Effective Point Spread Function Research Articles

High-precision astrometry with VVV

Aims. We use near-infrared, ground-based data from the VISTA Variables in the Via Lactea (VVV) survey to indirectly extend the astrometry provided by the Gaia catalog to objects in heavily extinct regions toward the Galactic bulge and plane that are beyond Gaia’s reach. Methods. We made use of state-of-the-art techniques developed for high-precision astrometry and photometry with the Hubble Space Telescope to process the VVV data. We employed empirical, spatially variable, effective point spread functions and local transformations to mitigate the effects of systematic errors, like residual geometric distortion and image motion, and to improve measurements in crowded fields and for faint stars. We also anchored our astrometry to the absolute reference frame of Gaia Data Release 3. Results. We measure between 20 and 60 times more sources than Gaia in the region surrounding the Galactic center, obtaining a single-exposure precision of about 12 mas and a proper-motion precision of better than 1 mas yr−1 for bright, unsaturated sources. Our astrometry provides an extension of Gaia into the Galactic center. We publicly release the astro-photometric catalogs of the two VVV fields considered in this work, which contain a total of ~3.5 million sources. Our catalogs cover ~3 sq. deg, about 0.5% of the entire VVV survey area.

Virtual cylindrical PET for efficient DOI image reconstruction with sub-millimetre resolution

Objective. Image reconstruction in high resolution, narrow bore PET scanners with depth of interaction (DOI) capability presents a substantial computational challenge due to the very high sampling in detector and image space. The aim of this study is to evaluate the use of a virtual cylinder in reducing the number of lines of response (LOR) for DOI-based reconstruction in high resolution PET systems while maintaining uniform sub-millimetre spatial resolution. Approach. Virtual geometry was investigated using the awake animal mousePET as a high resolution test case. Using GEANT4 Application for Tomographic Emission (GATE), we simulated the physical scanner and three virtual cylinder implementations with detector size 0.74 mm, 0.47 mm and 0.36 mm (vPET1, vPET2 and vPET3, respectively). The virtual cylinder condenses physical LORs stemming from various crystal pairs and DOI combinations, and which intersect a single virtual detector pair, into a single virtual LOR. Quantitative comparisons of the point spread function (PSF) at various positions within the field of view (FOV) were compared for reconstructions based on the vPET implementations and the physical scanner. We also assessed the impact of the anisotropic PSFs by reconstructing images of a micro Derenzo phantom. Main results. All virtual cylinder implementations achieved LOR data compression of at least 50% for DOI PET reconstruction. PSF anisotropy in radial and tangential profiles was chiefly influenced by DOI resolution and only marginally by virtual detector size. Spatial degradation introduced by virtual cylinders was most prominent in the axial profile. All virtual cylinders achieved sub-millimetre volumetric resolution across the FOV when 6-bin DOI reconstructions (3.3 mm DOI resolution) were performed. Using vPET2 with 6 DOI bins yielded nearly identical reconstructions to the non-virtual case in the transaxial plane, with an LOR compression factor of 86%. Resolution modelling significantly reduced the effects of the asymmetric PSF arising from the non-cylindrical geometry of mousePET. Significance. Narrow bore and high resolution PET scanners require detectors with DOI capability, leading to computationally demanding reconstructions due to the large number of LORs. In this study, we show that DOI PET reconstruction with 50%–86% LOR compression is possible using virtual cylinders while maintaining sub-millimetre spatial resolution throughout the FOV. The methodology and analysis can be extended to other scanners with DOI capability intended for high resolution PET imaging.

Improving spatiotemporal image fusion incorporating unmixing step by considering the point spread function effect

ABSTRACT In this paper, an improving spatiotemporal image fusion (STIF) incorporating unmixing step by considering the point spread function (PSF) effect model (CPSF) is proposed to address the problem that unmixing accuracy in STIF models incorporating unmixing step is vulnerable to the PSF effect. First, an unsupervised clustering algorithm is used to cluster the fine image at previous date into a land cover map, and the coarse class fraction matrix is generated from this land cover map. Second, the fine class fraction matrix uncontaminated by the PSF effect is calculated by upsampling the coarse class fraction matrix using the Area-to-Point Kriging (ATPK) method under the PSF constraint. Third, this fine class fraction matrix is convolved with the ideal square wave filter to downsample, and then normalized to obtain the coarse class fraction matrix without the influence of the PSF effect. Finally, the CPSF-processed class fraction matrix is used to replace the original one in the following unmixing and prediction process to generate the fine resolution image at the prediction date. Experiments on two remote sensing data sets show that the proposed method can effectively improve the accuracy of spatiotemporal image fusion methods incorporating unmixing step.

