2D Gaussian Research Articles

The Sloan Digital Sky Survey peculiar velocity catalogue

ABSTRACT We present a new catalogue of distances and peculiar velocities (PVs) of 34 059 early-type galaxies derived from fundamental plane (FP) measurements using data from the Sloan Digital Sky Survey (SDSS). This $7016\, \mathrm{deg}^{2}$ homogeneous sample comprises the largest set of PVs produced to date and extends the reach of PV surveys up to a redshift limit of z = 0.1. Our SDSS-based FP distance measurements have a mean uncertainty of 23 per cent. Alongside the data, we produce an ensemble of 2048 mock galaxy catalogues that reproduce the data selection function, and are used to validate our fitting pipelines and check for systematic errors. We uncover a significant trend between group richness and mean surface brightness within the sample, which may hint at an environmental dependence within the FP or the presence of unresolved systematics, and can result in biased PVs. This is removed by using multiple FP fits as function of group richness, a procedure made tractable through a new analytic derivation for the integral of a three-dimensional (3D) Gaussian over non-trivial limits. Our catalogue is calibrated to the zero-point of the CosmicFlows-III sample with an uncertainty of 0.004 dex (not including cosmic variance or the error within CosmicFlows-III itself), which is validated using independent cross-checks with the predicted zero-point from the 2M++ reconstruction of our local velocity field. Finally, as an example of what is possible with our new catalogue, we obtain preliminary bulk flow measurements up to a depth of $135\,{\rm h}^{-1}\mathrm{Mpc}$. We find a slightly larger-than-expected bulk flow at high redshift, although this could be caused by the presence of the Shapley supercluster, which lies outside the SDSS PV footprint.

Deep reconstruction of high-quality ultrasound images from raw plane-wave data: A simulation and in vivo study

This paper presents a novel beamforming approach based on deep learning to get closer to the ideal Point Spread Function (PSF) in Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct a high-quality version of Tissue Reflectivity Function (TRF) from echo traces acquired by transducer elements using only a single plane-wave transmission. In this approach, first, a model for the TRF is introduced by setting the imaging PSF as an isotropic (i.e., circularly symmetric) 2D Gaussian kernel convolved with a cosine function. Then, a mapping function between the pre-beamformed Radio-Frequency (RF) channel data and the proposed output is constructed using deep learning. Network architecture contains multi-resolution decomposition and reconstruction using wavelet transform for effective recovery of high-frequency content of the desired output. We exploit step by step training from coarse (mean square error) to fine (ℓ0.2) loss functions. The proposed method is trained on 1174 simulation ultrasound data with the ground-truth echogenicity map extracted from real photographic images. The performance of the trained network is evaluated on the publicly available simulation and in vivo test data without any further fine-tuning. Simulation test results show an improvement of 37.5% and 65.8% in terms of axial and lateral resolution as compared to Delay-And-Sum (DAS) results, respectively. The contrast is also improved by 33.7% in comparison to DAS. Furthermore, the reconstructed in vivo images confirm that the trained mapping function does not need any fine-tuning in the new domain. Therefore, the proposed approach maintains high resolution, contrast, and framerate simultaneously.

A meshless method to compute pressure fields from image velocimetry

We propose a meshless method to compute pressure fields from image velocimetry data, regardless of whether this is available on a regular grid as in cross-correlation based velocimetry or on scattered points as in tracking velocimetry. The proposed approach is based on radial basis functions (RBFs) regression and relies on the solution of two constrained least square problems. The first one is the regression of the measurements to create an analytic representation of the velocity field. This regression can be constrained to impose boundary conditions (e.g. no-slip velocity on a wall or inlet conditions) or differential constraints (e.g. the solenoidal condition for an incompressible flow). The second one is the meshless integration of the pressure Poisson equation, achieved by seeking a solution in the form of a RBF expansion and using constraints to impose boundary conditions. We first illustrate the derivation of the two least square problems and the numerical techniques implemented for their solution. Then, we showcase the method with three numerical test cases of growing complexity. These are a 2D Gaussian Vortex, a 2D flow past a cylinder from CFD and a 3D Stokes flow past a sphere. For each case, we consider randomly sampled vector fields simulating particle tracking measurements and analyze the sensitivity to noise and seeding density.

