Physical Detectors Research Articles

Differentiable simulation of a liquid argon time projection chamber

Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The ability of these simulators to mimic real, measured data is limited by the modeling of the physical detectors used for data collection. This modeling can be improved by performing dedicated calibration measurements. Conventional approaches calibrate individual detector parameters or processes one at a time. However, the impact of detector processes is entangled, making this a poor description of the underlying physics. We introduce a differentiable simulator that enables a gradient-based optimization, allowing for the first time a simultaneous calibration of all detector parameters. We describe the procedure of making a differentiable simulator, highlighting the challenges of retaining the physics quality of the standard, non-differentiable version while providing meaningful gradient information. We further discuss the advantages and drawbacks of using our differentiable simulator for calibration. Finally, we provide a starting point for extensions to our approach, including applications of the differentiable simulator to physics analysis pipelines.

Evaluation of characteristics on the indoor dwelling environments of traditional villages in Hehuang area of China based on basic environmental parameters under remote sensing technology

In Chinese northwestern rural households where the indoor air quality is worse than the outside, rural residents face much higher health risks than urban residents. In this study, remote physical sensor devices and detectors technology were used to monitor and calculate the dwelling environmental quality index of 12 dwellings of traditional villages in the Hehuang area of China based on four environmental parameters to assess the indoor air quality of local dwellings. The results show that (1) the DEQI value reached 76 in summer but only 50 in winter, and the indoor dwelling environment quality in the Hehuang area was better in summer than in winter. (2) The ST was only 34 in summer and even lower in winter, at only 10, and low indoor temperatures were a chronic problem for the indoor dwelling spaces throughout the year. (3) In both summer and winter, the SRH reached 86, which was satisfactory overall. (4) In summer, the SCO2 value was 89 and the SPM10 value was 96, reflecting relatively good overall conditions. In winter, the SCO2 value was 65, the concentrations generally increased, and the SPM10 value was equal to 40, with concentrations exceeding the national standard value more than half the time. This study enables local governments and residents to accurately evaluate and analysis existing indoor dwelling environment problems based on this remote sensing technology, enhance the environmental health awareness of local residents, and provide an objective basis for improving the local dwelling environment and developing relevant policies.

Adaptive green split optimization for traffic control with low penetration rate trajectory data

Adaptive traffic signal control systems often rely on expensive physical detection infrastructure. However, with the advent of widespread trajectory data, it is now possible to implement adaptive control entirely avoiding such costs. We present two simple adaptive control policies which only require sample delay and number of stops, with the goal to mitigate the presence of oversaturation. The simplicity stems from the necessity of controlling under any trajectory penetration rate. The two policies differ on the possibilities of the control infrastructure to be implemented. The first one minimizes oversaturation by deviating from a reference pre-timed signal plan. This signal plan can be an existing one or an estimated one from aggregating trajectory data. The second policy creates first a set of green split plans to be then selected by a control logic. This second policy is intended to be used in SCATS-like systems where signal plans are limited to a pre-defined discrete set. We propose a plan selection logics or alternatively, the original plan selection policy can be used as well. Both policies are tested in the field, achieving a significant reduction in delay, oversaturation and spillover ratios. Lastly, we test an application of this policy as an enhancement of SCATS systems in the presence of malfunctioning physical detectors.

Applied Photometry: Review of Retrospective and Reality Part 1: Candela and Watt, from Incandescent Lamp to LED

Photometry is the science of measuring light, which began with a visual comparison of the light of stars, the moon, and the sun with the light of a candle. Today it is a part of optical radiometry, inextricably linked with physical radiation detectors, taking into account the fact that light is actinic radiation, and physical detectors must be actinic in the case of integral measurements. At the same time, optical radiometry has introduced and introducing new technologies into spectral radiometric measurement methods, which, in turn, make it possible to solve the problems of measuring the parameters of actinic radiation by objective physical methods of spectral radiometry, taking into account the weight functions of actinic radiation. The choice between integral and spectral methods is determined by: – The required accuracy of the measurement task to be solved; – Knowledge of the weight functions of actinic radiation; – The quality of correction of radiation receivers for the weight functions of actinic radiation; – Methods for calibrating radiation receivers; – Actinic coefficients of optical radiation receivers (correction coefficients) and the accuracy of their determination; – Technical capabilities of spectral equipment; – Methods and means of spectrometers calibration; – Software for spectral measuring instruments, – Comparative analysis of the accuracy (uncertainty) of measurements by the spectral and integral methods in each individual case. The replacement of traditional sources of general lighting and signaling by radiation sources with LEDs, which have a spectral distribution of light emitting characteristics different from traditional light sources, has raised a number of issues in the metrological support of optical radiometry and applied photometry related to the oneness of the reproduction of units of energy and photometric quantities based on Watt.