A novel variational approach for multiphoton microscopy image restoration: from PSF estimation to 3D deconvolution

In multi-photon microscopy (MPM), a recent in-vivo fluorescence microscopy system, the task of image restoration can be decomposed into two interlinked inverse problems: firstly, the characterization of the point spread function (PSF) and subsequently, the deconvolution (i.e. deblurring) to remove the PSF effect, and reduce noise. The acquired MPM image quality is critically affected by PSF blurring and intense noise. The PSF in MPM is highly spread in 3D and is not well characterized, presenting high variability with respect to the observed objects. This makes the restoration of MPM images challenging. Common PSF estimation methods in fluorescence microscopy, including MPM, involve capturing images of sub-resolution beads, followed by quantifying the resulting ellipsoidal 3D spot. In this work, we revisit this approach, coping with its inherent limitations in terms of accuracy and practicality. We estimate the PSF from the observation of relatively large beads (approximately 1 μm in diameter). This goes through the formulation and resolution of an original non-convex minimization problem, for which we propose a proximal alternating method along with convergence guarantees. Following the PSF estimation step, we then introduce an innovative strategy to deal with the high level multiplicative noise degrading the acquisitions. We rely on a heteroscedastic noise model for which we estimate the parameters. We then solve a constrained optimization problem to restore the image, accounting for the estimated PSF and noise, while allowing a minimal hyper-parameter tuning. Theoretical guarantees are given for the restoration algorithm. These algorithmic contributions lead to an end-to-end pipeline for 3D image restoration in MPM, that we share as a publicly available Python software. We demonstrate its effectiveness through several experiments on both simulated and real data.

Millipixel accuracy optical localization for point-like targets on pixelated image sensors

Localizing point-like targets, such as stars and single molecules on pixelated image sensors, are of great importance in optical measurement fields, including astronomy and microscopy. To date, a localization accuracy of 0.01 pixels has been considered high in practical applications. It is difficult to assess or achieve a higher accuracy (e.g., millipixel level) using experimental data due to noise from conventional setups or environment. Here, we propose a millipixel-scale localization accuracy assessment and realization method. The method combines organic light emitting diode subpixel precise motion control and large-ratio optical scaling to form spot array motion with ultrahigh accuracy in digital images for localization assessment and uses effective point spread function (PSF) modeling and maximum likelihood estimation fitting to realize millipixel localization performance. A localization accuracy better than 0.001 pixels has been demonstrated on two types of practical spots. The work not only provides an experimental assessment method for localization techniques with ultrahigh accuracy but also verifies the ability of optical localization as a powerful tool to measure 2D displacements at nano-scale accuracy in advanced manufacturing.

Photometry and astrometry with JWST – III. A NIRCam-Gaia DR3 analysis of the open cluster NGC 2506

ABSTRACT In the third paper of this series aimed at developing the tools for analysing resolved stellar populations using the cameras on board of the James Webb Space Telescope (JWST), we present a detailed multiband study of the 2 Gyr Galactic open cluster NGC 2506. We employ public calibration data sets collected in multiple filters to: (i) derive improved effective Point Spread Functions (ePSFs) for 10 NIRCam filters; (ii) extract high-precision photometry and astrometry for stars in the cluster, approaching the main sequence (MS) lower mass of ∼0.1 M⊙; and (iii) take advantage of the synergy between JWST and Gaia DR3 to perform a comprehensive analysis of the cluster’s global and local properties. We derived a MS binary fraction of ∼57.5 per cent, extending the Gaia limit (∼0.8 M⊙) to lower masses (∼0.4 M⊙) with JWST. We conducted a study on the mass functions (MFs) of NGC 2506, mapping the mass segregation with Gaia data, and extending MFs to lower masses with the JWST field. We also combined information on the derived MFs to infer an estimate of the cluster present-day total mass. Lastly, we investigated the presence of white dwarfs (WDs) and identified a strong candidate. However, to firmly establish its cluster membership, as well as that of four other WD candidates and of the majority of faint low-mass MS stars, further JWST equally deep observations will be required. We make publicly available catalogues, atlases, and the improved ePSFs.