Pellet source density in toroidal plasma configurations based on a 2D Gaussian deposition model

We develop a two-dimensional (2D) Gaussian deposition model to calculate the initial pellet deposition density immediately after pellet ablation, which is valid before the ∇B-drift of the ablated material significantly shifts its location. A 2D Gaussian particle distribution is assumed in the ablation cloud cross-section. Applying this new model to a typical EAST plasma, and comparing it with the conventional point deposition model, it is found that the new model can resolve the tangential singularity problem encountered by the point deposition model. In addition, the model predicts that the initial pellet deposition density depends strongly on the ablation cloud radius as well as the form of the radial particle distribution in the ablation cloud with tangential injection. The ∇B-drift is then introduced with the drift displacement estimated based on a scaling formula derived from HPI2 simulations. The model can provide a fast evaluation of pellet deposition density compared to the predictive HPI2 code at the expense of acceptable accuracy loss. This model could be a useful tool for physical studies relevant to pellet injection, such as pellet ELM triggering and particle and energy transport.

A New Fiber Detection Method for LAMOST Based on the Front-illuminated Method

The double revolving fiber positioning technology is one of the key technologies for the success of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). The accuracy of fiber positioning will directly affect the observation efficiency of LAMOST. To achieve higher fiber positioning accuracy, the original open-loop controlled fiber positioning system urgently needs to be upgraded into a closed-loop control system. The fiber detection is the most important part of the closed-loop controlled fiber positioning system. The back-illuminated detection method is usually used to detect the fiber position by directly detecting the light spot generated at the fiber end in the multi-fiber spectral surveys. In this paper, we introduce a new method to measure the fiber position based on the image of the front-illuminated LAMOST focal plane. The front-illuminated image does not require lighting devices inside the spectrograph, and it could reduce the instability and light pollution in the spectrograph end. Our method measures the fiber position by fitting the profile of the fiber pinhole with a 2D Gaussian function. A series of tests show that the relative position measurement precision of the front-illuminated method is about 0.″12, and the method could have the same accuracy as the back-illuminated method once the system bias is calibrated by a simple radial correction function. The required fiber positioning accuracy of LAMOST is 0.″4, and the new method satisfies the requirement of LAMOST fiber detection accuracy and could be used in the closed-loop fiber control system.

Investigating the contribution of extended radio sources to the Epoch of Reionization power spectrum

ABSTRACT We investigate the contribution of extended radio sources such as Centaurus A, and Galactic supernova remnants (SNRs) to our ability to detect the statistical 21-cm signal from the Epoch of Reionisation (EoR) with the Murchison Widefield Array (MWA). These sources are typically ignored because they are in highly attenuated parts of the MWA primary beam, however, in aggregate, these sources have apparent flux densities of $10\, \rm {Jy}$ on angular scales we expect to detect the 21-cm signal. We create bespoke multicomponent 2D Gaussian models for Galactic SNRs and for Centaurus A, and simulate the visibilities for two MWA snapshot observations. We grid those visibilities and then Fourier transform them with respect to frequency, averaging them both spherically and cylindrically to produce the 1D and 2D power spectra. We compare the simulated 1D power spectra to the expected 21-$\rm {cm}$ power spectrum. We find that although these extended sources are in highly attenuated parts of the MWA primary beam pattern, collectively they have enough power (∼104−105 $\rm {mK^2}\, {\it h^{-3}} \, \rm {Mpc^{3}}$) on EoR significant modes $(|{\boldsymbol k}| \lesssim 0.1\, h\, \rm {Mpc^{-1}})$ to prohibit detection of the 21-$\rm {cm}$ signal (∼104 $\rm {mK^2}\, {\it h^{-3}} \, \rm {Mpc^{3}}$). We find that $50{-}90{{\ \rm per\ cent}}$ of sources must be removed in order to reduce leakage to a level of $\sim 10{-}20{{\ \rm per\ cent}}$ of the 21-$\rm {cm}$ power spectrum on EoR significant modes. The effects of wide-field extended sources will have implications on the detectability of the 21-$\rm {cm}$ signal for the MWA and with the future Square Kilometre Array (SKA).

