PSF Model Research Articles

HOLISMOKES. XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys

While supervised neural networks have become state of the art for identifying the rare strong gravitational lenses from large imaging data sets, their selection remains significantly affected by the large number and diversity of non-lens contaminants. This work evaluates and compares systematically the performance of neural networks in order to move towards a rapid selection of galaxy-scale strong lenses with minimal human input in the era of deep, wide-scale surveys. We used multiband images from PDR2 of the Hyper-Suprime Cam (HSC) Wide survey to build test sets mimicking an actual classification experiment, with 189 securely-identified strong lenses from the literature over the HSC footprint and 70\,910 non-lens galaxies in COSMOS covering representative lens-like morphologies. Multiple networks were trained on different sets of realistic strong-lens simulations and non-lens galaxies, with various architectures and data preprocessing, mainly using the deepest $gri$-bands. Most networks reached excellent area under the Receiver Operating Characteristic (ROC) curves on the test set of 71,099 objects, and we determined the ingredients to optimize the true positive rate for a total number of false positives equal to zero or 10 $ and TPR$_ $). The overall performances strongly depend on the construction of the ground-truth training data and they typically, but not systematically, improve using our baseline residual network architecture presented in HOLISMOKES VI. TPR$_ $ tends to be higher for ResNets (simeq 10--40<!PCT!>) compared to AlexNet-like networks or G-CNNs. Improvements are found when (1) applying random shifts to the image centroids, (2) using square-root scaled images to enhance faint arcs, (3) adding $z$-band to the otherwise used $gri$-bands, or (4) using random viewpoints of the original images. In contrast, we find no improvement when adding $g - i$ difference images (where alpha is a tuned constant) to subtract emission from the central galaxy. The most significant gain is obtained with committees of networks trained on different data sets, with a moderate overlap between populations of false positives. Nearly-perfect invariance to image quality can be achieved by using realistic PSF models in our lens simulation pipeline, and by training networks either with large number of bands, or jointly with the PSF and science frames. Overall, we show the possibility to reach a $ as high as 60<!PCT!> for the test sets under consideration, which opens promising perspectives for pure selection of strong lenses without human input using the Rubin Observatory and other forthcoming ground-based surveys.

Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning

Dual panel PET systems, such as Breast-PET (B-PET) scanner, exhibit strong asymmetric and anisotropic spatially-variant deformations in the reconstructed images due to the limited-angle data and strong depth of interaction effects for the oblique LORs inherent in such systems. In our previous work, we studied time-of-flight (TOF) effects and image-based spatially-variant PSF resolution models within dual-panel PET reconstruction to reduce these deformations. The application of PSF based models led to better and more uniform quantification of small lesions across the field of view (FOV). However, the ability of such a model to correct for PSF deformation is limited to small objects. On the other hand, large object deformations caused by the limited-angle reconstruction cannot be corrected with the PSF modeling alone. In this work, we investigate the ability of deep-learning (DL) networks to recover such strong spatially-variant image deformations using first simulated PSF deformations in image space of a generic dual panel PET system and then using simulated and acquired phantom reconstructions from dual panel B-PET system developed in our lab at University of Pennsylvania. For the studies using real B-PET data, the network was trained on the simulated synthetic data sets providing ground truth for objects resembling experimentally acquired phantoms on which the network deformation corrections were then tested. The synthetic and acquired limited-angle B-PET data were reconstructed using DIRECT-RAMLA reconstructions, which were then used as the network inputs. Our results demonstrate that DL approaches can significantly eliminate deformations of limited angle systems and improve their quantitative performance.

Development of the quantitative PET prostate phantom (Q3P) for improved quality assurance of 18F-PSMA PET imaging in metastatic prostate cancer.

