Sky Area Research Articles

A multimodal celestial object classification network based on 2D spectrum and photometric image

ABSTRACT In astronomy, classifying celestial objects based on the spectral data observed by astronomical telescopes is a basic task. So far, most of the work of spectral classification is based on 1D spectral data. However, 2D spectral data, which is the predecessor of 1D spectral data, is rarely used for research. This paper proposes a multimodal celestial classification network (MAC-Net) based on 2D spectra and photometric images that introduces an attention mechanism. In this work, all 2D spectral data and photometric data were obtained from LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope) DR6 and SDSS (Sloan Digital Sky Survey), respectively. The model extracts the features of the blue arm, red arm, and photometric images through three input branches, merges the features at the feature level and sends them to its classifiers for classification. The 2D spectral data set used in this experiment includes 1223 galaxy spectra, 466 quasar spectra, and 1202 star spectra. The same number of photometric images constitute the photometric image data set. Experimental results show that MAC-Net can classify galaxies, quasars, and stars with a classification precision of 99.2 per cent, 100 per cent, and 97.6 per cent, respectively. And the accuracy reached 98.6 per cent, it means that the similarity between this result and the results obtained by the LAMOST template matching method is 98.6 per cent. The results exceed the performance of the 1D spectrum classification network. At the same time, it also proves the feasibility and effectiveness of directly using 2D spectra to classify celestial bodies by using MAC-Net.

Performance of LiDAR-SLAM-based PNT with initial poses based on NDT scan matching algorithm

To achieve higher automation level of vehicles defined by the Society of Automotive Engineers, safety is a key requirement affecting navigation accuracy. We apply Light Detection and Ranging (LiDAR) as a main auxiliary sensor and propose LiDAR-based Simultaneously Localization and Mapping (SLAM) approach for Positioning, Navigation, and Timing. Furthermore, point cloud registration is handled with 3D Normal Distribution Transform (NDT) method. The initial guess of the LiDAR pose for LiDAR-based SLAM comes from two sources: one is the differential Global Navigation Satellite System (GNSS) solution; the other is Inertial Navigation System (INS) and GNSS integrated solution, generated with Extended Kalman Filter and motion constraints added, including Zero Velocity Update and Non-Holonomic Constraint. The experiment compares two initial guesses for scan matching in terms navigation accuracy. To emphasize the importance of a multi-sensor scheme in contrast to the conventional navigation method using the stand-alone system, the tests are conducted in both open sky area and GNSS signal block area, the latter might cause Multipath and Non-Line-Of-Sight effects. To enhance the navigation accuracy, the Fault Detection and Exclusion (FDE) mechanism is applied to correct the navigation outcome. The results show that the application of NDT and FDE for INS/GNSS integrated system can not only reach where-in-lane level navigation accuracy (0.5 m), but also enable constructing the dynamic map.

Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey

A small fraction of giants possess photospheric lithium (Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giant sample is needed. In this study, we designed a modified convolutional neural network model called Coord-DenseNet to determine the A(Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) giant spectra. The precision is good on the test set: MAE = 0.15 dex, and σ = 0.21 dex. We used this model to predict the Li abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified 7768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting for about 1.02% of all giants. We compared the Li abundance estimated by our work with those derived from high-resolution spectra. We found that the consistency was good if the overall deviation of 0.27 dex between them was not considered. The analysis shows that the difference is mainly due to the high A(Li) from the medium-resolution spectra in the training set. This sample of Li-rich giants dramatically expands the existing sample size of Li-rich giants and provides us with more samples to further study the formation and evolution of Li-rich giants.