Bedload transport analysis using image processing techniques

Bedload transport is an important factor to describe the hydromorphological processes of fluvial systems. However, conventional bedload sampling methods have large uncertainty, making it harder to understand this notoriously complex phenomenon. In this study, a novel, image-based approach, the Video-based Bedload Tracker (VBT), is implemented to quantify gravel bedload transport by combining two different techniques: Statistical Background Model and Large-Scale Particle Image Velocimetry. For testing purposes, we use underwater videos, captured in a laboratory flume, with future field adaptation as an overall goal. VBT offers a full statistics of the individual velocity and grainsize data for the moving particles. The paper introduces the testing of the method which requires minimal preprocessing (a simple and quick 2D Gaussian filter) to retrieve and calculate bedload transport rate. A detailed sensitivity analysis is also carried out to introduce the parameters of the method, during which it was found that by simply relying on literature and the visual evaluation of the resulting segmented videos, it is simple to set them to the correct values. Practical aspects of the applicability of VBT in the field are also discussed and a statistical filter, accounting for the suspended sediment and air bubbles, is provided.

Study on the molten pool behavior and porosity formation mechanism in dual-beam laser welding of aluminum alloy

Based on the mechanism of dynamic coupling between molten pool and keyhole, a three-dimensional (3D) transient model for the dual-beam laser welding of aluminum alloy is established by considering the surface tension, Marangoni force, and recoil pressure. The morphology of the molten pool, porosity formation process, and the heat transfer mechanism during the process of laser welding under different parameters are analyzed. A double rotating 3D Gaussian heat source is used to represent the laser beam, and the volume of fluid method is used to track the gas-liquid free surface, and the gas-liquid interface force is transformed by the continuous surface force model. The results show that in the process of dual-beam welding, the interaction between the two keyholes enhances the fluid flow perpendicular to the spot line, and the shape of weld pool surface changes from elliptical to circular. Furthermore, welding speed and spot spacing have a significant effect on the shape of the molten pool. Furthermore, it is observed that dual-beam welding can improve the stability of the keyhole and reduce the maximum oscillation amplitude and the number of keyhole breakups. At a specific spot spacing, a unique process of separation and fusion is discovered in addition to the common stages of growth, maintenance, breakup, and shrinkage of the keyhole. The simulation results are in good agreement with the existing experimental results. Overall, this paper provides useful insights into the dynamics of the molten pool in dual-beam welding and reveals the molten pool behavior and porosity formation mechanism.

Multi-parametric PET/MRI for enhanced tumor characterization of patients with cervical cancer

AimThe concept of personalized medicine has brought increased awareness to the importance of inter- and intra-tumor heterogeneity for cancer treatment. The aim of this study was to explore simultaneous multi-parametric PET/MRI prior to chemoradiotherapy for cervical cancer for characterization of tumors and tumor heterogeneity.MethodsTen patients with histologically proven primary cervical cancer were examined with multi-parametric 68Ga-NODAGA-E[c(RGDyK)]2-PET/MRI for radiation treatment planning after diagnostic 18F-FDG-PET/CT. Standardized uptake values (SUV) of RGD and FDG, diffusion weighted MRI and the derived apparent diffusion coefficient (ADC), and pharmacokinetic maps obtained from dynamic contrast-enhanced MRI with the Tofts model (iAUC60, Ktrans, ve, and kep) were included in the analysis. The spatial relation between functional imaging parameters in tumors was examined by a correlation analysis and joint histograms at the voxel level. The ability of multi-parametric imaging to identify tumor tissue classes was explored using an unsupervised 3D Gaussian mixture model-based cluster analysis.ResultsFunctional MRI and PET of cervical cancers appeared heterogeneous both between patients and spatially within the tumors, and the relations between parameters varied strongly within the patient cohort. The strongest spatial correlation was observed between FDG uptake and ADC (median r = − 0.7). There was moderate voxel-wise correlation between RGD and FDG uptake, and weak correlations between all other modalities. Distinct relations between the ADC and RGD uptake as well as the ADC and FDG uptake were apparent in joint histograms. A cluster analysis using the combination of ADC, FDG and RGD uptake suggested tissue classes which could potentially relate to tumor sub-volumes.ConclusionA multi-parametric PET/MRI examination of patients with cervical cancer integrated with treatment planning and including estimation of angiogenesis and glucose metabolism as well as MRI diffusion and perfusion parameters is feasible. A combined analysis of functional imaging parameters indicates a potential of multi-parametric PET/MRI to contribute to a better characterization of tumor heterogeneity than the modalities alone. However, the study is based on small patient numbers and further studies are needed prior to the future design of individually adapted treatment approaches based on multi-parametric functional imaging.

