighted version of Cohen's kappa measure of agreement. The second phase selects a subset of diverse and accurate crisp detectors from the base soft detectors (selected in Phase1) based on the unweighted kappa measure. The selected complementary crisp detectors are then combined in the final phase using Boolean combinations. The results on two large scale datasets show that the proposed weighted pruning approach is able to maintain and even improve the accuracy of existing Boolean combination techniques, while significantly reducing the combination time and the number of detectors selected for combination.

Improved background suppression for radiative capture reactions at LUNA with HPGe and BGO detectors

Direct measurements of small nuclear reaction cross sections require a low background in the signal region of interest to achieve the necessary sensitivity. We describe two complementary detector setups that have been used for studies of reactions with solid targets at the Laboratory for Underground Nuclear Astrophysics (LUNA): a high-purity germanium detector and a bismuth germanate (BGO) detector. We present the effect of a customised lead shielding on the measured background spectra in the two detector setups at LUNA. We developed a model to describe the contributions of environmental and intrinsic backgrounds in the BGO detector measurements. Furthermore we present an upgrade of the data acquisition system for our BGO detector, which allows us to exploit the features of the segmented detector and overcome some of the limitations encountered in previous experiments. We conclude with a discussion on the improved sensitivity of the presented setups, and the benefits for ongoing and possible future measurements.

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.

Upgrade of the CMS muon trigger system in the barrel region

To maintain the excellent performance shown during the LHC's Run-1 the Level-1 Trigger of the Compact Muon Solenoid experiment underwent a significant upgrade. One part of this upgrade is the re-organization of the muon trigger path from a subsystem-centric view in which hits in the drift tubes (DT), the cathode strip chambers (CSC), and the resistive plate chambers (RPC) were treated separately in dedicated track-finding systems to one in which complementary detector systems for a given region (barrel, overlap, and endcap) are merged at the track-finding level. This fundamental restructuring of the muon trigger system required the development of a system to receive track candidates from the track-finding layer, remove potential duplicate tracks, and forward the best candidates to the global decision layer.An overview will be given of the new track-finder system for the barrel region, the Barrel Muon Track Finder (BMTF), as well as the cancel-out and sorting layer: the upgraded Global Muon Trigger (μGMT). Both the BMTF and μGMT have been implemented in a Xilinx Virtex-7 card utilizing the microTCA architecture. While the BMTF improves on the proven and well-tested algorithms used in the Drift Tube Track Finder during Run-1, the μGMT is an almost complete re-development due to the re-organization of the underlying systems from track-finders for a specific detector to regional track finders covering a given area of the whole detector. Additionally the μGMT calculates a muon's isolation using energy information received from the calorimeter trigger. This information is added to the muon objects forwarded to the global decision layer, the so-called Global Trigger.

Integration of Weigh-in-Motion (WIM) and inductive signature data for truck body classification

Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately in the US, currently available commercial vehicle data contain critical gaps when it comes to linking vehicle and operational characteristics. Leveraging existing traffic sensor infrastructure, we developed a novel, readily implementable approach of integrating two complementary data collection devices, Weigh-in-Motion (WIM) systems and advanced inductive loop detectors (ILD), to produce high resolution truck data. For each vehicle traversing a WIM site, an inductive signature was collected along with WIM measurements such as axle spacing and weight which were then used as inputs to a series of truck body classification models that encompass all truck classes in the most common axle-based Federal Highway Administration (FHWA) classification scheme in the US. Since body configuration can be linked to commodity carried, drive and duty cycle, and other distinct operating characteristics, body class data is undeniably useful for freight planning and air quality monitoring. A multiple classifier systems (MCS) method was adopted to increase the classification accuracy for minority body classes. In all, eight separate body classifications models were developed from an extensive data set of 18,967 truck records distinguishing an unprecedented total of 23 single unit truck and 31 single and semi-trailer body configurations, each with over 80% correct classification rates (CCR). Remarkably, the body class model for five axle semi-tractor trailers – the most diverse truck category – achieved MCS CCRs above 85% for several industry specific classes including refrigerated and non-refrigerated intermodal containers, livestock, and logging trailers.

A fully electronic microfabricated gas chromatograph with complementary capacitive detectors for indoor pollutants.

This paper reports a complete micro gas chromatography (μGC) system in which all the components are lithographically microfabricated and electronically interfaced. The components include a bi-directional Knudsen pump, a preconcentrator, separation columns and a pair of capacitive gas detectors; together, these form the iGC3.c2 system. All the fluidic components of the system are fabricated by a common three-mask lithographic process. The Knudsen pump is a thermomolecular pump that provides air flow to the μGC without any moving parts. The film heaters embedded in the separation columns permit temperature programming. The capacitive detectors provide complementary response patterns, enhancing vapor recognition and resolving co-eluting peaks. With the components assembled on printed circuit boards, the system has a footprint of 8×10 cm2 . Using room air as the carrier gas, the system is used to experimentally demonstrate the analysis of 19 chemicals with concentration levels on the order of parts per million (p.p.m.) and parts per billion (p.p.b.). The tested chemicals include alkanes, aromatic hydrocarbons, aldehydes, halogenated hydrocarbons and terpenes. This set of chemicals represents a variety of common indoor air pollutants, among which benzene, toluene and xylenes (BTX) are of particular interest.

