Gamma-ray Spectra Research Articles

Limits on compression of cosmic rays in supernova remnants

ABSTRACTThe spectral shape of the gamma-ray emission observed for dynamically old supernova remnants that interact with molecular clouds triggered an exciting scenario of adiabatic compression and farther re-acceleration of Galactic cosmic rays (GCRs) in radiative shells of the remnants, which was extensively discussed and applied to various sources over recent years. Indeed, the observed gamma-ray spectrum from a number of remnants strongly resembles the expected spectrum of the gamma-ray emission from the compressed population of GCRs. In the following we discuss the feasibility of this scenario and show that it is very unlikely that compressed GCRs could produce sufficient amount of gamma-rays and that the observed spectral shape is putting strong limits on the allowed compression factors. Further, absence of curvature in featureless power-law spectra of evolved supernova remnants at radio wavelengths is strongly disfavouring the compression scenario for electrons and hence for hadrons. Our calculations show that the contribution of compressed electrons to the observed radio flux could reach at most $\sim 10\,$ per cent.

Low-resolution gamma-ray spectrum analysis using comprehensive training set and deep ResNet architecture

Considering limitations of training sets and architectures in existing methods for gamma-ray spectrum analysis based on artificial neural networks, we propose a method for constructing a comprehensive training set and a modified ResNet architecture to perform low-resolution gamma-ray spectrum analysis. The constructed training set reproduces the shape feature diversity of spectra acquired from real detection scenarios by using various parameter settings in Monte Carlo simulations. The proposed ResNet constitutes the largest and deepest architecture ever applied to gamma-ray spectrum analysis, containing 51 layers and more than 107 parameters. Results from tests on simulated and measured spectra show that the key performance values of average precision and F1 score of the proposed network are substantially better than those of a convolutional neural network and a fully connected network. Moreover, our approach provides weak ray identification, fake peak discrimination, and overlapping peak resolution. This study demonstrates the feasibility of applying deep learning to gamma-ray spectrum analysis and introduces an approach to achieve general, accurate, sensitive, and reliable gamma-ray spectrum analysis.

High-Resolution Gamma-Ray Spectrometry of Pebble Bed Reactor Fuel Using Adaptive Digital Pulse Processing

In this work, an investigation was performed to assess the feasibility of passive gamma-ray spectrometry using adaptive digital pulse processing for online interrogation of pebble bed reactor (PBR) fuel. This work incorporates the physics of the radiation emission phenomenon with advanced pulse processing techniques to develop a high-resolution gamma-spectrometry system capable of handling ultrahigh count rates in various applications of nuclear science and technology. Computational modeling was used to simulate the irradiation of PBR fuel and to design the adaptive digital pulse processing–based gamma-ray spectrometry system. Monte Carlo simulations were performed to study the gamma-ray spectra of the PBR fuel and to perform coupled photon-electron transport analysis to calculate the pulse-height spectrum of PBR fuel. A Monte Carlo computer routine was used to predict the effect of pulse pileup at high-count-rate conditions. This code utilizes the random interval distribution function based on Poisson statistics to simulate the pileup behavior. Combined with pileup logic, a recursive trapezoid filter with adaptive shaping parameters was implemented to simulate the pileup behavior of a digital gamma-ray spectrometry system. The adaptive shaping algorithm selects the rise time of the trapezoid shaping filter based on the separation between the input pulses for each incoming signal. The simulation results using the proposed adaptive digital pulse processing demonstrated that with the improved energy resolution, the burnup information can be more accurately determined on a pebble-by-pebble basis as compared to fixed shaping, and tasks related to in-core fuel management, safeguards, and waste management become feasible to perform efficiently and accurately.