Searching for Barium Stars from the LAMOST Spectra Using the Machine-learning Method: I

Barium stars are chemically peculiar stars that exhibit enhancement of s-process elements. Chemical abundance analysis of barium stars can provide crucial clues for the study of the chemical evolution of the Galaxy. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has released more than 6 million low-resolution spectra of FGK-type stars by Data Release 9, which can significantly increase the sample size of barium stars. In this paper, we used machine-learning algorithms to search for barium stars from low-resolution spectra of LAMOST. We have applied the Light Gradient Boosting Machine (LGBM) algorithm to build classifiers of barium stars based on different features, and build predictors for determining [Ba/Fe] and [Sr/Fe] of barium candidates. The classification with features in the whole spectrum performs best: for the sample with strontium enhancement, Precision = 97.81% and Recall = 96.05%; for the sample with barium enhancement, Precision = 96.03% and Recall = 97.70%. In prediction, [Ba/Fe] estimated from Ba ii line at 4554 Å has smaller dispersion than that from Ba ii line at 4934 Å: MAE4554 Å = 0.07, σ 4554 Å = 0.12. [Sr/Fe] estimated from Sr ii line at 4077 Å performs better than that from Sr ii line at 4215 Å: MAE4077 Å = 0.09, σ 4077 Å = 0.16. A comparison of the LGBM and other popular algorithms shows that LGBM is accurate and efficient in classifying barium stars. This work demonstrated that machine learning can be used as an effective means to identify chemically peculiar stars and determine their elemental abundance.

The Pristine survey – XX. GTC follow-up observations of extremely metal-poor stars identified from Pristine and LAMOST

ABSTRACT Ultra-metal-poor stars ($\rm {[Fe/H]} \lt -4.0$) are very rare, and finding them is a challenging task. Both narrow-band photometry and low-resolution spectroscopy have been useful tools for identifying candidates, and in this work, we combine both approaches. We cross-matched metallicity-sensitive photometry from the Pristine survey with the low-resolution spectroscopic Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) data base, and re-analysed all LAMOST spectra with $\rm {[Fe/H]} _{\rm Pristine} \lt -2.5$. We find that ∼1/3rd of this sample (selected without $\rm {[Fe/H]} _{\rm Pristine}$ quality cuts) also have spectroscopic $\rm {[Fe/H]} \lt -2.5$. From this sample, containing many low signal-to-noise ratio (S/N) spectra, we selected 11 stars potentially having $\rm {[Fe/H]} \lt -4.0$ or $\rm {[Fe/H]} \lt -3.0$ with very high carbon abundances, and we performed higher S/N medium-resolution spectroscopic follow-up with the Optical System for Imaging and low Resolution Integrated Spectroscopy (OSIRIS) on the 10.4-m Gran Telescopio Canarias (GTC). We confirm their extremely low metallicities, with a mean of $\rm {[Fe/H]} = -3.4$, and the most metal-poor star having $\rm {[Fe/H]} = -3.8$. Three of these are clearly carbon-enhanced metal-poor (CEMP) stars with $+1.65 \lt \rm {[C/Fe]} \lt +2.45$. The two most carbon-rich stars are either among the most metal-poor CEMP-s stars or the most carbon-rich CEMP-no stars known, the third is likely a CEMP-no star. We derived orbital properties for the OSIRIS sample and find that only one of our targets can be confidently associated with known substructures/accretion events, and that three out of four inner halo stars have prograde orbits. Large spectroscopic surveys may contain many hidden extremely and ultra-metal-poor stars, and adding additional information from e.g. photometry as in this work can uncover them more efficiently and confidently.

Assessment of overhead environments on pedestrian thermal comfort in a dense urban district

Outdoor thermal comfort in urban environmental settings is often investigated at a small scale, e.g., in street canyons. Yet, pedestrian thermal comfort in a district is scarcely assessed, especially in terms of the comprehensive overhead environments such as sky openness, green coverage, and sun exposure. This study provides a systematic methodology to quantify the district-scale pedestrian thermal comfort performance considering overhead environments. Mobile transects were designed for data collection during peak hot hours within a medium-scale district. A portable weather station was used to measure outdoor thermal environments and a fisheye lens was to capture hemispherical images of overhead environments. A universal thermal comfort index, Physiological Equivalent Temperature (PET), was subsequently calculated with the collected thermal environment data. Meanwhile, hemispherical images were segmented into areas of visible sky, overhead greenery, and sun, which were further weighed into corresponding view factors. Eventually, a multiple regression model was developed between PET and view factors together with meteorological variables. The results showed that a decrease in the sky view factor of 0.17 or an increase in the green view factor of 0.21 could reduce PET by 0.5°C. The findings scientifically support resilient urban planning in greening and cooling urban dense spaces for comfortable and liveable environments.

