Realistic Detector Research Articles

Simulations and analysis tools for charge-exchange (d, 2He) reactions in inverse kinematics with the AT-TPC

Charge-exchange (d,2He) reactions in inverse kinematics at intermediate energies are a very promising method to investigate the Gamow–Teller transition strength in unstable nuclei. A simulation and analysis software based on the attpcroot package was developed to study this type of reactions with the active-target time projection chamber (AT-TPC). The simulation routines provide a realistic detector response that can be used to understand and benchmark experimental data. Analysis tools and correction routines can be developed and tested from simulations in attpcroot, because they are processed in the same way as the real data. In particular, we study the feasibility of using coincidences with beam-like particles to unambiguously identify the (d,2He) reaction channel, and to develop a kinematic fitting routine for future applications. More technically, the impact of space-charge effects in the track reconstruction, and a possible correction method are investigated in detail. This analysis and simulation package constitutes an essential part of the software development for the fast-beams program with the AT-TPC.

Ensemble Models for Calorimeter Simulations

Foreseen increasing demand for simulations of particle transport through detectors in High Energy Physics motivated the search for faster alternatives to Monte Carlo-based simulations. Deep learning approaches provide promising results in terms of speed up and accuracy, among which generative adversarial networks (GANs) appear to be particularly successful in reproducing realistic detector data. However, the GANs tend to suffer from different issues such as not reproducing the full variability of the training data, missing modes problem, and unstable convergence. Various ensemble techniques applied to image generation proved that these issues can be moderated either by deploying multiple generators or multiple discriminators. This work follows a development of a GAN with two-dimensional convolutions that reproduces 3D images of an electromagnetic calorimeter. We build on top of this model and construct an ensemble of generators. With each new generator, the ensemble shows better agreement with the Monte Carlo images in terms of shower shapes and the sampling fraction.

Optical link acquisition for the LISA mission with in-field pointing architecture

We present a comprehensive simulation of the spatial acquisition of optical links for the LISA mission in the in-field pointing architecture, where a fast pointing mirror is used to move the line-of-sight of the optical transceiver, which was studied as an alternative scheme to the baselined telescope pointing architecture. The simulation includes a representative model of the far-field intensity distribution and the beam detection process using a realistic detector model, and a model of the expected platform jitter for two alternative control modes with different associated jitter spectra. For optimally adjusted detector settings and accounting for the actual far-field beam profile, we investigate the dependency of acquisition performance on the jitter spectrum and the track-width of the search spiral, while scan speed and detector integration time are varied over several orders of magnitude. Results show a strong dependency of the probability for acquisition failure on the width of the auto-correlation function of the jitter spectrum, which we compare to predictions of analytical models. Depending on the choice of scan speed, three different regimes may be entered which differ in failure probability by several orders of magnitude. We then use these results to optimize the acquisition architecture for the given jitter spectra with respect to failure rate and overall duration, concluding that the full constellation could be acquired on average in less than one minute. Our method and findings can be applied to any other space mission using a fine-steering mirror for link acquisition.

Impact of nuclear effects in energy reconstruction methods on sensitivity of neutrino oscillation parameters at NOνA experiment