Phantoms are commonly used to evaluate and compare the performance of imaging systems given the known ground truth. Positron emission tomography (PET) scanners are routinely validated using the NEMA image quality phantom, in which lesions are modeled using 10 to 37mm fillable spheres. The NEMA phantom neglects, however, to model focal (3-10-mm), high-uptake lesions that are increasingly observed in prostate-specific membrane antigen (PSMA) PET images. PSMA-targeting radiopharmaceuticals allow for enhanced detection of metastatic prostate cancers. As such, there is significant need to develop an updated phantom which considers both the quantitative and lesion detectability of this new paradigm in oncological PET imaging. In this work, we present the Quantitative PET Prostate Phantom (Q3P); a portable and modular phantom that can be used to improve and harmonize imaging protocols for 18F-PSMA PET scans. A one-piece cylindrical phantom was designed effectively in two halves, which we call modules. Module 1 was designed to mimic lesions in the presence of background, and Module 2 mimicked very high contrast conditions (i.e., very low background) that can be observed in 18F-PSMA PET scans. Shell-less radioactive spheres (3-16-mm) were cast using epoxy resin mixed with sodium-22 (22Na), a long half-life positron emitter with positron range similar to 18F. To establish realistic lesion contrast, the 22Na spheres were mounted in a cylindrical chamber that can be filled with an 18F background (module 1). Thirteen exchangeable spherical cavity inserts (3-37-mm) were machined in two parts and solvent welded together, and filled with 18F (50 kBq/mL) to model lesions with very high contrast (module 2). Five 2.5-min PET scans were acquired on a 5-ring GE Discovery MI PET/CT scanner (General Electric, USA). Lesions were segmented using 41% of SUVmax fixed thresholding (41% FT) and recovery coefficients (RCs) were computed from 5 noise realizations. The manufactured phantom is portable (5.7kg) and scan preparation takes less than 40min. The total 22Na activity is 250 kBq, allowing it to be shipped as an exempt package under International Atomic Energy Agency (IAEA) regulations. Recovery coefficients, computed using PSF modeling and no post-reconstruction smoothing, were 130.3% (16mm), 147.1% (10mm), 87.2% (6mm), and 7.0% (3mm) for RCmax, which decreased to 91.1% (16mm), 90.6% (10mm), 53.2% (6mm), and 3.6% (3mm) for RCmean in the 22Na spheres. Comparatively, 18F sphere recovery was 110.7% (17mm), 123.6% (10mm), 106.5% (7mm), and 23.3% (3mm) for RCmax, which was reduced to 76.7% (17mm), 77.7% (10mm), 66.8% (7mm), and 13.5% (3mm), for RCmean. A standardized imaging phantom was developed for lesion quantification assessment in 18F-PSMA PET images. The phantom is configurable, providing users with the opportunity to modify background activity levels or sphere sizes according to clinical demands. Distributed to the community, the Q3P phantom has the potential to enable better assessment of lesion quantification and harmonization of 18F-PSMA PET imaging, which may lead to more robust predictive metrics and better outcome prediction in metastatic prostate cancer.

Resolution recovery on list mode MLEM reconstruction for dynamic cardiac SPECT system

The Dynamic Cardiac SPECT (DC-SPECT) system is being developed at the Massachusetts General Hospital, featuring a static cardio focus asymmetrical geometry enabling simultaneous high-resolution and high-sensitivity imaging. Among 14 design iterations of the DC-SPECT with varying number of detector heads, system sensitivity and resolution, the current version under development features 10 mm FWHM geometrical resolution (without resolution recovery) and 0.07% sensitivity at the center of the FOV, this is 1.5× resolution gain and 7× sensitivity gain compared to a conventional dual head gamma camera (0.01% sensitivity and 15-mm resolution). This work presents improvement in imaging resolution by implementing a spatially variant point spread function (SV-PSF) with list mode MLEM reconstruction. A resolution recovery method by PSF deconvolution is validated on list mode MLEM reconstruction for the DC-SPECT. A spatial invariant PSF is included as an additional test to show the influence of the PSF modelling accuracy on reconstructed image quality. We compare the MLEM reconstruction with and without PSF deconvolution; an analytic model is used for the calculation of system response, and the results are compared to the reconstruction with system modelling using Monte Carlo (MC) based methods. Results show that with PSF modelling applied, the quality of the reconstructed image is improved, and the DC-SPECT system can achieve a 4.5 mm central spatial resolution with average 795 counts/Mbq. Both the SV-PSF and the spatial-invariant PSF improve the image quality, and the reconstruction with SV-PSF generates line profiles closer to the ground truth. The results show substantial improvement over the GE Discovery 570c performance (7 mm spatial resolution with an average 460 counts/MBq, 5.8 mm resolution at the FOV center). The impact of PSF deconvolution is significant, improvement of the reconstructed image quality is evident in comparison to MC simulated system matrix with the same sampling size in the simulation.