Constraints on dark matter scattering with long lived mediators from observations of the Sun with the Fermi Large Area Telescope

The Sun represents a promising target for indirect dark matter searches, as dark matter particles from the Galactic halo can be gravitationally trapped in its core or in external orbits, and their annihilations can lead to final states with standard model particles that are able to reach the Earth. In this work we have considered a scenario in which dark matter particles can annihilate into pairs of long-lived mediators, which in turn can escape from the Sun and decay into pairs of gamma rays or into the bb̅, τ + τ -, μ + μ - channels, with the production of gamma rays in the final states. All these processes are expected to yield an excess in the energy spectrum of gamma rays towards the Sun. We have therefore analyzed the data collected by the Fermi Large Area Telescope during its first 13.5 years of operation, searching for possible excesses in the solar gamma-ray spectrum. Since no statistically significant excess is found, we have set constraints on the dark matter-nucleon scattering cross sections in both the spin-dependent and spin-independent cases. For all the mediator decay channels explored and for dark matter masses between a few GeV/c2 and 1 TeV/c2, we have found that the upper limits on the spin-dependent and spin-independent cross sections are in the ranges from 10-45 to 10-39 cm2 and from 10-47 up to 10-42 cm2, respectively.

Artificial gamma ray spectra simulation using Generative Adversarial Networks (GANs) and Supervised Generative Networks (SGNs)

X-ray and gamma-ray spectral analysis is classically based on models of expected spectra that are compared with acquired data. The usual approach is to define a mathematical model of an instrument in a stable and constant environment. However, when the operating conditions are varied, unstable, when the instrument has multiple configurations or when the environment itself has an impact on the measurement, it becomes extremely difficult to make accurate models to represent those different conditions. In this context, new approaches based on Deep Neural Networks are developed for nuclear, medical and astrophysical applications to perform spectra analysis, with the advantage that they can handle a wide variety of acquisition conditions, provided that they are represented in the training database. Therefore, in order to train these models, it is utterly important to have high quality and representative data, usually based on simulation models. The main problem with these models is the trade-off between the quality of the simulated data used in training the network and the simulation time to generate that data. The problem lies in the time spent during Monte Carlo (MC) simulations, for instance using GEANT4 software, which can take weeks to simulate the complete instrumental response in a specific situation. In this paper, we address this issue by presenting an approach based on Deep Learning algorithms to generate MC data. We develop two deep neural network models, one based on Generative Adversarial Networks (GANs) and the other based on Supervised Generative Networks (SGNs). Once the networks are trained, we are able to generate in milliseconds, realistic spectral signatures of different radioelements and mixtures with the specifics response of our Caliste-HD detection system. The objective of this work is to study the feasibility of using this approach to provide a faster but still reliable alternative form of gamma spectra generation in order to train other models dedicated to Deep Neural spectra identification with high quality data, improving their performance and extending their domain of validity.

Research on Gamma Spectroscopy System While Drilling Based on DSP and FPGA

The traditional gamma energy spectrum measurement system performs filtering, shaping, amplification, baseline recovery, stacking discrimination, peak hold, and low-speed ADC sampling on the pulse signal output by the gamma-ray detector. It generates energy spectrum curves. There are problems such as large dead time and pulse signal accumulation. A high-speed digital processing chip (FPGA) and a high-speed ADC are used to perform full pulse sampling on the amplified ray pulses directly. The high-speed digital processing chip (DSP) directly realizes the pulse signal recognition, which remarkably improves the efficiency of signal acquisition and is very suitable for developing the drill gamma spectroscopy system. Scholars have studied the pulse digital Gaussian shaping algorithm and the trapezoidal shaping algorithm in gamma-spectroscopy while drilling to explore this phenomenon. In this case, a gamma-ray spectrum measurement while drilling system based on DSP and FPGA is designed. It realizes the functions of full-pulse high-speed sampling and energy spectrum curve generation for the narrow pulse signal input to the detector. It also converts the energy spectrum curve. It is transmitted to the host computer through the serial port for energy spectrum analysis.