Long baseline (LBL) neutrino experiments aim to measure the neutrino oscillation parameters to high precision. These experiments use nuclear targets for neutrino scattering and hence are inflicted with complexities of nuclear effects. Nuclear effects and their percolation into sensitivity measurement of neutrino oscillations parameters are not yet fully understood and therefore need to be dealt with carefully. In a recent work [1], we reported some results on this for NOνA experiment using the kinematic method of neutrino energy reconstruction, where it was observed that the nuclear effects are important in sensitivity analysis, and inclusion of realistic detector set up specifications increases uncertainty in this analysis as compared to ideal detector case. Precise reconstruction of neutrino energy is an important component in the extraction of oscillation parameters, and it is required that the neutrino energy is reconstructed with very high precision. With this motivation, in this work, we use two methods of neutrino energy reconstruction - kinematic and calorimetric, including the nuclear effects, and study their impact on sensitivity analysis. We consider nuclear interactions such as RPA and 2p2h and compare two energy reconstruction methods with reference to events generation, measurement of neutrino oscillation parameters Δm322 and θ23 for (νμ→νμ) disappearance channel, mass hierarchy sensitivity, and CP-violation sensitivity for (νμ→νe) appearance channel of the NOνA experiment. It is observed that with an ideal detector setup, the kinematic method shows significant dependence on nuclear effects, as compared to the calorimetric method. We also investigate the impact of realistic detector setup for NOνA in these two methods (with nuclear effects) and find that the calorimetric method shows more bias (uncertainty increases) in sensitivity contours, as compared to the kinematic method. This is found to be true for both the mass hierarchies and for both neutrino and antineutrino incoming beams. The results of this study can offer useful insights into future neutrino oscillation analysis at LBL experiments (including leptonic CP phase measurement), especially when we are in the precision measurement era for the same, particularly in the context of some nuclear effects and energy reconstruction methods.

Uncertainties in neutrino oscillation parameter sensitivity due to resonance processes at NOνA

The long baseline (LBL) neutrino experiments use heavy nuclear targets for neutrino scattering in which nuclear effects give rise to complications in measuring the neutrino oscillation parameters up to high precision. These nuclear effects are not yet fully understood and therefore need to be quantified as they contribute to the systematic uncertainties. Precision reconstruction of neutrino energy is one of the main components in measuring the oscillation parameters, and it is required that the neutrino energy is reconstructed with very high precision. In this work, we investigate the effects of the resonance (RES) interaction process using two models for carbon target for [Formula: see text] disappearance channel of the NO[Formula: see text]A experiment, on neutrino-nucleus scattering cross-section, events, and neutrino oscillation parameter sensitivity. We also incorporate the realistic detector specifications of NO[Formula: see text]A. To quantify the systematic uncertainties due to RES interactions, we compare the cross-sections, events, and sensitivity analysis for two models of RES processes—Rein–Sehgal (RS) and Berger–Sehgal (BS), and comment on which model produces more precision. We observe that RES processes contribute significantly, and should be included carefully in models while extracting neutrino oscillation parameters.

Expected resolution limits of x-ray free-electron laser single-particle imaging for realistic source and detector properties.

The unprecedented intensity of x-ray free-electron laser sources has enabled single-particle x-ray diffraction imaging (SPI) of various biological specimens in both two-dimensional projection and three dimensions (3D). The potential of studying protein dynamics in their native conditions, without crystallization or chemical staining, has encouraged researchers to aim for increasingly higher resolutions with this technique. The currently achievable resolution of SPI is limited to the sub-10 nanometer range, mainly due to background effects, such as instrumental noise and parasitic scattering from the carrier gas used for sample delivery. Recent theoretical studies have quantified the effects of x-ray pulse parameters, as well as the required number of diffraction patterns to achieve a certain resolution, in a 3D reconstruction, although the effects of detector noise and the random particle orientation in each diffraction snapshot were not taken into account. In this work, we show these shortcomings and address limitations on achievable image resolution imposed by the adaptive gain integrating pixel detector noise.

Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals

Humans can portray different expressions contrary to their emotional state of mind. Therefore, it is difficult to judge humans’ real emotional state simply by judging their physical appearance. Although researchers are working on facial expressions analysis, voice recognition, and gesture recognition; the accuracy levels of such analysis are much less and the results are not reliable. Hence, it becomes vital to have realistic emotion detector. Electroencephalogram (EEG) signals remain neutral to the external appearance and behavior of the human and help in ensuring accurate analysis of the state of mind. The EEG signals from various electrodes in different scalp regions are studied for performance. Hence, EEG has gained attention over time to obtain accurate results for the classification of emotional states in human beings for human–machine interaction as well as to design a program where an individual could perform a self-analysis of his emotional state. In the proposed scheme, we extract power spectral densities of multivariate EEG signals from different sections of the brain. From the extracted power spectral density (PSD), the features which provide a better feature for classification are selected and classified using long short-term memory (LSTM) and bi-directional long short-term memory (Bi-LSTM). The 2-D emotion model considered for the classification of frontal, parietal, temporal, and occipital is studied. The region-based classification is performed by considering positive and negative emotions. The performance accuracy of our previous model’s results of artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (K-NN), and LSTM was compared and 94.95% accuracy was received using Bi-LSTM considering four prefrontal electrodes.