Sky Area Target of Opportunity Mission Planning Method

针对中法合作SVOM卫星的天区范围内机遇目标规划问题（ToO-MM），对其中的约束条件和优化目标进行抽象，建立了规划问题数学模型，设计实现了基于启发式规则的机遇目标规划算法TMHPA。以最大化卫星科学观测收益和最大化应急任务响应度为优化目标，考虑卫星姿态调整时间的影响，对观测任务和数传任务进行规划。通过仿真实验验证算法的有效性，结果表明该方法能够在保证算法收敛性和时效性同时，给出卫星在天区范围内的网格单元（tile）目标观测序列以及执行数传任务的时段安排，实现对ToO目标观测的快速响应，并及时下传机遇目标科学观测数据，满足规划算法的设计需求。

The <i>NuSTAR </i>extragalactic surveys: Source catalogs from the Extended <i>Chandra</i> Deep Field-South and the <i>Chandra</i> Deep Field-North

We present a routinized and reliable method to obtain source catalogs from the Nuclear Spectroscopic Telescope Array (<i>NuSTAR</i>) extragalactic surveys of the Extended <i>Chandra</i> Deep Field-South (E-CDF-S) and <i>Chandra</i> Deep Field-North (CDF-N). The <i>NuSTAR</i> E-CDF-S survey covers a sky area of <inline-formula><tex-math id="M1">\begin{document}$\sim30'\times30'$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2023-0032_M1.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2023-0032_M1.png"/></alternatives></inline-formula> to a maximum depth of <inline-formula><tex-math id="Z-20230704083543">\begin{document}$\sim230\;{\rm{ks}}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2023-0032_Z-20230704083543.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2023-0032_Z-20230704083543.png"/></alternatives></inline-formula> corrected for vignetting in the 3–24 keV band, with a total of 58 sources detected in our E-CDF-S catalog; the <i>NuSTAR</i> CDF-N survey covers a sky area of <inline-formula><tex-math id="M3">\begin{document}$\sim7'\times10'$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2023-0032_M3.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2023-0032_M3.png"/></alternatives></inline-formula> to a maximum depth of <inline-formula><tex-math id="Z-20230704083612">\begin{document}$\sim440\;{\rm{ks}}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2023-0032_Z-20230704083612.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2023-0032_Z-20230704083612.png"/></alternatives></inline-formula> corrected for vignetting in the 3–24 keV band, with a total of 42 sources detected in our CDF-N catalog that is produced for the first time. We verify the reliability of our two catalogs by crossmatching them with the relevant catalogs from the <i>Chandra</i> X-ray observatory and find that the fluxes of our <i>NuSTAR</i> sources are generally consistent with those of their <i>Chandra</i> counterparts. Our two catalogs are produced following the exact same method and made publicly available, thereby providing a uniform platform that facilitates further studies involving these two fields. Our source-detection method provides a systematic approach for source cataloging in other <i>NuSTAR</i> extragalactic surveys.

The Rapid ASKAP Continuum Survey III: Spectra and Polarisation In Cutouts of Extragalactic Sources (SPICE-RACS) first data release