Accuracy-Configurable 2-D Gaussian Filter Architecture for Energy-Efficient Image Processing

Most compute-intensive applications demand approaches to optimize energy efficiency in CMOS design. Configurable architectures and approximate computing are alternatives to meet the quality and energy requirements in multimedia processing during the runtime. This work proposes a 2D Gaussian filter design to provide multiple quality-power-performance points of operation. The main goal is to design an accuracy-configurable 2D Gaussian filter accelerator at the runtime with a low overhead cost when a new configuration is dynamically selected. The power consumption results show reductions ranging from 9.87% to 58.96%, with an up to 14.8% increase in the hardware area.

Mid-infrared Studies of Dusty Sources in the Galactic Center

Mid-infrared (MIR) images of the Galactic center show extended gas and dust features along with bright infrared sources (IRS). Some of these dust features are a part of ionized clumpy streamers orbiting Sgr A*, known as the mini-spiral. We present their proper motions over a 12 yr time period and report their flux densities in N-band filters and derive their spectral indices. The observations were carried out by VISIR at the ESO’s Very Large Telescope. High-pass filtering led to the detection of several resolved filaments and clumps along the mini-spiral. Each source was fit by a 2D Gaussian profile to determine the offsets and aperture sizes. We perform aperture photometry to extract fluxes in two different bands. We present the proper motions of the largest consistent set of resolved and reliably determined sources. In addition to stellar orbital motions, we identify a stream-like motion of extended clumps along the mini-spiral. We also detect MIR counterparts of the radio tail components of the IRS 7 source. They show a clear kinematical deviation with respect to the star. They likely represent Kelvin–Helmholtz instabilities formed downstream in the shocked stellar wind. We also analyze the shape and orientation of the extended late-type IRS 3 star that is consistent with the Atacama Large Millimeter/submillimeter Array submillimeter detection of the source. Its puffed-up envelope with a radius of ∼2 × 106 R ⊙ could be the result of the red-giant collision with a nuclear jet, which was followed by tidal prolongation along the orbit.

Dual-high-frequency VLBI study of blazar-jet brightness-temperature gradients and collimation profiles