Influence of the solar activity on the background of a high-resolution gamma-ray spectrometer

The count rate in the peak of a gamma-ray spectrum at 2223 keV was measured over a period of 25 years. The peak is produced by neutron capture on hydrogen, a constituent of the spectrometer's shield. Since the neutrons are produced by cosmic rays, the count rate in the peak is correlated with the solar activity via the interaction between the solar wind and the cosmic rays. The correlation between the total daily number of sunspots, as a measure of the solar activity, and the count rate in the peak was investigated as a function of the time shift between the time dependence of the count rate in the peak and the time dependence of the total daily number of sunspots. Variations of the correlation coefficient as a function of the shift are discussed in terms of phenomena occurring on the surface of the Sun. The variations indicate a long-term correlation, corresponding to the 11-year solar cycle, and a short-term correlation, corresponding to the sunspots.

Possible Origin of a Newly Discovered GeV Gamma-Ray Source Fermi J1242.5+3236

Based on the first 13.4 yr of Fermi science data in the energy range from 300 MeV to 500 GeV, we discovered a bright GeV gamma-ray source with a ∼5.64σ detection, named Fermi J1242.5+3236, which has an offset of about 0.°0996 from a nearby star-forming galaxy NGC 4631. When using the 12 yr’ data, the detection significance of Fermi J1242.5+3236 is about 4.72σ. Fermi J1242.5+3236 is a steady point source without significant temporal variability and has a hard gamma-ray photon index of about −1.60 ± 0.24. The spatial offset and the hard gamma-ray spectrum disfavor this source as the diffuse gamma-ray emission from this galaxy. This new source might have a possible origin of an unidentified background blazar, which is more likely a high-synchrotron-peaked blazar for its hard gamma-ray photon index. A follow-up optical observation would help distinguish origin of Fermi J1242.5+3236.

A highly scalable and autonomous spectroscopic radiation mapping system with resilient IoT detector units for dosimetry, safety and security

Technologies utilizing radiological materials across power generation, defence, industry, research and medicine have increased the global inventory of highly active and hazardous materials. Consequently, an amplified threat exists of illicitly obtained materials being used as part of hostile acts. The potential for intentional releases occurs alongside risks from natural disasters or facility accidents. In any such event, it is crucial to rapidly assess the release composition and extent of response and remediation activities. Therefore, the deployment of an effective, resilient and autonomous radiation monitoring network is pivotal both during and after an incident. Underpinning this assessment is a detailed understanding of the pre-event or background, radiation levels, the knowledge of which is also essential in assessing a population’s dosimetric exposure to, and impact from anthropogenic and naturally occurring/varying sources of ionizing radiation. Presented here is a fully operational cloud-based spectroscopic radiation mapping platform comprising IoT modules compatible with cellular networks, without modification, in over 180 countries. Combined with locally roaming vehicles, a continuous multi-pass radiological characterization of an urban environment was performed. Such IoT devices are deployable as either individual sensors for specific localized temporal events or integrated over a greater time period (and area) to represent a larger static sensor. Over several months of continued operation, more than 1000 000 individual location-referenced gamma-ray spectra were collected and securely uploaded, in real-time, to an online cloud database and automatically characterized via a custom multi-step workflow. Fine-scale local variations in the radiological fingerprint of a 1 km × 1 km urban area were subsequently rendered in near-real-time to an interactive secure online graphical dashboard for temporal, spatial and spectral interrogation by the user. Considerations for the automated ‘elastic’ handling of ever-expanding volumes of input data have been carried out, facilitating propagation and expansion of the system’s database without human input.

Identification of Pb–Zn ore under the condition of low count rate detection of slim hole based on PGNAA technology

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F1 score for both types of ores is greater than 0.9.

