We present the application of the image coaddition algorithm, Up-sampling and PSF Deconvolution Coaddition (UPDC), for stacking multiple exposure images captured by the James Webb Space Telescope (JWST) Near-Infrared Camera (NIRCam). By addressing the point spread function (PSF) effect, UPDC provides visually enhanced and sharper images. Furthermore, the anti-aliasing and super-resolution capabilities of UPDC make it easier to deblend sources overlapped on images, yielding a higher accuracy of aperture photometry. We apply this algorithm to the SMACS J0723 imaging data. Comparative analysis with the Drizzle algorithm demonstrates significant improvements in detecting faint sources, achieving accurate photometry, and effectively deblending (super-resolution) closely packed sources. {As a result, we have newly detected a pair of close binary stars that were previously unresolvable in the original exposures or the Drizzled image.} These improvements significantly benefit various scientific projects conducted by JWST. The resulting dataset, named "UPdec-Webb", can be accessible through the official website of the Chinese Virtual Observatory (ChinaVO).
Abstract SN 2023ixf, recently reported in the nearby galaxy M101 at a distance of 6.85 Mpc, was one of the closest and brightest core-collapse supernovae in the last decade. In this work, we present multiwavelength photometric observation of SN 2023ixf with the Multi-channel Photometric Survey Telescope (Mephisto) in the uvgr bands and with the twin 50 cm telescopes in the griz bands. We find that the bolometric luminosity reached the maximum value of 3 × 10 43 erg s −1 at 3.9 days after the explosion and fully settled onto the radioactive tail at ∼90 days. The effective temperature decreased from 3.2 × 10 4 K at the first observation and approached a constant of ∼(3000–4000) K after the first 2 months. The evolution of the photospheric radius is consistent with a homologous expansion with a velocity of 8700 km s −1 in the first 2 months, and it shrunk subsequently. Based on the radioactive tail, the initial nickel mass is about M Ni ∼ 0.098 M ⊙ . The explosion energy and the ejecta mass are estimated to be E ≃ (1.0–5.7) × 10 51 erg and M ej ≃ (3.8–16) M ⊙ , respectively. The peak bolometric luminosity is proposed to be contributed by the interaction between the ejecta and the circumstellar medium (CSM). We find a shocked CSM mass of M CSM ∼ 0.013 M ⊙ , a CSM density of ρ CSM ∼ 2.5 × 10 −13 g cm −3 , and a mass-loss rate of the progenitor of Ṁ∼0.022M⊙yr−1 .
We propose a novel method to detect cosmic magnification signals by cross-correlating foreground convergence fields constructed from galaxy shear measurements with background galaxy positional distributions---namely, shear-number density correlation. We apply it to the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey data. With 27 nonindependent data points and their full covariance, ${\ensuremath{\chi}}_{0}^{2}\ensuremath{\approx}34.1$ and ${\ensuremath{\chi}}_{T}^{2}\ensuremath{\approx}24.0$ with respect to the null and the cosmological model with the parameters from HSC shear correlation analyses in Hamana et al. [Publ. Astron. Soc. Jpn. 72, 16 (2020)], respectively. The Bayes factor of the two is ${\mathrm{log}}_{10}{B}_{T0}\ensuremath{\approx}2.2$ assuming equal model probabilities of null and HSC cosmology, showing a clear detection of the magnification signals. Theoretically, the ratio of the shear-number density and shear-shear correlations can provide a constraint on the effective multiplicative shear bias $\overline{m}$ using internal data themselves. We demonstrate the idea with the signals from our HSC-SSP mock simulations and rescale the statistical uncertainties to a survey of $15000\text{ }\text{ }{\mathrm{deg}}^{2}$. For two-bin analyses with background galaxies brighter than ${m}_{\mathrm{lim}}=23$, the combined analyses lead to a forecasted constraint of $\ensuremath{\sigma}(\overline{m})\ensuremath{\sim}0.032$, 2.3 times tighter than that found when using the shear-shear correlation alone. Correspondingly, $\ensuremath{\sigma}({S}_{8})$ with ${S}_{8}={\ensuremath{\sigma}}_{8}({\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}/0.3{)}^{0.5}$ is tightened by $\ensuremath{\sim}2.1$ times. Importantly, the joint constraint on $\overline{m}$ is nearly independent of cosmological parameters. Our study therefore points to the importance of including the shear-number density correlation in weak lensing analyses, which can provide valuable consistency tests of observational data, and thus to solidify the derived cosmological constraints.