Feature Quality-Based Dynamic Feature Selection for Improving Salient Object Detection.

Salient object detection is typically accomplished by combining the outputs of multiple primitive feature detectors (that output feature maps or features). The diversity of images means that different basic features are useful in different contexts, which motivates the use of complementary feature detectors in a general setting. However, naive inclusion of features that are not useful for a particular image leads to a reduction in performance. In this paper, we introduce four novel measures of feature quality and then use those measures to dynamically select useful features for the combination process. The resulting saliency is thereby individually tailored to each image. Using benchmark data sets, we demonstrate the efficacy of our dynamic feature selection system by measuring the performance enhancement over the state-of-the-art models for complementary feature selection and saliency aggregation tasks. We show that a salient object detection technique using our approach outperforms competitive models on the PASCAL VOC 2012 dataset. We find that the most pronounced performance improvements occur in challenging images with cluttered backgrounds, or containing multiple salient objects.

Wireless fluorescence capsule for endoscopy using single photon-based detection.

Fluorescence Imaging (FI) is a powerful technique in biological science and clinical medicine. Current FI devices that are used either for in-vivo or in-vitro studies are expensive, bulky and consume substantial power, confining the technique to laboratories and hospital examination rooms. Here we present a miniaturised wireless fluorescence endoscope capsule with low power consumption that will pave the way for future FI systems and applications. With enhanced sensitivity compared to existing technology we have demonstrated that the capsule can be successfully used to image tissue autofluorescence and targeted fluorescence via fluorophore labelling of tissues. The capsule incorporates a state-of-the-art complementary metal oxide semiconductor single photon avalanche detector imaging array, miniaturised optical isolation, wireless technology and low power design. When in use the capsule consumes only 30.9 mW, and deploys very low-level 468 nm illumination. The device has the potential to replace highly power-hungry intrusive optical fibre based endoscopes and to extend the range of clinical examination below the duodenum. To demonstrate the performance of our capsule, we imaged fluorescence phantoms incorporating principal tissue fluorophores (flavins) and absorbers (haemoglobin). We also demonstrated the utility of marker identification by imaging a 20 μM fluorescein isothiocyanate (FITC) labelling solution on mammalian tissue.

Experimental studies of Generalized Parton Distributions

Generalized Parton Distributions (GPDs) are nowadays the object of an intense effort of research, in the perspective of understanding nucleon structure. They describe the correlations between the longitudinal momentum and the transverse spatial position of the partons inside the nucleon and they can give access to the contribution of the orbital momentum of the quarks to the nucleon spin. Deeply Virtual Compton scattering (DVCS), the electroproduction on the nucleon, at the quark level, of a real photon, is the process more directly interpretable in terms of GPDs of the nucleon. Depending on the target nucleon (proton or neutron) and on the DVCS observable extracted (cross-sections, target- or beam-spin asymmetries, etc.), different sensitivity to the various GPDs for each quark flavor can be exploited. This article is focused on recent promising results, obtained at Jefferson Lab, on cross-sections and asymmetries for DVCS, and their link to GPDs. These data open the way to a “tomographic” representation of the structure of the nucleon, allowing the extraction of transverse-space densities of the quarks at fixed longitudinal momentum. The extensive experimental program to measure GPDs at Jefferson Lab with the 12 GeV-upgraded electron accelerator and the complementary detectors that will be housed in three experimental Halls (A, B and C), will also be presented.

Hyphenation of Field-Flow Fractionation and Magnetic Particle Spectroscopy

Magnetic nanoparticles (MNPs) exhibit unique magnetic properties making them ideally suited for a variety of biomedical applications. Depending on the desired magnetic effect, MNPs must meet special magnetic requirements which are mainly determined by their structural properties (e.g., size distribution). The hyphenation of chromatographic separation techniques with complementary detectors is capable of providing multidimensional information of submicron particles. Although various methods have already been combined for this approach, so far, no detector for the online magnetic analysis was used. Magnetic particle spectroscopy (MPS) has been proven a straightforward technique for specific quantification and characterization of MNPs. It combines high sensitivity with high temporal resolution; both of these are prerequisites for a successful hyphenation with chromatographic separation. We demonstrate the capability of MPS to specifically detect and characterize MNPs under usually applied asymmetric flow field-flow fractionation (A4F) conditions (flow rates, MNP concentration, different MNP types). To this end MPS has been successfully integrated into an A4F multidetector platform including dynamic ligth scattering (DLS), multi-angle light scattering (MALS) and ultraviolet (UV) detection. Our system allows for rapid and comprehensive characterization of typical MNP samples for the systematic investigation of structure-dependent magnetic properties. This has been demonstrated by magnetic analysis of the commercial magnetic resonance imaging (MRI) contrast agent Ferucarbotran (FER) during hydrodynamic A4F fractionation.