Prospective study on observations of gamma-ray emission from active galactic nuclei using the HADAR experiment

The High Altitude Detection of Astronomical Radiation (HADAR) experiment is a refracting terrestrial telescope array based on the atmospheric Cherenkov imaging technique. It is a hybrid array consisting of four water-lens telescopes and a surrounding scintillation detector array for observing Cherenkov light induced by 10 GeV–10 TeV cosmic rays and gamma rays in the atmosphere. The water-lens telescope mainly consists of a hemispherical lens with a diameter of 5 m acting as a Cherenkov light collector, a cylindrical metal tank with a 4 m radius and 7 m height, and an imaging system at the bottom of the tank. The sky region covered by HADAR is much larger than the current generation of Imaging Atmospheric Cherenkov Telescopes, and even the CTA. The field-of-view (FOV) of HADAR can reach up to 60 degrees. The HADAR experiment possesses the advantages of a large field-of-view and low energy threshold, so it can continuously scan wide portions of the sky and easily observe extragalactic gamma-ray sources. The majority of the extragalactic gamma-ray sources detected at very high energy (VHE) energies are active galactic nuclei (AGNs). In this study, we present the potential of using the HADAR experiment for detecting AGN. Based on the AGN catalog sources of the Fermi Large Area Telescope (Fermi-LAT), the observed energy is extrapolated to the VHE range. The VHE gamma rays propagating over cosmological distances can interact with the low-energy of the extragalactic background light (EBL) and produce electron-positron pairs. Therefore, we consider the absorption effects of different EBL models when calculating the expected gamma ray spectra of the AGN sample. We select the sample with redshift measurements and locations inside the FOV of HADAR from 4LAC catalog. In total, there are 375 BL Lacertae objects (BL Lacs) and 289 flat-spectrum radio quasars (FSRQs) satisfying the selection conditions. The integral gamma ray spectra are derived and compared with the sensitivity curve of HADAR, the number of sources with fluxes above the sensitivity of HADAR is counted. Further, we calculate the statistical significance of HADAR for AGN source observation based on the equi-zenith angle sky scanning analysis method. The simulation results reveal that a total of 31 sources of Fermi-LAT AGN can be detected by HADAR with a significance greater than five standard deviations over a one-year survey period, most of which are BL Lacs.

Latest results from the DAMPE space mission

The space-based DAMPE (DArk Matter Particle Explorer) particle detector has been taking data for more than 7 years since its successful launch in December 2015. Its main scientific goals include the indirect search for dark matter signatures in the cosmic lepton and gamma-ray spectra, the study of galactic cosmic rays up to energies of hundreds of TeV and studies on highenergy gamma ray astronomy. The measurement of galactic cosmic ray spectra are reported here, those being fundamental tools to investigate the mechanisms of acceleration at their sources and propagation through the interstellar medium. Results on proton and helium, which revealed new spectral features, are described. Ongoing analyses on the cosmic ray light, medium and heavy mass nuclei are outlined, together with studies of the so-called secondary cosmic rays. Latest results on gamma-ray astronomy and dark matter search will be also summarized.

Attaining the PeV frontier of the cosmic ray spectrum in space with HERD

The High Energy cosmic-Radiation Detection facility (HERD) is a calorimetric experiment planned to be launched in 2027. It will be operational for at least 10 years on board the China Space Station. With HERD we will measure the energy flux of cosmic protons and heavier nuclei from 30 GeV up to, for the first time in space, a few PeV. We will search for signatures of annihilation and decay products of dark matter in the energy spectrum of cosmic electrons and gamma rays from 10 GeV to 100 TeV. A wide field of view monitoring of the gamma-ray full-sky from 100 MeV will also be performed. The five HERD subdetectors, the calorimeter (CALO), the scintillating fiber tracker (FIT), the plastic scintillator detector (PSD), the silicon charge detector (SCD) and the transition radiation detector (TRD), are currently under development. The design, prospects and expected performance of HERD, as well as its contribution to the multimessenger astronomy will be presented in this contribution.