Realistic photon-number resolution in generalized Hong-Ou-Mandel experiment

We consider realistic photodetection in a generalization of the Hong-Ou-Mandel experiment to the multimode case. The basic layout of this experiment underlies boson sampling—a promising model of nonuniversal quantum computations. Peculiarities of photocounting probabilities in such an experiment witness important nonclassical properties of electromagnetic field related to indistinguishability of boson particles. In practice, these probabilities are changed from their theoretical values due to the imperfect ability of realistic detectors to distinguish numbers of bunched photons. We derive analytical expressions for photocounting distributions in the generalized Hong-Ou-Mandel experiment for the case of realistic photon-number resolving (PNR) detectors. It is shown that probabilities of properly postselected events are proportional to probabilities obtained for perfect PNR detectors. Our results are illustrated with examples of arrays of on/off detectors and detectors affected by a finite dead time.

A substandard candle: the low-nu method at few-GeV neutrino energies

As accelerator-based neutrino oscillation experiments improve oscillation parameter constraints with more data, control over systematic uncertainties on the incoming neutrino flux and interaction models is increasingly important. The intense beams offered by modern experiments permit a variety of options to constrain the flux using in situ “standard candle” measurements. These standard candles must use very well understood interaction processes to avoid introducing additional interaction model dependence. One option often discussed in this context is the “low-nu ” method, which is designed to isolate neutrino interactions where there is low energy-transfer to the nucleus, such that the interaction cross section is expected to be approximately constant as a function of neutrino energy. The shape of the low-energy transfer event sample can then be used to extract the flux shape. Applications of the method at high neutrino energies (many tens of GeV) are well understood. However, the applicability of the method at the lower energies of current and future few-GeV accelerator neutrino experiments remains unclear due to the presence of nuclear and form-factor effects inherent in the interaction models.In this analysis we examine the prospects for improving constraints on the accelerator neutrino fluxes in situ with the low-nu method in an experiment-independent way, using (anti)neutrino interactions on argon and hydrocarbon targets from the GENIE, NEUT, NuWro and GiBUU event generators. We begin by investigating the extent to which deviations from the constant cross-section assumption are dependent on poorly understood aspects of the neutrino interaction model. We then assess whether a low energy-transfer event sample can be confidently identified using experimentally accessible observables. We finally consider how the practicalities of reconstructing the energy spectrum of interacting neutrinos in realistic detectors might further limit the utility of low-nu flux constraints. The results show that flux constraints from the low-nu method would be severely dependent on the interaction model assumptions used in an analysis of neutrinos with energies below 5 GeV, and anti-neutrinos below at least 15 GeV. The spread of model predictions show that a low-nu analysis is unlikely to offer much improvement on typical neutrino flux uncertainties, even with a perfect detector. Notably—running counter to the assumption inherent to the low-nu method—the model-dependence increases with decreasing energy transfer for experiments in the few-GeV region.

Unitarity constraints and collider searches for dark photons

Dark photons are predicted by various new physics models, and are being intensively studied in a variety of experiments. In the first part of this paper, we obtain partial wave unitarity constraints on the dark photon parameter space from the allowed $VV\rightarrow VV$ scattering processes in the limit of large center-of-mass energy, where $V=W,Z$. In the second part of the paper, searches are performed using the expected differential rates with a realistic detector simulation including a comprehensive set of background processes on dilepton and dilepton plus a photon events at the High Luminosity LHC. In these searches, sensitive differential distributions are used in an optimized way to determine the sensitivity to dark photon parameter space. It is shown that remarkable sensitivity to the dark photon model is achieved and kinetic mixing strength can be probed down to $(1.4-10)\times 10^{-4}$ for dark photon mass between $15$ GeV to $2$ TeV. We also investigate the sensitivity of a future muon collider suggested by the Muon Accelerator Program (MAP) to the dark photon model at different center-of-mass energies. It is shown that a future muon collider is able to reach a sensitivity to kinetic mixing at the order of $10^{-4}$.