AbstractThe Australian SKA Pathfinder (ASKAP) radio telescope has carried out a survey of the entire Southern Sky at 887.5 MHz. The wide area, high angular resolution, and broad bandwidth provided by the low-band Rapid ASKAP Continuum Survey (RACS-low) allow the production of a next-generation rotation measure (RM) grid across the entire Southern Sky. Here we introduce this project as Spectral and Polarisation in Cutouts of Extragalactic sources from RACS (SPICE-RACS). In our first data release, we image 30 RACS-low fields in StokesI,Q,Uat 25$^{\prime\prime}$angular resolution, across 744–1032 MHz with 1 MHz spectral resolution. Using a bespoke, highly parallelised, software pipeline we are able to rapidly process wide-area spectro-polarimetric ASKAP observations. Notably, we use ‘postage stamp’ cutouts to assess the polarisation properties of 105912 radio components detected in total intensity. We find that our StokesQandUimages have anrmsnoise of$\sim$80$\unicode{x03BC}$Jy PSF$^{-1}$, and our correction for instrumental polarisation leakage allows us to characterise components with$\gtrsim$1% polarisation fraction over most of the field of view. We produce a broadband polarised radio component catalogue that contains 5818 RM measurements over an area of$\sim$1300 deg$^{2}$with an average error in RM of$1.6^{+1.1}_{-1.0}$rad m$^{-2}$, and an average linear polarisation fraction$3.4^{+3.0}_{-1.6}$%. We determine this subset of components using the conditions that the polarised signal-to-noise ratio is$>$8, the polarisation fraction is above our estimated polarised leakage, and the StokesIspectrum has a reliable model. Our catalogue provides an areal density of$4\pm2$RMs deg$^{-2}$; an increase of$\sim$4 times over the previous state-of-the-art (Taylor, Stil, Sunstrum 2009, ApJ, 702, 1230). Meaning that, having used just 3% of the RACS-low sky area, we have produced the 3rd largest RM catalogue to date. This catalogue has broad applications for studying astrophysical magnetic fields; notably revealing remarkable structure in the Galactic RM sky. We will explore this Galactic structure in a follow-up paper. We will also apply the techniques described here to produce an all-Southern-sky RM catalogue from RACS observations. Finally, we make our catalogue, spectra, images, and processing pipeline publicly available.

Extracting stellar emissivity via a machine learning analysis of MSX and LAMOST catalog data

Many astronomical studies model stars like radiating blackbodies with unit emissivity. Their conclusions should be reconsidered if the stellar spectral emissivity, ${ϵ}_{\ensuremath{\lambda}}$, were to be proven to be appreciably smaller. However, determining ${ϵ}_{\ensuremath{\lambda}}$ from raw observational data poses serious technical challenges. Here, using a machine learning technique, we implemented an inverse model for calculating the stellar spectral radiation flux in a given spectral band emissivity. Radiation flux data in some spectral bands serve as input to determine the unknown model parameters. To this purpose, we chose 411 stars (361 from the Midcourse Space Experiment (MSX) catalog and 50 from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) catalog) as training samples of a stochastic particle swarm optimization algorithm. The mean values of the emissivity estimates thus obtained deviate significantly from the ideal blackbody value. Knowledge of the model parameters then enabled us to calculate the radiation fluxes in other spectral bands to compare with the existing observational data and thus validate our approach. Finally, based on the trained algorithm, we discuss our predictions for spectral bands where astronomical data are unavailable. Besides providing direct evidence against modeling stars as emitting blackbodies, our conclusions also call for more direct investigations of the stellar emissivity.

Peering into the Milky Way by FAST: IV. Identification of two new Galactic supernova remnants G203.1+6.6 and G206.7+5.9

A 5$^{\circ}$ $\times$ 7$^{\circ}$ sky area containing two large radio structures of G203.1+6.6 and G206.7+5.9 with a size of about 2.5$^{\circ}$ and 3.5$^{\circ}$ respectively is scanned by using the L-band 19-beam receiver of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The FAST L-band receiver covers a frequency range of 1.0-1.5 GHz. Commissioning of the receiving system, including the measurements of the half-power beam width, gain, and main-beam efficiency is made by observing the calibrators. The multi-channel spectroscopy backend mounted to the receiver allows an in-band spectral-index determination. The brightness-temperature spectral indices of both objects are measured to be $\beta$ $\sim$ $-$2.6 to $-$2.7. Polarized emission is detected from the archival Effelsberg $\lambda$11 cm data for all the shell structures of G203.1+6.6 and G206.7+5.9. These results clearly indicate a non-thermal synchrotron emitting nature, confirming that G203.1+6.6 and G206.7+5.9 are large shell-type supernova remnants (SNRs). Based on morphological correlation between the radio continuum emission of G206.7+5.9 and the HI structures, the kinematic distance to this new SNR is estimated to be about 440 pc, placing it in the Local Arm.

