In this paper, facing the high-contrast, high-resolution and high-sensitivity technical difficulties that need to be solvedfordirect detection of habitable exoplanets, we research on the high-precision simulation method of spatial distributedsynthetic aperture optical interference system, which is one of the main ways to achieve this detection goal, by ZEMAXsoftware. We have built the simulation model of optical system for Michelson-type synthetic aperture telescope. It isverified from the principle and simulation analysis in ZEMAX software that the simulation precision of the optical system can achieve a high-contrast better than 10-7, and the simulation results of the distribution and variationof theinterference fringes are correct. Finally, we analyze the influence of factors such as band width and optical systemerroron the key index of the fringe visibility. The research in this paper can provide the necessary foundationfor theparameter decomposition and optimization design of the future spatial distributed synthetic aperture optical interferencesystem in habitable exoplanets.
Two-point statistics can be used to probe various types of cosmic structures. We perform a cross-correlation measurement using $\sim 160,000$ red satellite galaxies in SDSS redMaPPer clusters and find evidence that subhalo correlations do persist well beyond their tidal radius, suggesting that many of the observed satellites fell into their current host less than a dynamical time ago, $t_{\rm{infall}} < t_{\rm{dyn}}$. Combined with estimated dynamical times $t_{\rm{dyn}} \sim 3 - 5$ Gyr and SED fitting results for the time at which satellites stopped forming stars, $t_{\rm{quench}} \sim 6$ Gyr, we infer that for a significant fraction of the satellites, star formation quenched before those satellites entered their current hosts. In addition to cross-correlation measurement, galaxy-galaxy lensing is another powerful two-point statistic in cosmology analysis. We introduce and perform a number of tests of systematics on the DES Science Verification shear catalogs and photometric redshifts. We estimate the covariance matrices for the DES Year 1 galaxy-galaxy lensing measurement using the Jackknife approach. We validate the estimation using a suite of log-normal mock surveys. After testing the pipeline of our weak lensing measurement, we perform comprehensive measurements of weak lensing and galaxy clustering around voids in DES Year 1 data. We get heretofore the highest signal-to-noise void lensing measurements for voids identified by two different void finding algorithms. Using data from the MICE simulation, we study the impact of photo-z scatter on watershed types of void finder. We show that the photo-z scatter has introduced a selection bias which results in a boosting of the negative lensing signal. We also combine our observables of void lensing and void-galaxy cross-correlation to test the linear bias of redMaGiC galaxies distributed around voids. We see no evidence of departure from linearity.
Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine learning algorithms and applied to cut-out-centered galaxies. However, according to the design and survey strategy of optical surveys by CSST, preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images. Moreover, given that multi-color images of strong lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong lensing systems in images with any number of channels. As evaluated using CSST mock data based on an Semi-Analytic Model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. 61 new strong lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.
Abstract Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong-lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine-learning algorithms and applied to cutout-centered galaxies. However, according to the design and survey strategy of optical surveys by the China Space Station Telescope (CSST), preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual transformer with a sliding window technique to search for strong-lensing systems within entire images. Moreover, given that multicolor images of strong-lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong-lensing systems in images with any number of channels. As evaluated using CSST mock data based on a semianalytic model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. A total of 61 new strong-lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.
Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the galaxy morphology and understand its evolution across time. So far, high-quality data have allowed detailed B-D decomposition to redshift below 0.5, with limited excursions over small volumes at higher redshifts. Next-generation large sky space surveys in optical, e.g. from the China Space Station Telescope (CSST), and near-infrared, e.g. from the space EUCLID mission, will produce a gigantic leap in these studies as they will provide deep, high-quality photometric images over more than 15000 deg2 of the sky, including billions of galaxies. Here, we extend the use of the Galaxy Light profile neural Network (GaLNet) to predict 2-S\'ersic model parameters, specifically from CSST data. We simulate point-spread function (PSF) convolved galaxies, with realistic B-D parameter distributions, on CSST mock observations to train the new GaLNet and predict the structural parameters (e.g. magnitude, effective radius, Sersic index, axis ratio, etc.) of both bulge and disk components. We find that the GaLNet can achieve very good accuracy for most of the B-D parameters down to an $r$-band magnitude of 23.5 and redshift $\sim$1. The best accuracy is obtained for magnitudes, implying accurate bulge-to-total (B/T) estimates. To further forecast the CSST performances, we also discuss the results of the 1-S\'ersic GaLNet and show that CSST half-depth data will allow us to derive accurate 1-component models up to $r\sim$24 and redshift z$\sim$1.7.