Signal processing and noise analysis on realistic radiation detector model

In this paper, equations for signal processing and noise analysis were derived for a realistic radiation detector model, and simulations were performed under various conditions. Realistic radiation detector model was composed of silicon PIN diode detector, Charge Sensitive Amplifier (CSA), and CR-RC2 shaper. For the realistic CSA model, a finite bandwidth model with a single pole response amplifier was presented and related equations were derived. Through realistic CSA model, it was possible to calculate the signal distortion phenomena such as charge transfer loss and ballistic deficit of the actual circuit and the noise value of the CSA output stage. For the realistic shaper model, signal and noise transfer functions were derived for the CR-RC2 circuit with non-inverting amplifier. In particular, the equation for the internal noise of the shaper, which has been ignored in the current noise analysis, was derived. Through this, it was possible to analyze the effects of amplifier characteristics, resistance value, and signal gain for each stage on the overall noise in the radiation detector of the CSA–shaper structure. For comparison with the simulation, a circuit equivalent to the realistic radiation detector model was made and measurements under the same conditions as the simulation were made. As a result of the verification experiment, the measured values matched well with simulations under various conditions, and through this, the validity of this model was verified. Signal processing and noise analysis were mainly performed for PCB circuits using commercial components, but also included content for CMOS front-end detectors. This model consists only of analytic formulas for the time domain function and the transfer function, nonetheless it has been shown to give accurate results.

Pile-up mitigation using attention

Particle production from secondary proton-proton collisions, commonly referred to as pile-up, impair the sensitivity of both new physics searches and precision measurements at large hadron collider (LHC) experiments. We propose a novel algorithm, Puma, for modeling pile-up with the help of deep neural networks based on sparse transformers. These attention mechanisms were developed for natural language processing but have become popular in other applications. In a realistic detector simulation, our method outperforms classical benchmark algorithms for pile-up mitigation in key observables. It provides a perspective for mitigating the effects of pile-up in the high luminosity era of the LHC, where up to 200 proton-proton collisions are expected to occur simultaneously.

Search for lepton-flavor-violating ALPs at a future muon collider and utilization of polarization-induced effects

Axion-Like Particles (ALPs) are pseudo Nambu-Goldstone bosons associated with spontaneously broken global U(1) symmetries. Such particles can have lepton-flavor-violating (LFV) couplings to the SM charged leptons. LFV ALPs provide the possibility to address some of the SM long-lasting problems. We investigate the sensitivity of a future muon collider suggested by the Muon Accelerator Program (MAP) to the production of LFV ALPs in the ALP mass range ma≤1 MeV. ALPs are assumed to be produced through the LFV decay τ→ℓa (ℓ=e,μ) of one of the tau leptons produced in the muon-anti muon annihilation. Performing a realistic detector simulation and deploying a multivariate technique, we constrain the LFV couplings cτe and cτμ for both the cases of unpolarized and polarized muon beams. Three different chiral structures are considered for the LFV ALP coupling and the muon collider is assumed to operate at the center-of-mass energies of 126, 350 and 1500 GeV. We present a procedure to search for LFV ALPs at colliders which takes advantage of tau polarization-induced effects. Polarized tau leptons which produce such effects can be produced when the initial muon beams are polarized. Utilizing the properties of polarized tau decays, the main SM background which overwhelms the ALP production in the case of unpolarized muon beams can be significantly suppressed. We present 95% CL expected limits on the LFV couplings and show that the present analysis can improve current experimental limits on the ALP LFV couplings by roughly one order of magnitude.