Deep Transferable Intelligence for Spatial Variability Characterization and Data-Efficient Learning in Biomechanical Measurement

Biomechanical measurement is of promising value for rehabilitation, assisted-living, and lifestyle management applications. Nevertheless, the understanding is still limited on the spatial variability of biomechanical dynamics that is essential for optimal motion sensor configuration. Besides, training physical activity detectors is usually data-heavy and time-consuming. Targeting these two challenges, in this study, we propose a novel deep transfer intelligence framework, which leverages deep learning to characterize spatial variability of different motion sensors on diverse body locations, and further leverages inter-subject transfer learning to maximize data-efficiency in challenging scarce data learning. More specifically, to characterize the spatial variability, we propose deep convolutional neural networks to investigate capabilities of both different sensor locations and channels, on physical activity measurement. The characterization determines both optimal sensor configuration and optimal channel configuration. Further, we propose a transfer learning approach to mine inter-subject similarity and then share learned knowledge among subjects, thereby minimizing the training effort and maximizing the data-efficiency in the wearable scarce data learning scenario. Our evaluation experiments have determined the optimal sensor location from seven options as thigh, and the optimal sensor&channel configuration from 42 options as thigh-accelerometer-axis-Y. Our experiments have further demonstrated that, with transfer learning under the optimal sensor&channel configuration, only 10% of data from the target subject for model fine-tuning can yield a physical activity detection accuracy up to 91.6%, with a performance boosting of 9% compared with direct learning without transfer learning. Therefore, the deep transferable learning framework will greatly advance spatial variability characterization for optimal sensor and channel configuration, and efficient scarce data learning in biomedical measurement.

Sensitivity of Gaussian energy broadening function of MCNP pulse height spectra on CLYC7 scintillation detector

The Cs2LiYCl6:Ce crystal (CLYC) is an inorganic scintillator which has been developed for the γ-ray and neutron measurement with the high detection efficiency, high resolution, and no need unfolding technique. To enhance the measurement of the fast neutron, the CLYC with 7Li-enrichment (CLYC7) scintillator is developed. In this work, the response of the CLYC7 detector to γ-ray is obtained using 137Cs γ-ray calibration source and calculated using Monte Carlo N-Particle transport code (MCNP). A comparison of measured and calculated γ-rays spectra is complicated by the fact that physical radiation detectors have finite energy resolution. In this study, we treated detector energy resolution effect by Gaussian energy broadening (GEB) in MCNP pulse height spectra calculation. We observe the parameters in the GEB function which provides simulation spectrum matches the experiment spectrum, especially on the photopeak region. The detail sensitivity of GEB function on CLYC7 scintillation detector is presented in this work.

Detection-time distribution for several quantum particles

We address the question of how to compute the probability distribution of the time at which a detector clicks in the situation of $n$ nonrelativistic quantum particles in a volume $\mathrm{\ensuremath{\Omega}}\ensuremath{\subset}{\mathbb{R}}^{3}$ in physical space and detectors placed along the boundary $\ensuremath{\partial}\mathrm{\ensuremath{\Omega}}$ of $\mathrm{\ensuremath{\Omega}}$. We previously [Tumulka, Ann. Phys. (NY) 442, 168910 (2022)] argued in favor of a rule for the one-particle case that involves a Schr\"odinger equation with an absorbing boundary condition on $\ensuremath{\partial}\mathrm{\ensuremath{\Omega}}$ introduced by Werner; we call this rule the ``absorbing boundary rule.'' Here, we describe the natural extension of the absorbing boundary rule to the $n$-particle case. A key element of this extension is that, upon a detection event, the wave function gets collapsed by inserting the detected position, at the time of detection, into the wave function, thus yielding a wave function of $n\ensuremath{-}1$ particles. We also describe an extension of the absorbing boundary rule to the case of moving detectors.

Sensing Qualia.

Accounting for qualia in the natural world is a difficult business, and it is worth understanding why. A close examination of several theories of mind—Behaviorism, Identity Theory, Functionalism, and Integrated Information Theory—will be discussed, revealing shortcomings for these theories in explaining the contents of conscious experience: qualia. It will be argued that in order to overcome the main difficulty of these theories the senses should be interpreted as physical detectors. A new theory, Grounded Functionalism, will be proposed, which retains multiple realizability while allowing for a scientifically based approach toward accounting for qualia in the natural world.