Context.On the kiloparsec scale, extragalactic radio jets show two distinct morphologies related to their power: collimated high-power jets ending in a bright termination shock and low-power jets opening up close to the core and showing a more diffuse surface brightness distribution. The emergence of this morphological dichotomy on the parsec scale at the innermost jet regions can be studied with very-long-baseline interferometry (VLBI) radio observations of blazars in which the jet emission is strongly Doppler boosted due to relativistic bulk motion at small angles between the jet direction and the line of sight.Aims.We seek to characterize the geometry and emission profiles of the parsec-scale radio jets of flat-spectrum radio quasars (FSRQs) and BL Lacertae objects (BL Lacs) on parsec scales to derive properties of the magnetic field, environment, and energetics for different classes of extragalactic jets.Methods.We analyze the VLBI radio data of 15 FSRQs, 11 BL Lacs, and two radio galaxies contained in both the Monitoring Of Jets in Active galactic nuclei with VLBA Experiments data archive and the Boston University blazar group sample archive at 15 GHz and 43 GHz, repectively. We derived the brightness-temperature and jet-width gradients along the jet axis from parameterizations of the jets using 2D Gaussian brightness distributions.Results.In most BL Lac objects, the diameter and brightness-temperature gradients along the jet axis can generally be described well by single power laws, while the jets of FSRQs show more complex behavior and remain more strongly collimated on larger physical scales. We find evidence for a transition of the global jet geometry from a parabolic to a conical shape in the BL Lac objects 3C 66A, Mrk 421 and BL Lacertae, the radio galaxy 3C 111 and the FSRQs CTA 26, PKS 0528+134, 4C +71.07, 4C +29.45, and 3C 279 outside the Bondi sphere.Conclusions.Our results combined with findings from kinematic VLBI studies that the jets of FSRQs exhibit larger bulk Lorentz factors than BL Lacs are in agreement with relativistic magnetohydrodynamical jet-disk simulations in which the flattening of the jet magnetization profile due to magnetic fields from the accretion disk leads to a more persistent collimation in high-accretion-rate blazars.

Neural Networks as Optimal Estimators to Marginalize Over Baryonic Effects

Abstract Many different studies have shown that a wealth of cosmological information resides on small, nonlinear scales. Unfortunately, there are two challenges to overcome to utilize that information. First, we do not know the optimal estimator that will allow us to retrieve the maximum information. Second, baryonic effects impact that regime significantly and in a poorly understood manner. Ideally, we would like to use an estimator that extracts the maximum cosmological information while marginalizing over baryonic effects. In this work we show that neural networks can achieve that when considering some simple scenarios. We made use of data where the maximum amount of cosmological information is known: power spectra and 2D Gaussian density fields. We also contaminate the data with simplified baryonic effects and train neural networks to predict the value of the cosmological parameters. For this data, we show that neural networks can (1) extract the maximum available cosmological information, (2) marginalize over baryonic effects, and (3) extract cosmological information that is buried in the regime dominated by baryonic physics. We also show that neural networks learn the priors of the data they are trained on, affecting their extrapolation properties. We conclude that a promising strategy to maximize the scientific return of cosmological experiments is to train neural networks on state-of-the-art numerical simulations with different strengths and implementations of baryonic effects.

Effect of position-dependent effective mass on donor impurity- and exciton-related electronic and optical properties of 2D Gaussian quantum dots

Effect of position-dependent effective mass on donor impurity- and exciton-related electronic and optical properties of 2D Gaussian quantum dots

Foreground-guided textural-focused person re-identification

Person re-identification (Re-ID) is challenging in computer vision. It is crucial to strengthen discriminative features and suppress irrelevant ones for high-performance. Existing Re-ID approaches have won significant progress by attention mechanisms or introducing key-point/part priors. However, these methods suffer the high cost of building models. Besides, potential feature false alarm problems may happen due to background interference and inaccurate priors. Moreover, they usually ignore the degradation of texture clues which are identity sensitive. In this paper, we propose the Foreground-Guided Textural-Focused Network (FTN) to address these problems. Specifically, our FTN is an end-to-end Re-ID framework, which consists of a Semantic Encoder (S-Enc), a Compact Foreground Attention (CFA) module, and a Texture-Focused Decoder (TF-Dec). First, based on CFA and 2D Gaussian kernel, a coarse foreground-guided learning strategy is developed to suppress the feature false alarms at the source. Its core idea lies in constructing foreground guidance, which forces S-Enc to pay more attention to person instance-level features. Second, the TF-Dec is designed as a lightweight reconstruction task. It is trained via a novel gradient loss, and further forces S-Enc to maintain texture-wise details. Our method is computationally efficient as TF-Dec is abandoned in the inference phase. Extensive experiments are conducted on three large-scale Re-ID datasets Market1501, CUHK03, MSMT17, and two other occluded datasets. The results indicate that FTN achieves superior performance against the state-of-the-art methods, e.g., Rank-1 of 96.2% and 59.0% on Market1501 and Occluded-Duke respectively.