Peering into the Milky Way by FAST: II. Ionized gas in the inner Galactic disk revealed by the piggyback line observations of the FAST GPPS survey

As one of the major components of the interstellar medium, the ionized gas in our Milky Way, especially the low-density diffuse component, has not been extensively observed in the radio band. The Galactic Plane Pulsar Snapshot (GPPS) survey covers the sky area within the Galactic latitude of $\pm10^\circ$ around the Galactic plane visible by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), and the spectral line data are simultaneously recorded during the pulsar survey observations. With an integration time of 5 minutes for each beam, the GPPS survey project provides the most sensitive piggyback spectra for tens of radio recombination lines (RRLs) in the band of 1000$-$1500 MHz for H$n\alpha$, He$n\alpha$, C$n\alpha$, as well as H$n\beta$ and H$n\gamma$. We processed the spectral data of RRLs, and obtained a sensitive averaged H$n\alpha$ RRL map of a sky area of 88 square degrees in the inner Galaxy of 33$^\circ$ $\leqslant l \leqslant$ 55$^\circ$ and $|b| \leqslant$ 2.0$^\circ$. The final spectral data of the H$n\alpha$ RRLs have a spatial resolution of $\sim$3$^\prime$, a spectral resolution of 2.2 km s$^{-1}$, and a typical spectral rms noise of 0.25 mJy beam$^{-1}$ or 6.3 mK in main-beam brightness temperature. The new H$n\alpha$ RRL map shows complex structural features dominated by a number of HII regions and large extended diffuse ionized gas regions. We detect about 94% of the known HII regions and confirm 43 WISE HII regions in the observed sky area. Several large HII regions or star-forming complexes in the distant outer Galaxy are resolved in the map of H$n\alpha$ RRLs. Extended RRL features of the diffuse ionized gas are detected. The RRL data products of the GPPS survey will be published and updated at http://zmtt.bao.ac.cn/MilkyWayFAST/

MIGHTEE: deep 1.4 GHz source counts and the sky temperature contribution of star-forming galaxies and active galactic nuclei

Abstract We present deep 1.4 GHz source counts from ∼5 deg2 of the continuum Early Science data release of the MeerKAT International Gigahertz Tiered Extragalactic Exploration survey down to S1.4GHz ∼15 $\mu$Jy. Using observations over two extragalactic fields (COSMOS and XMM-LSS), we provide a comprehensive investigation into correcting the incompleteness of the raw source counts within the survey to understand the true underlying source count population. We use a variety of simulations that account for: errors in source detection and characterization, clustering, and variations in the assumed source model used to simulate sources within the field and characterize source count incompleteness. We present these deep source count distributions and use them to investigate the contribution of extragalactic sources to the sky background temperature at 1.4 GHz using a relatively large sky area. We then use the wealth of ancillary data covering a subset of the COSMOS field to investigate the specific contributions from both active galactic nuclei (AGN) and star-forming galaxies (SFGs) to the source counts and sky background temperature. We find, similar to previous deep studies, that we are unable to reconcile the sky temperature observed by the ARCADE 2 experiment. We show that AGN provide the majority contribution to the sky temperature contribution from radio sources, but the relative contribution of SFGs rises sharply below 1 mJy, reaching an approximate 15–25 per cent contribution to the total sky background temperature (Tb ∼100 mK) at ∼15 $\mu$Jy.

Traffic image haze removal based on optimized retinex model and dark channel prior

GPS monitoring systems and the development of driverless vehicles are almost inseparable from camera images. The images taken by traffic cameras often contain certain sky areas and noise, the traditional dark channel prior (DCP) algorithm easily produces color distortion and halo effect, when processing the hazy traffic images with sky and high brightness areas. An optimized Retinex model and dark channel prior algorithm (ORDCP) is proposed in this paper. Firstly by adjusting the calculation method of dark channel image, the proportion of dark channel is improved; Then, the transmittance image is corrected and smoothed by guided filtering and mean filtering. Finally, the Retinex model is fused to save the details.ORDCP corrects the inaccurate calculation of scene transmittance value in DCP algorithm,and modifies some dehazing problems, such as the loss of details, halo effect, contrast and color distortion,etc. Using information entropy (IE) as the objective evaluation index, combined with the subjective evaluation, it is concluded that the algorithm proposed in this paper can effectively retain the detailed information of the image, and eliminate the halo effect. Meanwhile, it meets the visual characteristics of human eyes better, and has some practicality and applicability in traffic control and intelligent detection.