Hybrid electric buses become more and more popular in cities because of higher fuel efficiency and less emission pollution. The power split between internal-combustion engine and electric motor, known as energy management strategy (EMS), is an important issue in hybrid electric vehicles, which has a significant impact on the overall efficiency. In this paper, an energy management strategy for buses is proposed based on reinforcement learning, utilizing property that the bus runs on the same route again and again. With the self-learning EMS implemented, city buses can adapt to the driving condition automatically after some driving cycles. The benefits of the proposed strategy are shown by a simulation study using Advanced Vehicle Simulator (ADVISOR) in Matlab. The results suggest the proposed method achieves both better fuel economy and less emissions.
The Large Interferometer For Exoplanets (LIFE) is a proposed space mission that enables the spectral characterization of the thermal emission of exoplanets in the solar neighborhood. The mission is designed to search for global atmospheric biosignatures on dozens of temperate terrestrial exoplanets and it will naturally investigate the diversity of other worlds. Here, we review the status of the mission concept, discuss the key mission parameters, and outline the trade-offs related to the mission's architecture. In preparation for an upcoming concept study, we define a mission baseline based on a free-formation flying constellation of a double Bracewell nulling interferometer that consists of 4 collectors and a central beam-combiner spacecraft. The interferometric baselines are between 10–600m, and the estimated diameters of the collectors are at least 2m (but will depend on the total achievable instrument throughput). The spectral required wavelength range is 6–16μm (with a goal of 4–18.5μm), hence cryogenic temperatures are needed both for the collectors and the beam combiners. One of the key challenges is the required deep, stable, and broad-band nulling performance while maintaining a high system throughput for the planet signal. Among many ongoing or needed technology development activities, the demonstration of the measurement principle under cryogenic conditions is fundamentally important for LIFE.
When dark matter halos are accreted by massive host clusters, strong gravitational tidal forces begin stripping mass from the accreted subhalos. This stripping eventually removes all mass beyond a subhalo's tidal radius, but the unbound mass remains in the vicinity of the satellite for at least a dynamical time t_dyn. The N-body subhalo study of Chamberlain et al. verified this picture and pointed out a useful observational consequence: measurements of subhalo correlations beyond the tidal radius are sensitive to the infall time, t_infall, of the subhalo onto its host. We perform this cross-correlation measurement using ~ 160,000 red satellite galaxies in SDSS redMaPPer clusters and find evidence that subhalo correlations do persist well beyond the tidal radius, suggesting that many of the observed satellites fell into their current host less than a dynamical time ago, t_infall < t_dyn. Combined with estimated dynamical times t_dyn ~ 3-5 Gyr and SED fitting results for the time at which satellites stopped forming stars, t_quench ~ 6 Gyr, we infer that for a significant fraction of the satellites, star formation quenched before those satellites entered their current hosts. The result holds for red satellites over a large range of cluster-centric distances 0.1 - 0.6 Mpc/h. We discuss the implications of this result for models of galaxy formation.
Context . Weak gravitational lensing is one of the most important probes of the nature of dark matter and dark energy. In order to extract cosmological information from next-generation weak lensing surveys (e.g., Euclid , Roman , LSST, and CSST) as much as possible, accurate measurements of weak lensing shear are required. Aims . There are existing algorithms to measure the weak lensing shear on imaging data, which have been successfully applied in previous surveys. In the meantime, machine learning (ML) has been widely recognized in various astrophysics applications in modeling and observations. In this work, we present a fully deep-learning-based approach to measuring weak lensing shear accurately. Methods . Our approach comprises two modules. The first one contains a convolutional neural network (CNN) with two branches for taking galaxy images and point spread function (PSF) simultaneously, and the output of this module includes the galaxy’s magnitude, size, and shape. The second module includes a multiple-layer neural network (NN) to calibrate weak-lensing shear measurements. We name the program F ORKLENS and make it publicly available online. Results . Applying F ORKLENS to CSST-like mock images, we achieve consistent accuracy with traditional approaches (such as moment-based measurement and forward model fitting) on the sources with high signal-to-noise ratios (S /N > 20). For the sources with S/N < 10, F ORKLENS exhibits an ~36% higher Pearson coefficient on galaxy ellipticity measurements. Conclusions . After adopting galaxy weighting, the shear measurements with F ORKLENS deliver accuracy levels to 0.2%. The whole procedure of F ORKLENS is automated and costs about 0.7 milliseconds per galaxy, which is appropriate for adequately taking advantage of the sky coverage and depth of the upcoming weak lensing surveys.
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