Near-IR Weak-lensing (NIRWL) Measurements in the CANDELS Fields. I. Point-spread Function Modeling and Systematics

We have undertaken a near-IR weak-lensing (NIRWL) analysis of the CANDELS HST/WFC3-IR F160W observations. With the Gaia proper motion–corrected catalog as an astrometric reference, we updated the astrometry of the five CANDELS mosaics and achieved an absolute alignment within 0.″02 ± 0.″02, on average, which is a factor of several superior to existing mosaics. These mosaics are available to download (https://drive.google.com/drive/folders/1k9WEV3tBOuRKBlcaTJ0-wTZnUCisS__r). We investigated the systematic effects that need to be corrected for weak-lensing measurements. We find that the largest contributing systematic effect is caused by undersampling. We find a subpixel centroid dependence on the PSF shape that causes the PSF ellipticity and size to vary by up to 0.02 and 3%, respectively. Using the UDS as an example field, we show that undersampling induces a multiplicative shear bias of −0.025. We find that the brighter-fatter effect causes a 2% increase in the size of the PSF and discover a brighter-rounder effect that changes the ellipticity by 0.006. Based on the small range of slopes in a galaxy’s spectral energy distribution (SED) within the WFC3-IR bandpasses, we suggest that the impact of the galaxy SED on the PSF is minor. Finally, we model the PSF of WFC3-IR F160W for weak lensing using a principal component analysis. The PSF models account for temporal and spatial variations of the PSF. The PSF corrections result in residual ellipticities and sizes, ∣de 1∣ < 0.0005 ± 0.0003, ∣de 2∣ < 0.0005 ± 0.0003, and ∣dR∣ < 0.0005 ± 0.0001, that are sufficient for the upcoming NIRWL search for massive overdensities in the five CANDELS fields.

Reconstruction of multi-animal PET acquisitions with anisotropically variant PSF

Among other factors such as random, attenuation and scatter corrections, uniform spatial resolution is key to performing accurate quantitative studies in Positron emission tomography (PET). Particularly in preclinical PET studies involving simultaneous acquisition of multiple animals, the degradation of image resolution due to the depth of interaction (DOI) effect far from the center of the Field of View (FOV) becomes a significant concern. In this work, we incorporated a spatially-variant resolution model into a real time iterative reconstruction code to obtain accurate images of multi-animal acquisition. We estimated the spatially variant point spread function (SV-PSF) across the FOV using measurements and Monte Carlo (MC) simulations. The SV-PSF obtained was implemented in a GPU-based Ordered subset expectation maximization (OSEM) reconstruction code, which includes scatter, attenuation and random corrections. The method was evaluated with acquisitions from two preclinical PET/CT scanners of the SEDECAL Argus family: a Derenzo phantom placed 2 cm off center in the 4R-SuperArgus, and a multi-animal study with 4 mice in the 6R-SuperArgus. The SV-PSF reconstructions showed uniform spatial resolution without significant increase in reconstruction time, with superior image quality compared to the uniform PSF model.

A general framework for removing point-spread function additive systematics in cosmological weak lensing analysis

ABSTRACT Cosmological weak lensing measurements rely on a precise measurement of the shear two-point correlation function (2PCF) along with a deep understanding of systematics that affect it. In this work, we demonstrate a general framework for detecting and modelling the impact of PSF systematics on the cosmic shear 2PCF and mitigating its impact on cosmological analysis. Our framework can detect PSF leakage and modelling error from all spin-2 quantities contributed by the PSF second and higher moments, rather than just the second moments, using the cross-correlations between galaxy shapes and PSF moments. We interpret null tests using the HSC Year 3 (Y3) catalogs with this formalism and find that leakage from the spin-2 combination of PSF fourth moments is the leading contributor to additive shear systematics, with total contamination that is an order-of-magnitude higher than that contributed by PSF second moments alone. We conducted a mock cosmic shear analysis for HSC Y3 and find that, if uncorrected, PSF systematics can bias the cosmological parameters Ωm and S8 by ∼0.3σ. The traditional second moment-based model can only correct for a 0.1σ bias, leaving the contamination largely uncorrected. We conclude it is necessary to model both PSF second and fourth moment contaminations for HSC Y3 cosmic shear analysis. We also reanalyse the HSC Y1 cosmic shear analysis with our updated systematics model and identify a 0.07σ bias on Ωm when using the more restricted second moment model from the original analysis. We demonstrate how to self-consistently use the method in both real space and Fourier space, assess shear systematics in tomographic bins, and test for PSF model overfitting.

Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework

Context. High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter. Aims. Existing post-processing algorithms do not use all prior domain knowledge that is available about the problem. We propose a new method that builds on our understanding of the systematic noise and the causal structure of the data-generating process. Methods. Our algorithm is based on a modified version of half-sibling regression (HSR), a flexible denoising framework that combines ideas from the fields of machine learning and causality. We adapted the method to address the specific requirements of high-contrast exoplanet imaging data obtained in pupil tracking mode. The key idea is to estimate the systematic noise in a pixel by regressing the time series of this pixel onto a set of causally independent, signal-free predictor pixels. We use regularized linear models in this work; however, other (nonlinear) models are also possible. In a second step, we demonstrate how the HSR framework allows us to incorporate observing conditions such as wind speed or air temperature as additional predictors. Results. When we applied our method to four data sets from the VLT/NACO instrument, our algorithm provided a better false-positive fraction than a popular baseline method in the field. Additionally, we found that the HSR-based method provides direct and accurate estimates for the contrast of the exoplanets without the need to insert artificial companions for calibration in the data sets. Finally, we present a first piece of evidence that using the observing conditions as additional predictors can improve the results. Conclusions. Our HSR-based method provides an alternative, flexible, and promising approach to the challenge of modeling and subtracting the stellar PSF and systematic noise in exoplanet imaging data.

Global fitting for high-accuracy multi-channel single-molecule localization

Multi-channel detection in single-molecule localization microscopy greatly increases information content for various biological applications. Here, we present globLoc, a graphics processing unit based global fitting algorithm with flexible PSF modeling and parameter sharing, to extract maximum information from multi-channel single molecule data. As signals in multi-channel data are highly correlated, globLoc links parameters such as 3D coordinates or photon counts across channels, improving localization precision and robustness. We show, both in simulations and experiments, that global fitting can substantially improve the 3D localization precision for biplane and 4Pi single-molecule localization microscopy and color assignment for ratiometric multicolor imaging.

Dark Energy Survey Year 3 Results: Three-point shear correlations and mass aperture moments

We present high signal-to-noise measurements of three-point shear correlations and the third moment of the mass aperture statistic using the first 3 years of data from the Dark Energy Survey. We additionally obtain the first measurements of the configuration and scale dependence of the four three-point shear correlations which carry cosmological information. With the third-order mass aperture statistic, we present tomographic measurements over angular scales of 4 to 60 arcminutes with a combined statistical significance of 15.0σ. Using the tomographic information and measuring also the second-order mass aperture, we additionally obtain a skewness parameter and its redshift evolution. We find that the amplitudes and scale-dependence of these shear 3pt functions are in qualitative agreement with measurements in a mock galaxy catalog based on N-body simulations, indicating promise for including them in future cosmological analyses. We validate our measurements by showing that B-modes, parity-violating contributions and PSF modeling uncertainties are negligible, and determine that the measured signals are likely to be of astrophysical and gravitational origin.1 MoreReceived 24 January 2022Accepted 26 April 2022DOI:https://doi.org/10.1103/PhysRevD.105.103537© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCosmologyGravitational lensesGravitation, Cosmology & Astrophysics

The three-year shear catalog of the Subaru Hyper Suprime-Cam SSP Survey

Abstract We present the galaxy shear catalog that will be used for the three-year cosmological weak gravitational lensing analyses using data from the Wide layer of the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) Survey. The galaxy shapes are measured from the i-band imaging data acquired from 2014 to 2019 and calibrated with image simulations that resemble the observing conditions of the survey based on training galaxy images from the Hubble Space Telescope in the COSMOS region. The catalog covers an area of 433.48 deg2 of the northern sky, split into six fields. The mean i-band seeing is ${0{^{\prime \prime}_{.}}59}$. With conservative galaxy selection criteria (e.g., i-band magnitude brighter than 24.5), the observed raw galaxy number density is 22.9 arcmin−2, and the effective galaxy number density is 19.9 arcmin−2. The calibration removes the galaxy property-dependent shear estimation bias to the level |δm| &amp;lt; 9 × 10−3. The bias residual δm shows no dependence on redshift in the range 0 &amp;lt; z ≤ 3. We define the requirements for cosmological weak-lensing science for this shear catalog, and quantify potential systematics in the catalog using a series of internal null tests for systematics related to point-spread function modelling and shear estimation. A variety of the null tests are statistically consistent with zero or within requirements, but (i) there is evidence for PSF model shape residual correlations; and (ii) star–galaxy shape correlations reveal additive systematics. Both effects become significant on &amp;gt;1° scales and will require mitigation during the inference of cosmological parameters using cosmic shear measurements.

SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration

Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. However, choosing the best machine learning approach is still a challenging task. Therefore, in this study, the predictability comparisons have been made with the different machine learning methods used to model the MIT for iron dust. The MIT of iron dust was determined using the Godbert-Greenwald furnace for seventy unique combinations of dispersion pressures and dust concentrations. The data has been divided into 'Training Set' and 'Testing Set'. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. The support vector machines (SVM) regression models were trained with various kernel functions to enhance the performance of the resultant model. The cubic kernel function was found suitable for training SVMs. Besides, a feed-forward artificial neural network with the backpropagation algorithm and a polynomial surface fit model have also been developed to predict the MIT. For statistical phenomena, such as MIT, predictive modelling based on real-time experimental data is critical. If an accurate estimate of the combustible dust's minimum ignition temperature is confirmed, it is possible to ensure that the temperatures of the surrounding hot surfaces do not rise to that level, preventing an explosion. An overall comparison of predictive models has been given with unseen test data set. All the trained models yielded comparable results with unseen test data set. However, the SVM model with Bayesian optimizer approach can effectively assess the risk of ignition based on dust MIT under the influence of dispersion pressure and dust cloud concentration among all the approaches adopted in this study.

Fundamental precision bounds for three-dimensional optical localization microscopy using self-interference digital holography

Localization based microscopy using self-interference digital holography (SIDH) provides three-dimensional (3D) positional information about point sources with nanometer scale precision. To understand the performance limits of SIDH, here we calculate the theoretical limit to localization precision for SIDH when designed with two different configurations. One configuration creates the hologram using a plane wave and a spherical wave while the second configuration creates the hologram using two spherical waves. We further compare the calculated precision bounds to the 3D single molecule localization precision from different Point Spread Functions. SIDH results in almost constant localization precision in all three dimensions for a 20 µm thick depth of field. For high signal-to-background ratio (SBR), SIDH on average achieves better localization precision. For lower SBR values, the large size of the hologram on the detector becomes a problem, and PSF models perform better.

Analysis of attenuation coefficient estimation in Fourier-domain OCT of semi-infinite media.

The attenuation coefficient (AC) is an optical property of tissue that can be estimated from optical coherence tomography (OCT) data. In this paper, we aim to estimate the AC accurately by compensating for the shape of the focused beam. For this, we propose a method to estimate the axial PSF model parameters and AC by fitting a model for an OCT signal in a homogenous sample to the recorded OCT signal. In addition, we employ numerical analysis to obtain the theoretical optimal precision of the estimated parameters for different experimental setups. Finally, the method is applied to OCT B-scans obtained from homogeneous samples. The numerical and experimental results show accurate estimations of the AC and the focus location when the focus is located inside the sample.

Detection of sub-centimeter lesions using digital TOF-PET/CT system combined with Bayesian penalized likelihood reconstruction algorithm.

Many advances in PET/CT technology can potentially improve image quality and the ability to detect small lesions. A new digital TOF-PET/CT scanner based on silicon photomultipliers (SiPM) integrated with a Bayesian penalized likelihood (BPL) PET reconstruction algorithm (Q.Clear; GE Healthcare) has been introduced into clinical practice. The present study aimed to quantify the ability of a digital TOF-PET/CT scanner combined with BPL reconstruction to detect small lesions, and to determine the optimal penalization factor (β) in BPL to accurately detect such lesions. All PET data were acquired from a NEMA body phantom using a Discovery MI (DMI) PET/CT system (GE Healthcare). The phantom included six spheres with diameters of 4, 5, 6, 8, 10, and 13mm, and contained a background activity level of 5.3kBq/mL, with target-to-background ratios (TBR) of 4:1 and 8:1. Images were reconstructed using a baseline OSEM algorithm, with OSEM + PSF, OSEM + TOF, OSEM + PSF + TOF, and BPL + PSF + TOF (β: 50-400). The matrix size was 192 × 192 and 384 × 384. Data acquired in 100-min list mode were re-binned into acquisition times ranging from 2 to 100min. The quantitative accuracy and detectability of small hot spheres were evaluated by physical assessment of a recovery coefficient (RC) and a detectability index (DI), as well as visual assessment of PET images at each acquisition time. The RC and DI of sub-centimeter spheres were improved, because the digital TOF-PET/CT scanner has a larger TOF performance gain due to better timing resolution. The RC and DI were higher with BPL in sub-centimeter spheres, than with other OSEM-based types of reconstruction. The BPL for an 8-mm sphere overestimated uptake due to edge artifact overshoot induced by PSF modeling. The variability of RC and DI for acquisition times and TBR differed considerably according to β values. The RC for ~ 8-mm spheres were > 1 at β values between 50 and 100, but were close to 1 at β value of 200. The visual scores for β = 200 in BPL were maximal, whereas those for spheres that were ≥ 6mm exceeded the criterion of 3. The BPL in the digital TOF-PET/CT scanner improved the quantitation and detectability of sub-centimeter spheres compared with OSEM-based reconstruction. Optimization of the β value in BPL might allow the detection of lesions ≤ 6mm, although detectability depended on the TBR of lesions. A β value of 200 seemed optimal for detecting sub-centimeter lesions.

