Source Shape Research Articles

Investigating the excitation function of HBT radii for Lévy-stable sources

Abstract Contemporary heavy-ion physics research aims to explore the phase diagram of strongly interacting matter and search for signs of the possible critical endpoint on the QCD phase diagram. Femtoscopy is among the important tools used for this endeavor; there have been indications that combinations of femtoscopic radii parameters (referred to as HBT radii for identical boson pairs) can be related to the system's emission duration. An apparent non-monotonic behavior in their excitation function thus might signal the location of the critical point. In this paper, we show that conclusions drawn from the results obtained with a Gaussian approximation for the pion source shape might be altered if one utilizes a more general Lévy-stable source description. We find that the characteristic size of the pion source function is strongly connected to the shape of the source and its possible power-law behavior. Taking this into account properly changes the observed behavior of the excitation function.

The Influence of Using a Seismically Inferred Magma Reservoir Geometry in a Volcano Deformation Model for Soufrière Hills Volcano, Montserrat

AbstractVolcano deformation models contribute to hazard assessment by simulating magma system dynamics. Traditional magma reservoir pressure source shape assumptions often fail to replicate irregular, geophysically identified geometries. Uncertainties regarding the influence of reservoir geometry can limit the effectiveness of using deformation models to decipher unrest signals. Here, we aim to determine the feasibility of using a magma reservoir geometry directly derived from a seismic tomography survey in a volcano deformation model for Soufrière Hills Volcano, Montserrat. Three‐dimensional deformation models are created to simulate displacement using a pressure source geometry constrained from a low seismic velocity anomaly, inferred to be a region of partial melt, and contrasted against a traditional ellipsoid reservoir geometry. We also test a “hybrid” model combining a seismically inferred reservoir upper geometry and ellipsoidal base. Results of each model are evaluated against ground displacement observed on Montserrat from 2010 to 2022. Our results show that different reservoir geometries change the horizontal and vertical displacement fields across the island: the ellipsoid reservoir best reproduces vertical displacement magnitude, while the hybrid reservoirs best simulate horizontal displacement vectors and the region of maximum uplift. Overall, the ellipsoid‐shaped reservoir provides our best‐fit to the observed data, but we note this result could be biased due to prior years of optimization helping constrain the ellipsoid shape, size, and location. Our results show the potential for further use of geophysically constrained reservoir geometries in deformation modeling, and our methods could be applied to other deforming volcanoes worldwide.

Suitable AlN Source Shape for Optimizing Gas Mass Transfer During AlN Crystal Growth

Suitable AlN Source Shape for Optimizing Gas Mass Transfer During AlN Crystal Growth

The challenges of media and information literacy in the artificial intelligence ecology: deepfakes and misinformation

In the ecosystem of artificial intelligence (AI), generative models enable the creation of hyper-realistic manipulations that are extremely plausible due to the precision of the audiovisual objects. These deepfakes are undetectable thanks to their components, which heightens concerns about the distortion of reality in the information ecosystem and how the ability to distinguish between real and fake audiovisual content affects public trust and democratic systems. This is a major challenge for media and information literacy if it is to combat misinfor­mation effectively. In this context, this study presents the results of a quasi-experiment conducted with 80 young people from the Community of Madrid (Spain) to assess their ability to detect deepfakes in immersive environments and to establish whether the context-identifying elements that enable detection of the reputation of the media source shape the credibility of the images. The results show that the images take precedence over the context identifiers, preventing a critical reading of the information that would make it possible to detect visual forgeries, something that is reinforced by their exceptional verisimilitude. It is concluded that the new post-humanist biome of virtual reality and artificial intelligence requires a reorien­tation of media and information literacy to raise the public’s awareness and educate them to make them less susceptible to disinformation based on deepfakes created with generative models.

Exploring the optimal combination of Ru/Ta bilayer mask stacks and illumination source shapes to mitigate mask 3D effects at high-NA extreme ultraviolet lithography.