Convex Quantization Preserves Logconcavity

A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization. Quantization is often disregarded when writing likelihood models, ignoring the limitations of the physical detectors used to collect the data. These two facts call for the question: would including quantization in likelihood models preclude logconcavity? are the true data likelihoods logconcave? We provide a general proof that the same simple assumption that leads to logconcave continuous-data likelihoods also leads to logconcave quantized-data likelihoods, provided that convex quantization regions are used.

Measurements in QFT: Weakly coupled local particle detectors and entanglement harvesting

We present a comparison of the AQFT-based Fewster-Verch framework with the Unruh-DeWitt particle detector models commonly employed in relativistic quantum information and QFT in curved space. We use this comparison to respond to a recent paper [arXiv:2103.13400] in which it was argued that the Reeh-Schlieder theorem prevents weakly coupled local particle detectors from harvesting vacuum entanglement from a quantum field. Their claim can be traced back to the mixedness that a local particle detector cannot escape from because of the entanglement that it must have with the quantum fields outside of the localization area. We argue that for any realistic scale of localization for physical particle detectors the effect of that mixedness is negligible, and that weakly coupled localized particle detectors are not impeded from harvesting vacuum entanglement.

Perspectives, Measurability and Effects of Non-Contact Biofield-Based Practices: A Narrative Review of Quantitative Research.

Practices such as “Reiki”, therapeutic touch, healing touch, and external “Qigong” have been regarded as some form of “energy medicine” or “biofield therapy”. The biofield concept has been studied and debated by researchers of distinct areas of expertise, and although the phenomenon was sometimes described as physically related to electromagnetics, other factors such as “subtle energy” and focused intention might be involved. These nonconventional practices integrate contact and non-contact techniques, and those dealing with so-called distant healing interventions are perhaps the most difficult to understand and accept. Practitioners describe these so-called nonlocal interventions as involving intentional factors and particular states of consciousness. With a spiritual mindset and a particular state of awareness, compassion is said to work out as a catalyst to produce physiological and physical changes through mechanisms that are still unknown. At the body level, these vegetative changes might be related to individual self-perception variations as part of the body neurovegetative feedback system of regulation. Further mechanisms are difficult to document and measure, and might be more accessible to research by using physical signal detectors, chemical dynamics methods, detectors using biological materials, detectors using living sensors, and detectors using the human body. The growing interest in these practices and the considerable amount of research exploring their effects and clinical applications encouraged this narrative review, which aims to provide an easy to consult partial overview of the history, theory and findings of quantitative research strategies exploring non-contact biofield-based practices. This work also aims to stimulate the reader’s mind with the raised hypotheses, catalyzing further research on the subject to confirm or deny the reported outcomes.

Light-ray operators, detectors and gravitational event shapes

Light-ray operators naturally arise from integrating Einstein equations at null infinity along the light-cone time. We associate light-ray operators to physical detectors on the celestial sphere and we provide explicit expressions in perturbation theory for their hard modes using the steepest descent technique. We then study their algebra in generic 4-dimensional QFTs of massless particles with integer spin, comparing with complexified Cordova-Shao algebra. For the case of gravity, the Bondi news squared term provides an extension of the ANEC operator at infinity to a shear-inclusive ANEC, which as a quantum operator gives the energy of all quanta of radiation in a particular direction on the sky. We finally provide a direct connection of the action of the shear-inclusive ANEC with detector event shapes and we study infrared-safe gravitational wave event shapes produced in the scattering of massive compact objects, computing the energy flux at infinity in the classical limit at leading order in the soft expansion.

Security proof of practical quantum key distribution with detection-efficiency mismatch

Quantum key distribution (QKD) protocols with threshold detectors are driving high-performance QKD demonstrations. The corresponding security proofs usually assume that all physical detectors have the same detection efficiency. However, the efficiencies of the detectors used in practice might show a mismatch depending on the manufacturing and setup of these detectors. A mismatch can also be induced as the different spatial-temporal modes of an incoming signal might couple differently to a detector. Here we develop a method that allows to provide security proofs without the usual assumption. Our method can take the detection-efficiency mismatch into account without having to restrict the attack strategy of the adversary. Especially, we do not rely on any photon-number cut-off of incoming signals such that our security proof is directly applicable to practical situations. We illustrate our method for a receiver that is designed for polarization encoding and is sensitive to a number of spatial-temporal modes. In our detector model, the absence of quantum interference between any pair of spatial-temporal modes is assumed. For a QKD protocol with this detector model, we can perform a security proof with characterized efficiency mismatch and without photon-number cut-off assumption. Our method also shows that in the absence of efficiency mismatch in our detector model, the key rate increases if the loss due to detection inefficiency is assumed to be outside of the adversary's control, as compared to the view where for a security proof this loss is attributed to the action of the adversary.