JWST-TST Proper Motions. I. High-precision NIRISS Calibration and Large Magellanic Cloud Kinematics

We develop and disseminate effective point-spread functions and geometric-distortion solutions for high-precision astrometry and photometry with the JWST NIRISS instrument. We correct field dependencies and detector effects, and assess the quality and the temporal stability of the calibrations. As a scientific application and validation, we study the proper motion (PM) kinematics of stars in the JWST calibration field near the Large Magellanic Cloud (LMC) center, comparing to a first-epoch Hubble Space Telescope (HST) archival catalog with a 16 yr baseline. For stars with G ∼ 20, the median PM uncertainty is ∼13 μas yr−1 (3.1 km s−1), better than Gaia DR3 typically achieves for its very best-measured stars. We kinematically detect the known star cluster OGLE-CL LMC 407, measure its absolute PM for the first time, and show how this differs from other LMC populations. The inferred cluster dispersion sets an upper limit of 24 μas yr−1 (5.6 km s−1) on systematic uncertainties. Red-giant-branch stars have a velocity dispersion of 33.8 ± 0.6 km s−1, while younger blue populations have a narrower velocity distribution, but with a significant kinematical substructure. We discuss how this relates to the larger velocity dispersions inferred from Gaia DR3. These results establish JWST as capable of state-of-the-art astrometry, building on the extensive legacy of HST. This is the first paper in a series by our JWST Telescope Scientist Team, in which we will use Guaranteed Time Observations to study the PM kinematics of various stellar systems in the Local Group.

Star-image Centering with Deep Learning: HST/WFPC2 Images

A deep learning (DL) algorithm is built and tested for its ability to determine centers of star images in HST/WFPC2 exposures, in filters F555W and F814W. These archival observations hold great potential for proper-motion studies, but the undersampling in the camera’s detectors presents challenges for conventional centering algorithms. Two exquisite data sets of over 600 exposures of the cluster NGC 104 in these filters are used as a testbed for training and evaluating the DL code. Results indicate a single-measurement standard error from 8.5 to 11 mpix, depending on the detector and filter. This compares favorably to the ∼20 mpix achieved with the customary “effective point spread function (PSF)” centering procedure for WFPC2 images. Importantly, the pixel-phase error is largely eliminated when using the DL method. The current tests are limited to the central portion of each detector; in future studies, the DL code will be modified to allow for the known variation of the PSF across the detectors.

Effects of point spread function correction and time-of-flight on visual contrast level and pixel values in brain PET images.

Point spread function (PSF) correction and time-of-flight (TOF) can improve the quality of PET images. None have directly assessed the visual effects of these methods in brain PET images and evaluated the image quality from these methods based on the relationship between the number of updates and noise level. The present study aimed to clarify the effects of PSF and TOF on the visual contrast level and pixel values of brain PET images using an experimental phantom. The visual contrast level was evaluated based on the sum of edge strengths. In addition, the effects of PSF, TOF, and a combination of them on pixel values were evaluated after anatomical standardization of brain images, in which the whole brain was divided into 18 segments. These were evaluated using images reconstructed with the number of updates set to achieve the same noise level. Combined application of the point spread function and TOF resulted in the greatest increase in the sum of edge strengths (32%), followed by PSF (21%) and TOF (6%). The maximum increase in pixel values occurred in the thalamic area (17%). Although PSF and TOF can increase the visual contrast level by increasing the sum of edge strengths, they may affect the results of software-based analysis using pixel values. Nonetheless, using these methods may improve the ability to visualize areas of hypoaccumulation, such as epileptic foci.

TESS–Gaia Light Curve: A PSF-based TESS FFI Light-curve Product

The Transiting Exoplanet Survey Satellite (TESS) is continuing its second extended mission after 55 sectors of observations. TESS publishes full-frame images (FFIs) at a cadence of 1800, 600, or 200 s, allowing light curves to be extracted for stars beyond a limited number of pre-selected stars. Simulations show that thousands of exoplanets, eclipsing binaries, variable stars, and other astrophysical transients can be found in these FFI light curves. To obtain high-precision light curves, we forward model the FFI with the effective point-spread function (PSF) to remove contamination from nearby stars. We adopt star positions and magnitudes from Gaia DR3 as priors. The resulting light curves, called TESS–Gaia light curves (TGLCs), show a photometric precision closely tracking the prelaunch prediction of the noise level. The TGLCs’ photometric precision reaches ≲2% at 16th TESS magnitude even in crowded fields. We publish TGLC aperture and PSF light curves for stars down to 16th TESS magnitude through the Mikulski Archive for Space Telescopes for all available sectors and will continue to deliver future light curves. The open-source package tglc 3 3 Via 10.17909/610m‐9474. is publicly available to enable any user to produce customized light curves.