Convolutional Neutral Networks have been successfully applied in searching for strong lensing systems, leading to discoveries of new candidates from large surveys. On the other hand, systematic investigations about their robustness are still lacking. In this paper, we first construct a neutral network, and apply it to $r$-band images of Luminous Red Galaxies (LRGs) of the Kilo Degree Survey (KiDS) Data Release 3 to search for strong lensing systems. We build two sets of training samples, one fully from simulations, and the other one using the LRG stamps from KiDS observations as the foreground lens images. With the former training sample, we find 48 high probability candidates after human-inspection, and among them, 27 are newly identified. Using the latter training set, about 67\% of the aforementioned 48 candidates are also found, and there are 11 more new strong lensing candidates identified. We then carry out tests on the robustness of the network performance with respect to the variation of PSF. With the testing samples constructed using PSF in the range of 0.4 to 2 times of the median PSF of the training sample, we find that our network performs rather stable, and the degradation is small. We also investigate how the volume of the training set can affect our network performance by varying it from 0.1 millions to 0.8 millions. The output results are rather stable showing that within the considered range, our network performance is not very sensitive to the volume size.
In our previous work, we have proposed two methods for computing the luminosity distance d_{L}^{\Lambda} in LCDM model. In this paper, two effective quadrature algorithms, known as Romberg Integration and composite Gaussian Quadrature, are presented to calculate the luminosity distance d_{L}^{CPL} in the Chevallier-Polarski-Linder parametrization(CPL) model. By comparing the efficiency and accuracy of the two algorithms, we find that the second is more promising. Moreover, we develop another strategy adapted for approximating d_{L}^{\Lambda} in flat LCDM universe. To some extent, our methods can make contributions to the recent numerical stimulation for the investigation of dark energy cosmology.
Anisotropies of the cosmic optical background (COB) and cosmic near-IR background (CNIRB) are capable of addressing some of the key questions in cosmology and astrophysics. In this work, we measure and analyze the angular power spectra of the simulated COB and CNIRB in the ultra-deep field of the China Space Station Telescope (CSST-UDF). The CSST-UDF covers about 9 square degrees, with magnitude limits ~28.3, 28.2, 27.6, 26.7 AB mag for point sources with 5-sigma detection in the r (0.620 um), i (0.760 um), z (0.915 um), and y (0.965 um) bands, respectively. According to the design parameters and scanning pattern of the CSST, we generate mock data, merge images and mask the bright sources in the four bands. We obtain four angular power spectra from l=200 to 2,000,000 (from arcsecond to degree), and fit them with a multi-component model including intrahalo light (IHL) using the Markov chain Monte Carlo (MCMC) method. We find that the signal-to-noise ratio (SNR) of the IHL is larger than 8 over the range of angular scales that are useful for astrophysical studies (l~10,000-400,000). Comparing to previous works, the constraints on the model parameters are improved by factors of 3~4 in this study, which indicates that the CSST-UDF survey can be a powerful probe on the cosmic optical and near-IR backgrounds.