Non-scalable functions as biological object boundary models

This article considers the possibility of creating an algorithm for universal image preprocessing that would simplify its segmentation and is suitable for any type of medical or biological study methods. The authors propose a number of equally applicable functions that describe the changes in the parameters of a raster image along the normal to the border of an arbitrary biological object and at the same time doesn’t depend on the instrumental method of image acquisition. The work also determines the absence of any parametric adaptation as the boundary condition for reducing the subjective contribution to the final scientific and practical result. Methods. Based on the biological foundations, the following three simple mathematical expressions were chosen for changing brightness on the border of a biological object: All the functions participating in calculations were not scaled, their size corresponded to the coordinates expressed as raster image dots. The Pearson’s correlation without reliability assessment was used to describe the probabilities of the object’s border being present in the coordinate x=0, and to assess its affinity with properties of the proposed synthetic functions. The basis of functions was tested using digital images of three fundamentally-different objects obtained with different physical detectors: sagittal section of the MRI scan of human head capturing the hippocampus area (Siemens Magnetom Skyra, Germany); optical section of eye cornea (Pentacam, Oculus, Japan); image of transverse histological section of human skin stained with hematoxylin and eosin obtained with microscope (Olympus BX65, Olympus, Japan). In addition, practical evaluation of the applicability of the developed approach was done by processing 100 two-dimensional images of different arbitrarily selected brain MRI sections. A field of probability of the presence of “biological border” in each dot of the image was built for each image. Results. The best affinity — with significant outperformance — to all kinds of digital images of biological objects’ borders was found in the compound function described by the formula. Its affinity to object’s borders on the MRI images was 0.87–0.99; for the borders of biological objects on the images obtained with optical methods it was lower than 0.99 only in two out of seven tests (0.95 and 0.97). Discussion. The high affinity of the proposed compound function may be associated with the fact that the three members of this synthetic expression correspond to three individual additive physical phenomena manifesting its biological basis. The first member is responsible for general biological contrast differentiation — the delineation of tissues appears sharpest at the borders of the organs. The second member describes the conventional membrane present in all healthy organized biological structures, including epithelial lining of the esophagus, muscle fascia, developed mucous membrane etc. The third member of the proposed function characterizes the blurred junction of arbitrarily-uniform structures. The proposed mathematical apparatus describes the formation of scalar field of probabilities for selected borders of biological structures and can easily be transformed into vector representation. Additional advantage of the developed and tested mathematical apparatus is the fact that the formulated approach can be used for determining the orientation of brightness increment gradient. This, in turn, allows distinguishing additional properties of biological objects based on calculation of the direction of development and growth of the tissue and structural anisotropy.

Hot electrons in a nanowire hard X-ray detector

Nanowire chip-based electrical and optical devices such as biochemical sensors, physical detectors, or light emitters combine outstanding functionality with a small footprint, reducing expensive material and energy consumption. The core functionality of many nanowire-based devices is embedded in their p-n junctions. To fully unleash their potential, such nanowire-based devices require – besides a high performance – stability and reliability. Here, we report on an axial p-n junction GaAs nanowire X-ray detector that enables ultra-high spatial resolution (~200 nm) compared to micron scale conventional ones. In-operando X-ray analytical techniques based on a focused synchrotron X-ray nanobeam allow probing the internal electrical field and observing hot electron effects at the nanoscale. Finally, we study device stability and find a selective hot electron induced oxidization in the n-doped segment of the p-n junction. Our findings demonstrate capabilities and limitations of p-n junction nanowires, providing insight for further improvement and eventual integration into on-chip devices.

Ensemble of neural networks for 3D position estimation in monolithic PET detectors

We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photoelectric interactions in monolithic detectors. The ensemble is trained with data generated from optical Monte Carlo simulations only. The originality of our approach is to exploit simulations to obtain reference data, in combination with a variability reduction that the network ensembles offer, thus, removing the need of extensive per-detector calibration measurements. This procedure delivers an ensemble valid for any detector of the same design. We show the capability of the ensemble to solve the 3D positioning problem through testing four different detector designs with Monte Carlo data, measurements from physical detectors and reconstructed images from the MindView scanner. Network ensembles allow the detector to achieve a 2–2.4 mm FWHM, depending on its design, and the associated reconstructed images present improved SNR, CNR and SSIM when compared to those based on the MindView built-in positioning algorithm.