A Unifying Approach to Inverse Problems of Ultrasound Beamforming and Deconvolution

Beamforming is an essential step in the ultrasound image formation pipeline and has recently attracted growing interest. An important goal of beamforming is to increase the image spatial resolution, or in other words to narrow down the system Point Spread Function (PSF). In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF. Unfortunately, these two steps have only been considered separately in a sequential approach. Herein, a novel framework for unifying beamforming and deconvolution in ultrasound image reconstruction is introduced. More specifically, the proposed formulation is a regularized inverse problem including two linear models for beamforming and deconvolution plus additional sparsity constraint. We take advantage of the alternating direction method of multipliers algorithm to find the solution of the joint optimization problem. The performance evaluation is presented on a set of publicly available simulations, real phantoms, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> data. As compared to Delay-And-Sum (DAS) beamforming, simulation results indicate improvements of 45% and 44% in terms of axial and lateral resolution, respectively. Moreover, the proposed method improves the contrast of simulation data by 6.7% in comparison to DAS. The superiority of the proposed approach in comparison with the sequential approach as well as the state-of-the-art beamforming and deconvolution approaches is also shown.

Potential scientific synergies in weak lensing studies between the CSST and Euclid space probes

Aims. With the next generation of large surveys poised to join the ranks of observational cosmology in the near future, it is important to explore their potential synergies and to maximize their scientific outcomes. In this study, we aim to investigate the complementarity of two upcoming space missions: Euclid and the China Space Station Telescope (CSST), both of which will be focused on weak gravitational lensing for cosmology. In particular, we analyze the photometric redshift (photo-z) measurements by combining NUV, u, g, r, i, z, y bands from CSST with the VIS, Y, J, H bands from Euclid, and other optical bands from the ground-based Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) and Dark Energy Survey. We also consider the advantages of combining the two space observational data in simplifying image deblending. For Euclid, weak lensing measurements use the broad optical wavelength range of 550−900 nm, for which chromatic point-spread function (PSF) effects are significant. For this purpose, the CSST narrow-band data in the optical can provide valuable information for Euclid to obtain more accurate PSF measurements and to calibrate the color and color-gradient biases for galaxy shear measurements. Methods. We created image simulations, using the Hubble Deep UV data as the input catalog, for different surveys and quantified the photo-z performance using the EAZY template fitting code. For the blending analyses, we employed high-resolution HST-ACS CANDELS F606W and F814W data to synthesize mock simulated data for Euclid, CSST, and an LSST-like survey. We analyzed the blending fraction for different cases as well as the blending effects on galaxy photometric measurements. Furthermore, we demonstrated that CSST can provide a large enough number of high signal-to-noise ratio multi-band galaxy images to calibrate the color-gradient biases for Euclid. Results. The sky coverage of Euclid lies entirely within the CSST footprint. The combination of Euclid with the CSST data can thus be done more uniformly than with the various ground-based data that are part of the Euclid survey. Our studies show that by combining Euclid and CSST, we can reach a photo-z precision of σNMAD ≈ 0.04 and an outlier fraction of η ≈ 2.4% at the nominal depth of the Euclid Wide Survey (VIS &lt; 24.5 AB mag). For CSST, including the Euclid Y, J, H bands reduces the overall photo-z outlier fraction from ∼8.5% to 2.4%. For z &gt; 1, the improvements are even more significant. Because of the similarly high resolutions, the data combination of Euclid and CSST can be relatively straightforward for photometry measurements. On the other hand, to include ground-based data, sophisticated deblending utilizing priors from high-resolution space observations are required. The multi-band data from CSST are very helpful in controlling the chromatic PSF effect for Euclid VIS shear measurements. The color-gradient bias for Euclid galaxies with different bulge-to-total flux ratio at different redshifts can be well calibrated to the level of 0.1% using galaxies from the CSST deep survey.