Cosmological constraints from cosmic shear two-point correlation functions with HSC survey first-year data

Abstract We present measurements of cosmic shear two-point correlation functions (TPCFs) from Hyper Suprime-Cam Subaru Strategic Program (HSC) first-year data, and derive cosmological constraints based on a blind analysis. The HSC first-year shape catalog is divided into four tomographic redshift bins ranging from $z=0.3$ to 1.5 with equal widths of $\Delta z =0.3$. The unweighted galaxy number densities in each tomographic bin are 5.9, 5.9, 4.3, and $2.4\:$arcmin$^{-2}$ from the lowest to highest redshifts, respectively. We adopt the standard TPCF estimators, $\xi _\pm$, for our cosmological analysis, given that we find no evidence of significant B-mode shear. The TPCFs are detected at high significance for all 10 combinations of auto- and cross-tomographic bins over a wide angular range, yielding a total signal-to-noise ratio of 19 in the angular ranges adopted in the cosmological analysis, $7^{\prime }&amp;lt;\theta &amp;lt;56^{\prime }$ for $\xi _+$ and $28^{\prime }&amp;lt;\theta &amp;lt;178^{\prime }$ for $\xi _-$. We perform the standard Bayesian likelihood analysis for cosmological inference from the measured cosmic shear TPCFs, including contributions from intrinsic alignment of galaxies as well as systematic effects from PSF model errors, shear calibration uncertainty, and source redshift distribution errors. We adopt a covariance matrix derived from realistic mock catalogs constructed from full-sky gravitational lensing simulations that fully account for survey geometry and measurement noise. For a flat $\Lambda$ cold dark matter model, we find $S\,_8 \equiv \sigma _8\sqrt{\Omega _{\rm m}/0.3}=0.804_{-0.029}^{+0.032}$, and $\Omega _{\rm m}=0.346_{-0.100}^{+0.052}$. We carefully check the robustness of the cosmological results against astrophysical modeling uncertainties and systematic uncertainties in measurements, and find that none of them has a significant impact on the cosmological constraints.

Parameter-free Gaussian PSF Model for Extended Depth of Field in Brightfield Microscopy.

Due to their limited depth of field, conventional brightfield microscopes cannot image thick specimens entirely in focus. A common way to obtain an all-in-focus image is to acquire a z-stack of images by optically sectioning the specimen and then apply a multi-focus fusion method. Unfortunately, for undersampled image stacks, fusion methods cannot remove the blur in regions where the in-focus position is between two optical sections. In this work, we propose a parameter-free Gaussian PSF model in which the all-in-focus image together with both the depth map and sampling distances in image plane are estimated from the image sequence automatically, without knowledge on the z-stack acquisition. In a maximum a posteriori framework, an iteratively reweighted least squares method is used to estimate the image and an adaptive scaled gradient descent method is utilized to estimate the depth map and sampling distances efficiently. Experiments on synthetic and real data demonstrate that the proposed method outperforms the current state-of-the-art, mitigating fusion artifacts and recovering sharper edges.