Sizes and Shapes of Sources in Solar Metric Radio Bursts

Abstract Metric and decametric radio emissions from the Sun are the only direct source of information about the dynamics of nonthermal electrons in the upper corona. In addition, the combination of spectral and imaging (sizes, shapes, and positions) observations of low-frequency radio sources can be used as a unique diagnostic tool to probe plasma turbulence in the solar corona and inner heliosphere. The geometry of the low-frequency sources and its variation with frequency are still not understood, primarily due to the relatively low spatial resolution available for solar observations. Here we report the first detailed multifrequency analysis of the sizes of solar radio sources observed by the Low Frequency Array. Furthermore, we investigate the source shapes by approximating the derived intensity distributions using 2D Gaussian profiles with elliptical half-maximum contours. These measurements have been made possible by a novel empirical method for evaluating the instrumental and ionospheric effects on radio maps based on known source observations. The obtained deconvolved sizes of the sources are found to be smaller than previous estimations, and often show higher ellipticity. The sizes and ellipticities of the sources inferred using 2D Gaussian approximation, and their variation with frequency are consistent with models of anisotropic radio-wave scattering in the solar corona.

Optimization of 99m Tc whole-body SPECT/CT image quality: A phantom study.

PurposeInvestigate the impact of acquisition time and reconstruction parameters on single‐photon emission computed tomography/computed tomography (SPECT/CT) image quality with the ultimate aim of finding the shortest possible acquisition time for clinical whole‐body SPECT/CT (WB‐SPECT/CT) while maintaining image qualityMethodsThe National Electrical Manufacturers Association (NEMA) image quality measurements were performed on a SPECT/CT imaging system using a NEMA International Electrotechnical Commission (IEC) phantom with spherical inserts of varying diameter (10–37 mm), filled with 99mTc in activity sphere‐to‐background concentration ratio of 8.5:1. A gated acquisition was acquired and binned data were summed to simulate acquisitions of 15, 8, and 3 s per projection angle. Images were reconstructed on a Hermes (HERMES Medical Solutions AB, Stockholm, Sweden) workstation using eight subsets and between 4 and 24 iterations of the three‐dimensional (3D) ordered subset expectation maximization (OSEM) algorithm. Reconstructed images were post‐smoothed with 3D Gaussian filter ranging from 0 to 12 mm full‐width at half maximum (FWHM). Contrast recovery, background variability, and contrast‐to‐noise ratio were evaluatedResultsAs expected, the spheres were more clearly defined as acquisition time and count statistics improved. The optimal iteration number and Gaussian filter were determined from the contrast recovery convergence and level of noise. Convergence of contrast recovery was observed at eight iterations while 12 iterations yielded stabilized values at all acquisition times. In addition, it was observed that applying 3D Gaussian filter of 8–12 mm FWHM suppressed the noise and mitigated Gibbs artifacts. Background variability was larger for small spheres than larger spheres and the noise decreased when acquisition time became longer. A contrast‐to‐noise ratio >5 was reached for the two smallest spheres of 10 and 13 mm at acquisition times of 8 sConclusionOptimized reconstruction parameters preserved image quality with reduce acquisition time in present study. This study suggests an optimal protocol for clinical 99mTc SPECT/CT can be reached at 8 s per projection angle, with data reconstructed using 12 iterations and eight subset of the 3D OSEM algorithm and 8 mm Gaussian post‐filter.

Automated foveal location detection on spectral-domain optical coherence tomography in geographic atrophy patients