Validating continuous gravitational-wave candidates from a semicoherent search using Doppler modulation and an effective point spread function

Following up large numbers of candidates in continuous gravitational-wave searches presents a challenge, particularly in regard to computational power and the time required to manually scrutinize each of the candidates. It is important to design and test good follow-up procedures that are safe (i.e., minimize false dismissals) and computationally efficient across many search configurations. We investigate two follow-up procedures, or ``vetoes,'' both of which exploit the Doppler modulation predicted in astrophysical signals. In particular, we introduce the concept of using an effective point spread function as part of our veto criteria. We take advantage of a well-established semicoherent search algorithm based on a hidden Markov model to study various search configurations and to generalize the veto criteria by considering the overall veto performance in terms of efficiency and safety. The results can serve as a guideline for follow-up studies in future continuous gravitational-wave searches using a hidden Markov model algorithm. The results also apply qualitatively to other semicoherent search algorithms.

Controlling the artificial radiance of the night sky: The Añora urban laboratory

We provide radiometric evidence of the relevance of nearby light sources on the artificial brightness of the night sky. To obtain the required data we developed a method based on the use of power-regulated urban lighting systems, which also provides relevant information on the propagation of light pollution at short distances from the sources. A controlled experiment was carried out in the Andalusian municipality of Añora (1526 inhab.) in which the radiant power of its regulated public outdoor lighting system was modified in a predefined way during the central part of the night while continuously recording the zenith sky brightness in the TESS-W photometric band from two separate locations inside the town. We determined that the power-regulated streetlight sources contribute a 60–62% to the total zenith sky brightness at these observing locations in clear and moonless nights when operated at their maximum power rating, being the remaining sky radiance due to constant local sources and sources from neighboring towns. These results, in combination with georeferenced information on the sources' location and properties, impose some constraints on the functional form of the effective point-spread functions (PSF) of the zenith sky brightness at short distances from the sources. For the conditions of our experiment we have found that the expected exponents of power-law PSFs are close to -1.

Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI

Abstract. Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements. A set of observational and a priori constraints are developed in SIAC to constrain an estimate of coarse resolution (500 m) aerosol optical thickness (AOT) and total column water vapour (TCWV), along with associated uncertainty. This is then used to estimate the medium resolution (10–60 m) surface reflectance and uncertainty, given an assumed uncertainty of 5 % in TOA reflectance. The coarse resolution a priori constraints used are the MODIS MCD43 BRDF/Albedo product, giving a constraint on 500 m surface reflectance, and the Copernicus Atmosphere Monitoring Service (CAMS) operational forecasts of AOT and TCWV, providing estimates of atmospheric state at core 40 km spatial resolution, with an associated 500 m resolution spatial correlation model. The mapping in spatial scale between medium resolution observations and the coarser resolution constraints is achieved using a calibrated effective point spread function for MCD43. Efficient approximations (emulators) to the outputs of the 6S atmospheric radiative transfer code are used to estimate the state parameters in the atmospheric correction stage. SIAC is demonstrated for a set of global S2 and L8 images covering AERONET and RadCalNet sites. AOT retrievals show a very high correlation to AERONET estimates (correlation coefficient around 0.86, RMSE of 0.07 for both sensors), although with a small bias in AOT. TCWV is accurately retrieved from both sensors (correlation coefficient over 0.96, RMSE &lt;0.32 g cm−2). Comparisons with in situ surface reflectance measurements from the RadCalNet network show that SIAC provides accurate estimates of surface reflectance across the entire spectrum, with RMSE mismatches with the reference data between 0.01 and 0.02 in units of reflectance for both S2 and L8. For near-simultaneous S2 and L8 acquisitions, there is a very tight relationship (correlation coefficient over 0.95 for all common bands) between surface reflectance from both sensors, with negligible biases. Uncertainty estimates are assessed through discrepancy analysis and are found to provide viable estimates for AOT and TCWV. For surface reflectance, they give conservative estimates of uncertainty, suggesting that a lower estimate of TOA reflectance uncertainty might be appropriate.

Relationship between the kernel size of a convolutional layer and the optical point spread function in ghost imaging using deep learning for identifying defect locations.

We explore the contribution of convolutional neural networks to correcting for the effect of the point spread function (PSF) of the optics when applying ghost imaging (GI) combined with deep learning to identify defect positions in materials. GI can be accelerated by combining GI and deep learning. However, no method has been established for determining the relevant model parameters. A simple model with different kernel sizes was built. Its accuracy was evaluated for data containing the effects of different PSFs. Numerical analysis and empirical experiments demonstrate that the accuracy of defect identification improved by matching the kernel size with the PSF of the optics.