Afferent nerve fibres in the wall of the rat urinary bladder.

Structure and distribution of afferent nerve fibres in the rat bladder were studied by fluorescence microscopy after selective staining with antibodies against neuropeptide CGRP. Afferent fibres are very abundant (by comparison with other viscera) and interconnected in all bladder parts: muscle, urothelium, connective tissue, blood vessels, serosa. Their highest concentration is beneath the urothelium in equatorial and caudal regions, where they form a plexus, while individually maintaining a tree-like structure with innumerable branches running without preferential orientation. In cranial regions, mucosal afferent fibres become rare or absent. Abundant fibres are found in the detrusor, within each muscle bundle, with long strings of varicosities parallel to muscle cells. Afferent fibres, invariably varicose over hundreds of micrometres of their terminal parts and while still branching, comprise chains of hundreds of varicosities. Varicosities are irregular in size, frequency and separation, without specialised terminal structures around them, or within or around the fibre's ending. The possibility that varicosities are transduction points for sensory inputs is discussed, with the implication of a process taking place over considerable length in each branch of each fibre. Interconnectedness of afferent nerves of various bladder tissues, distribution of varicosities over hundreds of micrometres along axonal branches, absence of clear target structures for the fibres, apparent irregularity in the size and sequence of varicosities suggest an innervation that is not rigidly wired with distinct sensory pathways. In fact, the structural evidence suggests extensive afferent integration at the periphery, with wide distribution of source points and broad range of physical detectors.

Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

Signal offset optimization recently has been shown to be feasible with vehicle trajectory data at low levels of market penetration. Offset optimization was performed on two corridors with that type of data. A proposed procedure called “virtual detection” was used to process 6 weeks of trajectory splines and create vehicle arrival profiles for two corridors, comprising 25 signalized intersections. After data were processed and filtered, penetration rates between 0.09% and 0.80% were observed, with variations by approach. Then those arrival profiles were compared statistically with those measured with physical detectors, and most approaches showed statistically significant goodness of fit at a 90% confidence level. Finally, the arrival profiles created with virtual detection were used to optimize offsets and compared with a solution derived from arrival profiles obtained with physical detectors. Results demonstrate that virtual detection can produce good-quality offsets with current market penetration rates of probe data. In addition, a sensitivity analysis of the sampling period indicated that 2 weeks may be sufficient for data collection at current penetration rates.

Monte Carlo Latching Studies of Prompt-Gamma Detection During Hadrontherapy

Proton and carbon therapies of a human brain were simulated using FLUKA, with particular emphasis on treatment monitoring using the prompt gamma method where activities of the source proton/carbon in the patient’s body are to be extracted from the positional profile of exit photons. Tissue heterogeneity ranging from hydrogen to zinc was represented in a VIP-Man anthropomorphic voxel phantom. Each photon exiting the head was scored with ancestry attributes such as the particle type of its parent, the originating collision type and site. These data, not accessible via physical detectors, were analysed to characterise the escaping photons, delineating signal from noise according to the gamma origin and scatter history. Combinations of energy, geometry, time and angle filters, aided by raytracing, were studied for the optimal compromise between a sufficiently featured exit profile and a statistically adequate count. These will be put in context with ongoing research in the field; our perspective for the prompt gamma method will be discussed in detail.

Developments and trends in bioequivalent dosimetry.

Significant progress in radiobiology has refined the understanding of radiation-induced biological response at the cellular level and challenged the conventional application of a macroscopic description of radiation action to dosimetry in favour of a microscopic approach. Pioneering experiments, which investigated the stochastics of energy deposition from ionising radiations in volumes of cellular dimensions, contributed to the recognition of microdosimetry as a new scientific discipline. The first quantitative applications of Monte Carlo track structure simulations in radiobiology, however, supported evidence for target sizes of particular biological importance being in the nanometre regime. Bioequivalent dosimetry attempts to link particular features of the response of physical detectors with biological endpoints, exploiting clusters of multiple ionisations within nanometre scales in solid-state, gas- and water-filled devices. This approach supports the continued development of new concepts and quantities in radiation protection to permit evaluation of the biological effectiveness of radiations of different quality independently of dose and dose rate.