Transport of Cosmic-Ray Electrons from 1 au to the Sun

Gamma rays are produced by cosmic-ray (CR) protons interacting with the particles at the solar photosphere and by CR electrons and positrons (CRes) via inverse Compton scattering of solar photons. The former comes from the solar disk while the latter extends beyond the disk. Evaluation of these emissions requires the flux and spectrum of CRs in the vicinity of the Sun, while most observations provide flux and spectra near the Earth, at around 1 au from the Sun. Past estimates of the quiet Sun gamma-ray emission use phenomenological modulation procedures to estimate spectra near the Sun. We show that CRe transport in the inner heliosphere requires a kinetic approach and use a novel approximation to determine the variation of CRe flux and spectrum from 1 au to the Sun including the effects of (1) the structure of the large-scale magnetic field, (2) small scale turbulence in the solar wind from several in situ measurements, in particular, those by Parker Solar Probe that extend this information to 0.1 au, and (3) most importantly, energy losses due to synchrotron and inverse Compton processes. We present results on the flux and spectrum variation of CRes from 1 au to the Sun for several transport models. In forthcoming papers we will use these results for a more accurate estimate of quiet Sun inverse Compton gamma-ray spectra, and, for the first time, the spectra of extreme ultraviolet to hard X-ray photons produced by synchrotron emission. These can be compared with the quiet Sun gamma-ray observation by the Fermi and X-ray upper limits set by RHESSI.

A Study on the Gamma-ray Attenuation coefficient Parameters for Potassium Carbonate

The linear attenuation coefficient (LAC) and mass attenuation coefficient (MAC) are most important while studying of radiation, radio-isotopes in dosimentry and irradiation of materials. In the present work to calculate linear attenuation coefficient (LAC) and mass attenuation coefficient (MAC) values of different salts samples by using Gamma-ray spectrometry with Photo-Multiplier Tube (PMT) and Multi-Channel Analyzer (MCA). The 0.123, 0.511, 0.662, 1.17, 1.28 and 1.33MeV gamma energy rays used for the experiment in the interaction of Potassium carbonate salt samples and compared the Mass attenuation coefficient (MAC) values of salt samples. The obtained gamma-ray spectra were analyzed using computer software.

The contribution of unresolved sources on the gamma-ray spectrum of molecular clouds

Fermi-LAT observations of the large-scale diffuse gamma-ray emission unveiled that in some locations, towards the inner Galaxy, the spectrum of this component is harder than the local one, measured at the Earth position. If this signal is associated with the “truly” diffuse emission produced by the interaction of cosmic rays (CRs) with the interstellar medium (ISM) then its observed spectral features can be interpreted as indirect evidence of a CR spectral hardening toward the Galactic center. However, in order to correctly interpret the data, the contribution from unresolved sources has to be taken into account. Newly developed theoretical models showed that the cumulative flux produced by unresolved pulsar wind nebulae (PWNe) added to the “truly” diffuse emission significantly shapes the spectrum of the large-scale diffuse emission, challenging the CR spectral hardening hypothesis. In light of the recent results, we discuss the effect of unresolved PWNe on the observed spectra of the diffuse emission with a particular focus on molecular clouds (MCs). We analyze the influence of unresolved sources on clouds of different sizes and locations. Finally, we provide a prescription on how to choose the regions to target in order to have an unbiased determination of the “truly” diffuse emission.

Gamma-Ray Spectrum Measurement from Capture Reactions of Uranium-238 for Thermal and Resonance Energy Neutrons

γ-ray spectrum from the capture reaction of 238U is measured for the thermal and resonance energy neutrons at KURNS-LINAC facility. Prominent primary γ ray of 3.297 MeV listed in CapGam is not observed for the thermal energy neutrons. The measured γ-ray spectrum is varied with neutron energy so that we can expect neutron spectrum inducing 238U(n,γ) reactions in critical / subcritical cores could be deduced by the γ-ray spectroscopy for the cores. Preliminary deduced ratio of 4.060 MeV γ-ray emission to capture for the thermal energy neutron agrees with CapGam so that the deduction scheme is considered adequate.