Cosmological observations indicate that our universe is flat and dark energy (DE) dominated at present. The luminosity distance plays an important role in the investigation of the evolution and structure of the universe. Nevertheless, the evaluation of the luminosity distance d_L is associated computationally heavy numerical quadratures in practice. In this Letter we find a series solution of the luminosity distance in a spatially flat LCDM cosmological model. And it is further shown that the series solution has a relative error of less than 0.36% for any relative parameter β(β= Omega_m / Omega_L) from zero to four, i.e. 0.2 < Omega_L < 1 and redshift z > 0.1 when the order of the series is n = 100.
Many schemes have been proposed to define a model-independent constraint on cosmological dynamics, such as the nonparametric dark energy equation of state ω(z) or the deceleration parameter q(z). These methods usually contain derivatives with respect to observational data with noise. However, there can be large uncertainties when one estimates values with numerical differentiation, especially when noise is significant. We introduce a global numerical differentiation method, first formulated by Reinsch, which is smoothed by cubic spline functions, and apply it to the estimation of the transition redshift zt with a simulated expansion rate E(z) based on observational Hubble parameter data. We also discuss some deficiencies and limitations of this method.
The multi-band photometry of the VOICE imaging data, overlapping with 4.9 deg 2 of the Chandra Deep Field South (CDFS) area, enables both shape measurement and photometric redshift estimation to be the two essential quantities for weak lensing analysis. The depth of mag AB is up to 26.1 (5 σ limiting) in r -band. We estimate the excess surface density (ESD; ΔΣ) based on galaxy–galaxy measurements around galaxies at lower redshift (0.10 < z l < 0.35) while we select the background sources as those at higher redshift ranging from 0.3 to 1.5. The foreground galaxies are divided into two major categories according to their colour (blue and red), each of which has been further divided into high- and low-stellar-mass bins. The halo masses of the samples are then estimated by modelling the signals, and the posterior of the parameters are sampled using a Monte Carlo Markov chain process. We compare our results with the existing stellar-to-halo mass relation (SHMR) and find that the blue low-stellar-mass bin (median M * = 10 8.31 M ⊙ ) deviates from the SHMR relation whereas the other three samples agree well with empirical curves. We interpret this discrepancy as the effect of the low star-formation efficiency of the low-mass blue dwarf galaxy population dominated in the VOICE-CDFS area.
We present a forecast study on the cross-correlation between cosmic shear tomography from the Chinese Survey Space Telescope (CSST) and CMB lensing from Ali CMB Polarization Telescope (AliCPT-1) in Tibet. The correlated galaxy and CMB lensing signals were generated from Gaussian realizations based on inputted auto- and cross-spectra. To account for the error budget, we considered the CMB lensing reconstruction noise based on the AliCPT-1 lensing reconstruction pipeline; shape noise of the galaxy lensing measurement; CSST photo-$z$ error; photo-$z$ bias; intrinsic alignment effect, and multiplicative bias. The AliCPT-1 CMB lensing mock data were generated according to two experimental stages, namely the ``4 modules*yr'' and ``48 modules*yr'' cases. We estimate the cross-spectra in 4 tomographic bins according to the CSST photo-$z$ distribution in the range of $z\in[0,4)$. After reconstructing the pseudo-cross-spectra from the realizations, we calculate the signal-to-noise ratio (SNR). By combining the 4 photo-$z$ bins, the total cross-correlation SNR$\approx15$ (AliCPT-1 ``4 modules*yr'') and SNR$\approx22$ (AliCPT-1 ``48 modules*yr''). Finally, we study the cosmological application of this cross-correlation signal. Excluding intrinsic alignment (IA) in the template fitting would lead to roughly a $0.6σ$ increment in $σ_8$ due to the negative IA contribution to the galaxy lensing data. For AliCPT-1 first and second stages, the cross-correlation of CSST cosmic shear with CMB lensing gives errors on the clustering amplitude $σ_{σ_8}=^{+0.043}_{-0.038}$ or $σ_{S_8}=\pm 0.031$ and $σ_{σ_8}=^{+0.030}_{-0.027}$ or $σ_{S_8}=\pm 0.018$, respectively.
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