Second data release of the Hyper Suprime-Cam Subaru Strategic Program

Abstract This paper presents the second data release of the Hyper Suprime-Cam Subaru Strategic Program, a wide-field optical imaging survey using the 8.2 m Subaru Telescope. The release includes data from 174 nights of observation through 2018 January. The Wide layer data cover about 300 deg$^2$ in all five broad-band filters ($grizy$) to the nominal survey exposure (10 min in $gr$ and 20 min in $izy$). Partially observed areas are also included in the release; about 1100 deg$^2$ is observed in at least one filter and one exposure. The median seeing in the i-band is ${0_{.}^{\prime \prime }6}$, demonstrating the superb image quality of the survey. The Deep (26 deg$^2$) and UltraDeep (4 deg$^2$) data are jointly processed and the UltraDeep-COSMOS field reaches an unprecedented depth of $i\sim 28$ at $5 \, \sigma$ for point sources. In addition to the broad-band data, narrow-band data are also available in the Deep and UltraDeep fields. This release includes a major update to the processing pipeline, including improved sky subtraction, PSF modeling, object detection, and artifact rejection. The overall data quality has been improved, but this release is not without problems; there is a persistent deblender problem as well as new issues with masks around bright stars. The user is encouraged to review the issue list before utilizing the data for scientific explorations. All the image products as well as catalog products are available for download. The catalogs are also loaded into a database, which provides an easy interface for users to retrieve data for objects of interest. In addition to these main data products, detailed galaxy shape measurements withheld from Public Data Release 1 (PDR1) are now available to the community. The shape catalog is drawn from the S16A internal release, which has a larger area than PDR1 (160 deg$^2$). All products are available at the data release site, https://hsc-release.mtk.nao.ac.jp/.

Separating extended disc features from the protoplanet in PDS 70 using VLT/SINFONI

Transition discs are prime targets to look for protoplanets and study planet-disc interactions. We present VLT/SINFONI observations of PDS~70, a transition disc with a recently claimed embedded protoplanet. We take advantage of the angular and spectral diversity present in our data for an optimal PSF modeling and subtraction using principal component analysis (PCA). We report the redetection of PDS 70 b, both the front and far side of the outer disc edge, and the detection of several extended features in the annular gap. We compare spectral differential imaging applied before (PCA-SADI), and after (PCA-ASDI) angular differential imaging. Our tests suggest that PCA-SADI better recovers extended features, while PCA-ASDI is more sensitive to point sources. We adapted the negative fake companion (NEGFC) technique to infer the astrometry of the companion, and derived $r = 193.5 \pm 4.9 \mathrm{mas}$ and PA = 158.7deg $\pm$ 3.0deg. We used both NEGFC and ANDROMEDA to infer the $K$-band spectro-photometry of the protoplanet, and found results consistent with recent VLT/SPHERE observations, except for their 2018/02 epoch measurement in the $K2$ filter. Finally, we derived an upper limit of $\dot{M_b} < 1.26 \times 10^{-7} \big[ \frac{5 M_{\rm Jup}}{M_b} \big] \big[ \frac{R_b}{R_{\rm Jup}}\big] M_{\rm Jup} $ yr$^{-1}$ for the accretion rate of the companion based on an adaptation of PCA-SADI/PCA-ASDI around the Br$\gamma$ line.

CCD astrometric observations of 2017 VR12, Camillo and Midas

We have observed three near-Earth objects (NEOs), 2017 VR12, Camillo and Midas, during 2018. The observations were made with the 1-m telescope, operated by Yunnan Observatories, over two nights. Their precise astrometric positions are derived from 989 CCD observations. The theoretical positions of asteroids are retrieved from the Jet Propulsion Laboratory (JPL) Horizons System and Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE). The positions of three asteroids are measured with respect to stars in the Gaia DR2 star catalog. For 2017 VR12, the means (O−C) of right ascension and declination are −0.090′′ and −0.623′′ respectively based on the published JPL ephemeris, but the correspondingmeans (O − C) are 3.122′′ and −0.636′′ based on the published IMCCE ephemeris. The great difference in declination could be explained by several factors. (1) The degraded CCD images caused by the fast apparent motion of the objects lead to a reduction in positioning accuracy. (2) The poor timing system may introduce systematic errors, especially in the high speed direction. (3) The asteroid may be perturbed by Earth when it approaches the Earth too closely. These astrometric results demonstrate that the centroid centering method can reduce the dispersion of non-Gaussian images as compared with the PSF modeling method. For Camillo and Midas, the astrometric results are consistent based on the two ephemerides. Implementing a high-precision timing system, and analyzing some astronomical effects and geometric distortions in CCD images should be carefully considered in future works.