The imaging performance of extreme ultraviolet (EUV) lithography is determined by the optical properties and thickness of the photomask absorber material and the illumination source shape. Optimizing the trade-offs between imaging metrics, such as normalized image log slope, telecentricity error, and best focus variation through pitch (collectively known as mask-3-dimensional (M3D) effects), is crucial to improve the throughput of the EUV lithography process. This study aims to optimize Ru/Ta bilayer photomask absorber stacks and illumination source shapes to mitigate M3D effects using mask diffraction analysis. It intends to raise questions about the conventional absorber reflectivity or induced phase shift-based approach.

Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data

Abstract. Methane (CH4) is an important anthropogenic greenhouse gas, and its rising concentration in the atmosphere contributes significantly to global warming. A comparatively small number of highly emitting persistent methane sources are responsible for a large share of global methane emissions. The identification and quantification of these sources, which often show large uncertainties regarding their emissions or locations, are important to support mitigating climate change. Daily global column-averaged dry air mole fractions of atmospheric methane (XCH4) are retrieved from radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) on board on the Sentinel-5 Precursor (S5P) satellite with a moderately high spatial resolution, enabling the detection and quantification of localized methane sources. We developed a fully automated algorithm to detect regions with persistent methane enhancement and to quantify their emissions using a monthly TROPOMI XCH4 dataset from the years 2018–2021. We detect 217 potential persistent source regions (PPSRs), which account for approximately 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases, we conclude that 7.8 % of the detected source regions are dominated by coal, 7.8 % by oil and gas, 30.4 % by other anthropogenic sources like landfills or agriculture, 7.3 % by wetlands, and 46.5 % by unknown sources. Many of the identified PPSRs are in well-known source regions, like the Permian Basin in the USA, which is a large production area for oil and gas; the Bowen Basin coal mining area in Australia; or the Pantanal Wetlands in Brazil. We perform a detailed analysis of the PPSRs with the 10 highest emission estimates, including the Sudd Wetland in South Sudan, an oil- and gas-dominated area on the west coast in Turkmenistan, and one of the largest coal production areas in the world, the Kuznetsk Basin in Russia. The calculated emission estimates of these source regions are in agreement within the uncertainties in results from other studies but are in most of the cases higher than the emissions reported by emission databases. We demonstrate that our algorithm is able to automatically detect and quantify persistent localized methane sources of different source type and shape, including larger-scale enhancements such as wetlands or extensive oil- and gas-production basins.

A dynamic volumetric heat source model for laser additive manufacturing

Melt pool scale models of laser powder bed fusion (LPBF) offer insights into the process-structure-property relationships in additive manufacturing (AM). These models often neglect physical phenomena such as vapor cavity formation and fluid mechanics to reduce computational demands. Instead, volumetric heat source models are used to represent the effects that these phenomena have on the predicted melt pool dimensions. Generally, the dimensions and effective absorption of the volumetric heat source are calibrated to reproduce melt pool dimensions observed in metallographic cross sections taken from single-track experiments on bare plate. However, the transient nature of LPBF often deviates the melt pool dimensions from the assumed steady-state conditions of single-track experiments, motivating the need for a volumetric heat source model that more generally considers the dynamic relationship between melt pool shape and laser-material interactions. Here, we introduce a two-parameter volumetric heat source model that integrates several existing models into a generalized mathematical expression, providing independent control over the radial heat distribution via the parameter k and the volumetric shape of the heat source via the parameter m. This parameterization enables the calibration of melt pool shape predictions through simultaneous adjustment of these parameters, while keeping the radial heat source dimensions consistent with the experimental spot size (D4σ) and constraining the heat source depth and absorption to physically derived expressions for cavities. Consequently, the proposed volumetric heat source model adapts to changes in the local melt pool conditions due to scanning strategy and part geometry by dynamically adjusting the heat source depth and absorption. We demonstrate the capabilities of the proposed model through comparisons with a collection of experiments from the Additive Manufacturing Benchmark (AMBench).

Results from a synthetic model of the ITER XRCS-Core diagnostic based on high-fidelity x-ray ray tracing.