PurposeTo develop a fully automated algorithm for accurate detection of fovea location in atrophic age-related macular degeneration (AMD), based on spectral-domain optical coherence tomography (SD-OCT) scans.MethodsImage processing was conducted on a cohort of patients affected by geographic atrophy (GA). SD-OCT images (cube volume) from 55 eyes (51 patients) were extracted and processed with a layer segmentation algorithm to segment Ganglion Cell Layer (GCL) and Inner Plexiform Layer (IPL). Their en face thickness projection was convolved with a 2D Gaussian filter to find the global maximum, which corresponded to the detected fovea. The detection accuracy was evaluated by computing the distance between manual annotation and predicted location.ResultsThe mean total location error was 0.101±0.145mm; the mean error in horizontal and vertical en face axes was 0.064±0.140mm and 0.063±0.060mm, respectively. The mean error for foveal and extrafoveal retinal pigment epithelium and outer retinal atrophy (RORA) was 0.096±0.070mm and 0.107±0.212mm, respectively. Our method obtained a significantly smaller error than the fovea localization algorithm inbuilt in the OCT device (0.313±0.283mm, p <.001) or a method based on the thinnest central retinal thickness (0.843±1.221, p <.001). Significant outliers are depicted with the reliability score of the method.ConclusionDespite retinal anatomical alterations related to GA, the presented algorithm was able to detect the foveal location on SD-OCT cubes with high reliability. Such an algorithm could be useful for studying structural-functional correlations in atrophic AMD and could have further applications in different retinal pathologies.

Quantum Yield and Charge Diffusion in the Nancy Grace Roman Space Telescope Infrared Detectors

Weak gravitational lensing is a powerful tool for studying the growth of structure across cosmic time. The shear signal required for weak lensing analyses is very small, so any undesirable detector-level effects which distort astronomical images can significantly contaminate the inferred shear. The Nancy Grace Roman Space Telescope (Roman) will fly a focal plane with 18 Teledyne H4RG-10 near-infrared (IR) detector arrays; these have never before been used for weak lensing and they present different instrument calibration challenges relative to charge-coupled devices. A pair of previous investigations demonstrated that spatiotemporal correlations of flat field images can effectively separate the brighter-fatter effect (BFE) and interpixel capacitance (IPC). A third paper in the series introduced a Fourier-space treatment of these correlations which allowed the authors to expand to higher orders in BFE, IPC, and classical nonlinearity. This work expands the previous formalism to include quantum yield and charge diffusion. We test the updated formalism on simulations and show that we can recover input visible characterization values to within a few percent. We then apply the formalism to visible and IR flat field data from three Roman flight candidate detectors. We find that BFE is present in all detectors and that the magnitude of its central pixel value is comparable between visible data and IR data. We fit a 2D Gaussian model to the charge diffusion at 0.5 μm wavelength, and find variances of C 11 = 0.1066 ± 0.0011 pix2 in the horizontal direction, C 22 = 0.1136 ± 0.0012 pix2 in the vertical direction, and a covariance of C 12 = 0.0001 ± 0.0007 pix2 (stat) for SCA 20829. Last, we convert the asymmetry of the charge diffusion into an equivalent shear signal using the sensitivity coefficients for the Roman survey, and find a contamination of the shear correlation function to be ξ + ∼ 10−6 for each detector. This exceeds Roman’s allotted error budget for the measurement by a factor of in power (amplitude squared) but can likely be mitigated through standard methods for fitting the point-spread function (PSF) since for weak lensing applications the charge diffusion can be treated as a contribution to the PSF. Further work considering the impact of charge diffusion and quantum yield on shear measurements will follow once all detectors covering the Roman focal plane are selected.

DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model

A new deep learning method based on the target gray-level distribution constraint mechanism model is proposed to solve the infrared dim small target detection problem in the complex environment. First, to solve the uneven distribution of positive and negative samples, the designed smoothness operator is used to suppress the background and enhancement target by measuring the difference in their features in 1D and 2D gradient. Second, an infrared dim small target detection network based on dense feature fusion, namely the DFFIR-net network, is proposed. The DFFIR-net enhances the feature expression of dim small targets by integrating the original features and the smoothness features of gray-level gradient. Also, the DFFIR-net alleviates the problem of sparse feature extraction. Finally, a multiscale 2D Gaussian label generation strategy is proposed. This strategy is critical in supervising the training of DFFIR-net in multi-dimensional Gaussian space, improving the feature exploration ability of the network and detection performance under small training samples. The experimental results show that compared with the existing advanced detection methods, the proposed method has higher accuracy and lower false alarm rates in various complex scenes.