Deep learning-based point-spread function deconvolution for migration image deblurring

High-resolution seismic imaging is crucial for deriving an accurate geologic section and ensuring successful petroleum exploration. However, traditional high-resolution imaging methods, such as least-squares reverse time migration (LSRTM) and migration deconvolution, usually involve high computational costs for approximating the inverse Hessian matrix with limited improvements in resolution. To obtain high-resolution migration images and reduce computing costs, we present a deep learning-based point-spread function (PSF) deconvolution method. We decompose the large Hessian matrix into small dispersed PSFs and design a convolutional neural network (CNN) to automatically predict the deconvolution operator of every single PSF. The deconvolution operator eliminates the PSF effect and balances the insufficient illumination of the acquisition geometry. To obtain an efficient CNN model for predicting deconvolution operators, we trained 2500 pairs of PSFs and their corresponding deconvolution operators collected from part of a model using the regular PSF deconvolution method. After training the network, we could predict all deconvolution operators in a few seconds for the following processing of migration images. The results on synthetic and field-data applications indicate that our method could provide a deblurred migration image comparable to that of the LSRTM approaches with significantly reduced computational and memory costs.

New CCD Astrometric Observations of Himalia Using Gaia DR2 in 2015–2021

Abstract The long arc and high-quality astrometric measurements of outer irregular satellites are prerequisites for improving their orbital theories and increasing the precision of their ephemerides. In order to obtain good astrometric positions of Himalia, the largest irregular satellite of Jupiter, we have processed and reduced 911 ground-based CCD frames obtained between 2015 and 2021 by three telescopes (including 1 and 2.4 m telescopes at Yunnan Observatory, and 0.8 m telescope at Purple Mountain Observatory) over 61 nights. Subtracting off the companion star of our target by constructing an effective point-spread function (ePSF) model in some CCD frames, the ePSF-subtracted technique is used to reduce the centering error. Some additional techniques are applied in data reduction to further improve positional accuracy and precision of Himalia. This includes geometric distortion correction, weighted polynomial plate models, and the precision-premium effect, since their relative positional measurements have better precision when two objects are very near (e.g., less than 60″). The star catalog Gaia DR2 is used for astrometric calibration, and theoretical positions of Himalia are retrieved from JPL Horizons ephemeris, including the satellite ephemeris Jup344 and the newest planetary ephemeris DE441. Our results show the mean (O − C)s (observed minus computed) of the positional residuals of Himalia are −0."004 and 0.″005 in R.A. and decl., respectively, and their corresponding standard deviations are about 0.″020 in each direction.

Interpolation sub-pixel mapping based on multiple subpixel shifted images based on a consideration of the point spread function effect

ABSTRACT In this paper, the interpolation sub-pixel mapping (ISM) based on multiple sub-pixel shifted images (MSSIs) based on a consideration of the point spread function (PSF) effect, named as MSSI-PSF, is proposed. In the proposed MSSI-PSF, MSSIs are first unmixed to produce the coarse fractional images. Second, under considering the PSF effect, the improved coarse fractional images are obtained by applying the area-to-point kriging (ATPK) and then the ideal square-wave filter to the coarse fractional images. Third, the improved coarse fractional images are upsampled by interpolation to obtain the upsampled fractional images, and then the upsampled fractional images are integrated to obtain a fine fractional image. Finally, according to the fine fractional images from all MSSIs, the class allocation method is utilized to assign class labels to sub-pixels to obtain the final mapping result. The experimental results show that the proposed MSSI-PSF model produces better mapping result than the existing ISM models.

Investigation and application of fitting models for centering algorithms in astrometry

To determine the precise positions of stars in CCD frames, various centering algorithms have been proposed for astrometry. The effective point spread function (ePSF) and the Gaussian centering algorithms are two representative centering algorithms. This paper compares in detail and investigates these two centering algorithms in performing data reduction. Specifically, synthetic star images in different conditions (i.e. profiles, fluxes, backgrounds and full width at half maximums) are generated and processed. We find that the difference in precision between the two algorithms is related to the profiles of the star images. Therefore, the precision comparison results using an ideal Gaussian-profile star image cannot be extended to other more specific experimental scenarios. Based on the simulation results, the most appropriate algorithm can be selected according to the image characteristics of observations, and the loss of precision of other algorithms can be estimated. The conclusions are verified using observations captured by the 1-m and 2.4-m telescopes at Yunnan Observatory.