A high-fidelity synthetic diagnostic has been developed for the ITER core x-ray crystal spectrometer diagnostic based on x-ray ray tracing. This synthetic diagnostic has been used to model expected performance of the diagnostic, to aid in diagnostic design, and to develop engineering tolerances. The synthetic model is based on x-ray ray tracing using the recently developed xicsrt ray tracing code and includes a fully three-dimensional representation of the diagnostic based on the computer aided design. The modeled components are: plasma geometry and emission profiles, highly oriented pyrolytic graphite pre-reflectors, spherically bent crystals, and pixelated x-ray detectors. Plasma emission profiles have been calculated for Xe44+, Xe47+, and Xe51+, based on an ITER operational scenario available through the Integrated Modelling & Analysis Suite database, and modeled within the ray tracing code as a volumetric x-ray source; the shape of the plasma source is determined by equilibrium geometry and an appropriate wavelength distribution to match the expected ion temperature profile. All individual components of the x-ray optical system have been modeled with high-fidelity producing a synthetic detector image that is expected to closely match what will be seen in the final as-built system. Particular care is taken to maintain preservation of photon statistics throughout the ray tracing allowing for quantitative estimates of diagnostic performance.

Source shape estimation for neutron imaging systems using convolutional neural networks.

Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.

Spatial and Surface Correspondence Field for Interaction Transfer

In this paper, we introduce a new method for the task of interaction transfer. Given an example interaction between a source object and an agent, our method can automatically infer both surface and spatial relationships for the agent and target objects within the same category, yielding more accurate and valid transfers. Specifically, our method characterizes the example interaction using a combined spatial and surface representation. We correspond the agent points and object points related to the representation to the target object space using a learned spatial and surface correspondence field, which represents objects as deformed and rotated signed distance fields. With the corresponded points, an optimization is performed under the constraints of our spatial and surface interaction representation and additional regularization. Experiments conducted on human-chair and hand-mug interaction transfer tasks show that our approach can handle larger geometry and topology variations between source and target shapes, significantly outperforming state-of-the-art methods.

Unleashing cosmic shear information with the tomographic weak lensing PDF

In this work, we demonstrate the constraining power of the tomographic weak lensing convergence PDF for StageIV-like source galaxy redshift bins and shape noise. We focus on scales of 10 to 20 arcmin in the mildly nonlinear regime, where the convergence PDF and its changes with cosmological parameters can be predicted theoretically. We model the impact of reconstructing the convergence from the shear field using the well-known Kaiser-Squires formalism. We cross-validate the predicted and the measured convergence PDF derived from convergence maps reconstructed using simulated shear catalogues. Employing a Fisher forecast, we determine the constraining power for (Ωm,S8,w0). We find that adding a 5-bin tomography improves the κ−PDF constraints by a factor of {3.8,1.3,1.6} for (Ωm,S8,w0) respectively. Additionally, we perform a joint analysis with the shear two-point correlation functions, finding an enhancement of around a factor of 1.5 on all parameters with respect to the two-point statistics alone. These improved constraints come from disentangling from w0 by extracting non-Gaussian information, in particular, including the PDF skewness at different redshift bins. We also study the effect of varying the number of parameters to forecast, in particular we add h, finding that the convergence PDF maintains its constraining power while the precision from two-point correlations degrades by a factor of {1.7,1.4,1.8} for , respectively.

Numerical simulation and mathematical modeling of biomechanical stress distribution in poroelastic tumor tissue via magnetic field and bio-ferro-fluid

Based on scientific evidence, it seems that bio-magnetic systems can change the process of cancer cell death by affecting the distribution of pressure and mechanical stress in the tumor tissue. Already most of the research has been done experimentally and few mathematical modeling and numerical simulations have been done to investigate the relationship between the magnetic parameters and the mechanical stress of the tumor tissue. This is despite the fact that in order to be able to make new equipment with the help of medical engineering methods, it is definitely necessary that the mathematics governing the problem and changes in the effective magnetic parameters (such as the shape of the magnetic source, magnetic flux density, magnetic source distance and ferro-fluid volume fraction) should be studied as much as possible. In this research, using numerical simulation and mathematical modeling, four common geometrical shapes (rectangular and circular) of the static magnetic field source were used to investigate the relationship between the change of the effective magnetic parameters and the mechanical stress created in the tumor tissue. The results of this research showed that when the magnetic flux density and ferro-fluid volume fraction and also the distance between the magnet and the tissue are kept constant, as well as without spending any extra energy, for a rectangular magnet, just by changing the way the source is placed on the tissue, the average biomechanical stress inside the tumor tissue causes a 25 % change. Also, for a circular magnet, just by doubling the radius of the magnet, the average biomechanical stress inside the tumor tissue causes a 73 % change.