Starburst Nuclei and Cosmic-Rays Transport Mechanisms: Future Opportunities for Neutrino Astronomy and Beyond Standard Model Studies

Experimental observations have demonstrated a strong correlation between star-forming processes and gamma-ray luminosities, giving strong hints about the nature of the Cosmic-Rays (CRs) transport mechanisms inside Starburst Nuclei. In this contribution, we discuss the imprints on nearby Starburst galaxies (SBGs) gamma-ray spectra left by different CR transport models, quantifying the potentiality of future telescopes to distinguish between them. We also investigate the possibility of constraining the properties of light Dark Matter (DM) particles exploiting the peculiar nature of CR transport inside SBNs. We show that the property of scattering between high-energy CRs and DM particles leads to observable features, thereby posing stringent constraints on the DM parameter space.

Computational spectrometry and dosimetry, of neutrons and γ rays, outside a subcritical nuclear reactor with water and polyethylene moderators

An external neutron source is necessary to start up and run a subcritical thermal nuclear reactor. The external neutron source can be a neutron generator, a radionuclide having spontaneous fission or an isotopic neutrons source. Turning off the neutron generator or removing the neutron source shuts down the reactor. In subcritical reactors for teaching, fuel tubes and items for irradiation can be inserted or removed even when the reactor is running because are radiological safe. The aim of this work was to estimate, using Monte Carlo methods, the neutron and γ-ray spectra at five sites outside a Subcritical thermal neutron reactor Nuclear Chicago model 9000 using water and polyethylene moderators. In addition, the ambient dose equivalent and the ambient dose, for neutrons and γ-rays were calculated. For neutrons, the ambient dose equivalent were calculated with the neutron fluence-to-ambient dose equivalent coefficients from the ICRP 74 and the neutron fluence-to-ambient dose response of the neutron area monitor LB 6411; while, the ambient dose was calculated using the ICRU 95 neutron fluence-to-ambient dose coefficients. For γ-rays, was calculated the ambient dose equivalent using the ICRP 74 photon fluence-to-ambient dose equivalent coefficients; while, the ambient dose was calculated with the photon fluence-to-ambient dose coefficients from the ICRU 95. Outside the reactor, neutron spectra have fast, epithermal and thermal neutrons, and gamma ray spectra have γ rays induced in the inelastic scattering and capture of neutrons with the reactor materials. Neutron and γ-ray fluence rates and dose rates are larger in the water-moderated reactor. The ambient dose rates, due to neutrons and γ-rays, are smaller than the ambient dose equivalent, because the ambient dose resembles the maximum effective dose.

Gamma-ray anomaly detector for airborne surveys based on a machine learning methodology

Machine learning methods were used to develop an automated classification model for detecting gamma-ray spectra with anomalous (i.e., non-background) signatures collected during airborne surveys. Spectra were preprocessed with an altitude-based normalization procedure, followed by application of a digital filter to remove baseline and noise effects. Segments of the preprocessed spectra were then used as input patterns to piecewise linear discriminant analysis (PLDA). For use in building the classification model, a training set was constructed based on two data classes: (1) spectra containing various radioisotope signatures and (2) background spectra. Through the use of a piecewise linear discriminant based on seven separating boundaries, a general-purpose spectral anomaly detector was developed for use in the automated screening of large spectral datasets collected during airborne surveys. The intended application of the methodology is to aid first responders in locating lost or stolen radioactive sources or in managing other incidents in which radioactive material is released into the environment. In developing and testing the methodology, laboratory spectra, spectra collected during airborne surveys, and mathematically synthesized spectra were used. When applied to 17 airborne surveys in which known radioisotope sources were present, the anomaly detector was able to locate each source with high confidence. While false detections were observed at a rate of 3.7%, many of these were in the vicinity of the known source location but the radioisotope signature could not be visually observed in the spectrum. For false detections away from the source locations, elevated signatures from naturally occurring background components were typically observed.