Discriminating same-mass neutron stars and black holes gravitational waveforms

Gravitational wave-forms from coalescences of binary black hole systems and binary neutron star systems with low tidal effects can hardly be distinguished if the two systems have similar masses. In the absence of discriminating power based on the gravitational wave-forms, the classification of sources into binary neutron stars, binary black holes and mixed systems containing a black hole and a neutron star can only be unambiguous when assuming the standard model of stellar evolution and using the fact that there exists a mass gap between neutron stars and black holes. This approach is however limited by its own assumptions: for instance the 2.6 solar mass object detected in the GW190814 event remains unclassified, and models of new physics can introduce new compact objects, like primordial black holes, which may have masses in the same range as neutron stars. Then, without an electromagnetic counterpart (kilonova), classifying mergers of compact objects without mass gap criteria remains a difficult task, unless the source is close enough. In what follows we investigate a procedure to discriminate a model between binary neutron star merger and primordial binary black hole merger by using a Bayes factor in simulated wave-forms that we superimpose to realistic detector noise.

Quantum Key Distribution with Nonideal Heterodyne Detection: Composable Security of Discrete-Modulation Continuous-Variable Protocols

Security proofs that incorporate physical constraints of realistic heterodyne detectors are presented, establishing a rigorous analysis of collective attacks in a continuous-variable quantum cryptography scheme.

Photon-counting CT via interleaved/gapped spectral channels: Feasibility and imaging performance.

Compared to energy integration, photon-counting x-ray detection facilitates the spectral channelization (energy binning) in spectral CT and thus offers the opportunity to implement data acquisition via sophisticated schemes, for example, gapping or interleaving in spectral channels. In this article, we report our investigation of the performance of material decomposition based spectral imaging in photon-counting CT implemented in such data acquisition schemes, and their comparison with the benchmark scheme and other schemes without spectral gapping or interleaving. Using a deliberately designed anthropomorphic head phantom that mimics the intracranial soft tissues and bony structures, a simulation study is carried out with the focus on two-material decomposition based spectral imaging in photon-counting computed tomography (CT), under both ideal and realistic detector spectral responses. The projection data are acquired in four spectral channels, and then are sorted to implement the schemes of gapping ((ch1 , ch3 ); (ch2 , ch4 ); (ch1 , ch4 )) and interleaving ((ch1 , ch3 ) + (ch2 , ch4 ); (ch1 , ch4 ) + (ch2 , ch3 ); ((ch1 + ch3 ), (ch2 + ch4 )); ((ch1 + ch4 ), (ch2 + ch3 ))) in spectral channels, in addition to the benchmark scheme ((ch1 + ch2 ), (ch3 + ch4 )) and other conventional schemes (ch1 , ch2 ), (ch2 , ch3 ) and (ch3 , ch4 ), where ''ch'' denotes channel, ''+'' denote addition, and (·,·) the operation of material decomposition and image reconstruction. Using the contrast-to-noise ratio between targeted regions of interest as the figure of merit, we study the performance of spectral imaging (material specific and virtual monochromatic) associated with these spectral channelization schemes. Under ideal detector spectral response, the scheme (ch1 , ch4 ) outperforms the benchmark scheme ((ch1 + ch2 ), (ch3 + ch4 )) and others in gapped and/or interleaved spectral channelization in material specific imaging, while the interleaved scheme (ch1 , ch4 ) + (ch2 , ch3 ) performs the best in virtual monochromatic imaging. Notably, only about half x-ray dose is utilized in the scheme (ch1 , ch4 ) for image formation. Under realistic detector spectral response, the difference in imaging performance over all spectral channelization schemes diminishes, along with degradation in each scheme's individual performance. The results suggest that (i) different strategy in spectral channelization may have to be exercised in material specific imaging and virtual monochromatic imaging, respectively, and (ii) the spectral distortion in realistic detector's response due to charge-sharing, Compton scattering, and fluorescent escaping should be mitigated as much as possible. The spectral channelization schemes and associated imaging performance reported herein are novel and thus informative to the community, which may further the understanding of physical fundamentals and design principles for material decomposition based spectral imaging in photon-counting CT and other x-ray related imaging modalities.