Flame propagation speed prediction model of premixed methane gas deflagration experiments based on Adamax-LSTM for FLNG

A time-series prediction method based on AdaMax-LSTM neural network is proposed for predicting the flame propagation speed in premixed methane gas deflagration experiments, which can provide a decision-making basis for emergency operation of FLNG combustible gas deflagration accidents. Firstly, 54 sets of premixed methane gas deflagration experiments under semi-open duct obstacle conditions were conducted to investigate the different deflagration mechanisms by changing the obstacle parameters. The experimental results demonstrate that the distance between the obstacle and ignition source, obstacle length and obstacle shape will all effect the flame propagation speed and deflagration overpressure. Secondly, the LSTM neural network is employed to setup a novel method which can predict the flame speed in time series via calculating the Reynolds number and determining the turbulence of the flame accurately. The deflagration experiments results were used as the dataset for AI training for the proposed prediction method. In addition, the AdaMax optimizer is added into the backpropagation process of the proposed LSTM neural network to maximize the prediction accuracy of the method. The analysis results indicate that the AdaMax-LSTM neural network with sigmoid activation function can achieve the highest level of accuracy prediction, with the mean R2 value reaching 0.95, and can identify anomaly data and the most different deflagration mechanisms experimental condition. The proposed method provides an efficient and accurate way to predict and analyze the deflagration mechanisms via employing cutting-edge AI technology.

Reconstruction method for gamma-ray coded-aperture imaging based on mask and anti-mask functions

Gamma-ray coded-aperture imaging technology plays an important role in nuclear security, decommissioning of nuclear facilities, and nuclear medicine diagnosis. However, under near-field imaging condition, artifacts in the reconstructed image can interfere with identifying the shape and position of the radioactive source. In this paper, a gamma-ray coded-aperture imaging method based on mask and anti-mask functions was proposed to suppress imaging artifacts and speed up the acquisition of low-noise reconstructed images. Through simulation, the effects of the number of iterations and the thickness of the coded-aperture collimator on the imaging quality were studied, and the range of the optimal correction factor in the method was determined. Imaging experiments were conducted using a compact coded-aperture gamma camera based on CdZnTe detector to verify the applicability of the optimal correction factor range. The limitations of the proposed method were analyzed through complex-shaped source imaging simulations and multi-source imaging experiments. This method has an insufficient suppression effect on random artifacts and requires further improvement in imaging irregular radioactive sources. However, it has good imaging performance for single-point source and multi-point sources, effectively reducing regular cross-shaped and stripe-like artifacts. In the non-uniform radioactive background, it can eliminate a part of artifacts, significantly improving imaging quality. Therefore, this method has potential applications in complex radioactive environments.

Shape and location recovery of laser excitation sources in photoacoustic imaging using topological gradient optimization

This study focuses on source term inversion in fractional partial differential equations, specifically applied to photoacoustic imaging. This work contributes to advancing imaging techniques and provides practical insights for medical diagnostics and materials characterization. Our aim in this paper is to develop an accurate method for recovering the location and the shape of a laser excitation source from partial boundary data. To achieve this, we reformulate our inverse problem as an optimization challenge. We utilize topological sensitivity analysis to establish an asymptotic expansion of a relevant shape function. These theoretical findings form the basis for a rapid and precise detection algorithm. Additionally, we present several numerical experiments that demonstrate the effectiveness and accuracy of our proposed approach.