Discrete modulation continuous-variable measurement-device-independent quantum key distribution scheme based on realistic detector compensation

Discrete modulation continuous variable measurement device independent quantum key distribution scheme has good compatibility with efficient error correction codes, which leads to high reconciliation efficiency even at low signal-to-noise ratio. Besides, the implementation of this protocol is simpler than that of Gaussian modulation scheme. However, the quantum efficiency of homodyne detector commonly used in the experiment is only 0.6, which will seriously affect the practical application performance of discrete modulation continuous variable measurement device independent quantum key distribution scheme. To solve this problem, we propose a discrete modulation continuous variable measurement device independent quantum key distribution scheme based on realistic detector compensation. In our scheme, for the outputs of two quantum channels, each adopts a phase sensitive amplifier to compensate for the corresponding realistic homodyne detector. The simulation results show that the phase sensitive amplifier can well compensate for the quantum efficiency of the realistic detector and effectively improve the performance of the discrete modulation continuous variable measurement device independent quantum key distribution scheme with realistic detector in terms of secret key rate and secure transmission distance. The proposed protocol provides an effective method for promoting the practical development of the discrete modulation continuous variable measurement device independent quantum key distribution scheme.

Performance Evaluation of SOCA-CFAR Detectors in Weibull-Distributed Clutter Environments

In this letter, we derive a novel exact expression for the probability of false alarm (PFA) and an approximate closed-form solution for the probability of detection (PD) of a smallest of cell-averaging constant false alarm rate (SOCA-CFAR) detector operating over Weibull-distributed clutter. For the analysis, we consider an exponentially distributed target and allow arbitrary values for the shape parameter of clutter interference. To the best of our knowledge, there are no exact or approximate performance evaluations for an SOCA-CFAR detector considering arbitrary values for the shape parameter of the Weibull interference samples (i.e., different from 1) contained within the CFAR window. Therefore, our analytical derivations generalize previous performance evaluation studies and take a small step toward a better understanding of more realistic SOCA-CFAR detectors. Moreover, we obtain exact formulations for the probability density function (PDF) and the cumulative distribution function (CDF) for a minimum of two sums of independent and identically distributed (i.i.d.) Weibull random variables. Numerical results indicate that the system performance improves as the shape parameter of the Weibull interference increases. The validity of all our expressions is confirmed via Monte Carlo simulations.

A Geant4 tool for edge-illumination X-ray phase-contrast imaging

The PEPI project is developing a new experimental facility integrating a chromatic photon-counting detector within an edge-illumination (EI) phase-contrast setup. In this context, a novel Geant4-based simulation tool has been introduced with the aim of defining the optimal design of the experimental setup. The code includes a custom X-ray refraction process and allows simulating the whole EI system, comprising a polychromatic and extended source, absorbing masks, substrates, their movement during acquisition, and X-ray detection. In this paper, a realistic spectral detector model is introduced and its energy response validated against experimental data acquired with synchrotron radiation at energies between 26 and 50 keV. Moreover, refraction and transmission images of a plastic phantom are reconstructed from simulation data and successfully compared with theoretical predictions. Finally, an optimization study aiming at finding the effect of the X-ray focal spot size (i.e. spatial coherence) on image quality is presented; the results suggest that, in the considered configuration, the system can tolerate source sizes up to 30 μm, while, for a fixed exposure time, the best signal-to-noise ratio in refraction images is found for source sizes in the order of 10 to 15 μm.

Calorimetric Measurement of Multi-TeV Muons via Deep Regression

The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find improved solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momentum at very high energy, where an estimate of the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient for achieving good momentum resolution when detecting, e.g., a narrow, high mass resonance decaying to a muon pair. In this work we study the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by exploiting spatial information of the clusters of energy from radiated photons in a regression task. The use of a task-specific deep learning architecture based on convolutional layers allows us to treat the problem as one akin to image reconstruction, where images are constituted by the pattern of energy released in successive layers of the calorimeter. A measurement of muon energy with better than 20% relative resolution is shown to be achievable for ultra-TeV muons.