Effect of Heat Source Shape on Temperature and Heat Diffusion in Continuous Grinding Based on FEM

Effect of Heat Source Shape on Temperature and Heat Diffusion in Continuous Grinding Based on FEM

A simple method of shape transformation using the modified Gray–Scott model

In this paper, based on the original Gray–Scott model, we propose a modified Gray–Scott model by introducing a target term into the reaction–diffusion equations. We apply this modified model in the context of shape transformation problems. To expedite the process from the source shape to the target shape, we utilize the explicit Euler method to solve our proposed modified Gray–Scott model, making our approach simpler and more efficient. To validate the feasibility of our method, we conduct simulation experiments in both two-dimensional (2D) and three-dimensional (3D) spaces. By progressing through experiments of increasing complexity, we demonstrate the natural effectiveness of our simulation method as a viable approach for shape transformation. To demonstrate the efficiency of the method, we provide the runtime consumed by the simulated shape transformation experiment. Additionally, to assess the correspondence between the ground truth values of the target shape and the simulated results, we calculate the corresponding area change rate and volume change rate in 2D and 3D spaces to prove that our proposed method can effectively transform into the target shape.

Theoretical prediction model for minimum ignition energy of combustible gas mixtures

Minimum ignition energy (MIE) is a crucial parameter for identifying the hazards of combustible gases. To rapidly obtain accurate MIE values for various gases, the shape of the ignition source was simplified to cylindrical. Based on theories of heat conduction and chemical reaction kinetics, a physical model of the ignition process was established. The spatial and temporal evolution of temperature was used as a basis for judging the success of ignition. A numerical algorithm was developed and maximum flame temperatures and MIE were calculated for several combustible gases (H2, CH4, C2H6, C3H8, C4H10, C2H4, C2H2). This model accurately predicted the U-shaped trend of MIE values with concentration for CH4/air and H2/air mixtures. It found that the MIE was minimal at the stoichiometric concentration, at 0.37 mJ and 0.017 mJ, closely aligning with experimental data. This numerical model has been validated for its accuracy and promises to make relatively precise theoretical predictions for the MIE values of uncommon combustible gases or mixtures of various gases.

Geometry-aware 3D pose transfer using transformer autoencoder

3D pose transfer over unorganized point clouds is a challenging generation task, which transfers a source’s pose to a target shape and keeps the target’s identity. Recent deep models have learned deformations and used the target’s identity as a style to modulate the combined features of two shapes or the aligned vertices of the source shape. However, all operations in these models are point-wise and independent and ignore the geometric information on the surface and structure of the input shapes. This disadvantage severely limits the generation and generalization capabilities. In this study, we propose a geometry-aware method based on a novel transformer autoencoder to solve this problem. An efficient self-attention mechanism, that is, cross-covariance attention, was utilized across our framework to perceive the correlations between points at different distances. Specifically, the transformer encoder extracts the target shape’s local geometry details for identity attributes and the source shape’s global geometry structure for pose information. Our transformer decoder efficiently learns deformations and recovers identity properties by fusing and decoding the extracted features in a geometry attentional manner, which does not require corresponding information or modulation steps. The experiments demonstrated that the geometry-aware method achieved state-of-the-art performance in a 3D pose transfer task. The implementation code and data are available at https://github.com/SEULSH/Geometry-Aware-3D-Pose-Transfer-Using-Transformer-Autoencoder.

3D neutronic analysis on compact fusion reactors: PHITS-OpenMC cross-comparison

The development of high temperature superconducting tape technology enabled the design of compact fusion reactors, allowing faster development in the field. The reduced size makes even more important the evaluation of neutron flux on all components and at every region, including at the magnet position, to assess the lifetime of the materials and their performances during operations. Compactness, however, introduces new technological challenges, considering the reduced space available for shielding materials and the consequently harsher radiation environment for both structural and functional materials. Many different particle transport codes have been developed for nuclear engineering and fundamental physics purposes, each one relying on different algorithms and nuclear models, with different features and capabilities. In the present work the transport codes PHITS and OpenMC are compared, testing the impact on integral results such as the tritium breeding ratio and on spatially resolved neutron spectra of different nuclear libraries, nuclear models and codes, on a CAD imported 3D geometry of an ARC like fusion machine. The impact of different neutron source shapes is also investigated, from a simple ring-like source to a realistic toroidal plasma distribution, as well as the possibility of performing calculations on a 10°reduced domain, instead of on a full 360°geometry. The uncertainty introduced by each model and geometry choice is analyzed and the computational time required is briefly discussed.