Data-driven Seeing Prediction for Optics Telescope: from Statistical Modeling, Machine Learning to Deep Learning Techniques

Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future, and facilitate flexible scheduling of observation tasks to maximize the use of astronomical observatories. Traditional approaches to seeing prediction mostly rely on regional weather models to capture the in-dome optical turbulence patterns. Thanks to the developing of data gathering and aggregation facilities of astronomical observatories in recent years, data-driven approaches are becoming increasingly feasible and attractive to predict astronomical seeing. This paper systematically investigates data-driven approaches to seeing prediction by leveraging various big data techniques, from traditional statistical modeling, machine learning to new emerging deep learning methods, on the monitoring data of the Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST). The raw monitoring data are preprocessed to allow for big data modeling. Then we formulate the seeing prediction task under each type of modeling framework and develop seeing prediction models through using representative big data techniques, including ARIMA and Prophet for statistical modeling, MLP and XGBoost for machine learning, and LSTM, GRU and Transformer for deep learning. We perform empirical studies on the developed models with a variety of feature configurations, yielding notable insights into the applicability of big data techniques to the seeing prediction task.

Spectral classification of LAMOST emission line galaxies based on machine learning methods

Spectral classification of emission-line galaxies(ELGs) plays an important role to understand the formation and evolution of different galaxies. Machine learning can obtain the ability of spectrum recognition by learning the features of a large number of spectra so as to automatically classify unlabeled spectra. We apply several machine learning methods: multi-layer perception (MLP), support vector machine (SVM), K-nearest neighbor(KNN) and random forest(RF) for classifying 49,000 emission-line galaxies observed by LAMOST(Large Sky Area Multi-Object Fiber Spectroscopic Telescope). In classification process, we directly employ the spectral flux pixels around some frequently used emission lines(Hβ, [OIII]λλ4959,5007, [OI], Hα, [NII]λλ6548,6584 and [SII]λλ6717,6731) as feature space. By comparing four algorithms, MLP classifier has the highest accuracy of 92.31%. In addition, we confirm the robustness of our MLP classifier. Finally, we provide our MLP classifier for classifying the emission-line galaxies in LAMOST new observations.

The X-ray angular power spectrum of extended sources in the eROSITA Final Equatorial Depth survey

ABSTRACT The eROSITA Final Equatorial Depth Survey (eFEDS), with a sky area of 140 square degrees with depth equivalent to the equatorial patch of the final eROSITA all-sky survey, represents the largest continuous non-full-sky X-ray fields to-date, making it the premier data set for measuring the angular power spectrum. In this work, we measure the X-ray angular power spectrum of galaxy clusters and groups in the eFEDS field. We show that the measured power spectrum is consistent with past observations, including the ROSAT All Sky Survey, and the Chandra COSMOS and Bootes fields. The predictions of cluster gas halo model that is calibrated from Chandra observations is also consistent with the eFEDS power spectrum. While the eFEDS does not have large enough sky coverage to provide meaningful cosmological constraints, we predict that the X-ray power spectrum from the cycle 4 of the eROSITA all-sky survey (eRASS4) will provide constraints on ΩM and σ8 at the $10{{\ \rm per\ cent}}$ level.

Yunnan–Hong Kong wide field photometric survey

AbstractWe are running a wide field photometric survey under the collaboration between Yunnan Observatories and Hong Kong Astronomical Society with a Centurion 18‐inch telescope at the Lijiang station of Yunnan Observatories, China. Its scientific goals are discovering new transiting exoplanetary systems and other types of variable stars, and studying asteroids of the solar system. The survey was designed to monitor several fixed sky areas through time series photometry with short cadences. Since 2016, numerous observing data have been accumulated and reduced by means of dedicated pipelines to deliver the light curves for tens of thousands objects, which are cross‐matched through the Carlsberg Meridian Catalog 15 or USNO‐B1.0 catalog. Systematic errors and trends that existed in the raw photometric results are simulated through selected reference stars and then removed to improve signal‐to‐noise ratios of the light curves, which is crucial to detect weak signals like exoplanet transits and other variable stars of low amplitudes in brightness variations. The final data products own the photometric precision better than 0.01 magnitude for stars brighter than 13.8 magnitude in the V band. By means of generalized Lomb–Scargle, phase dispersion minimization, and box‐fitting least squares techniques, we have already identified about a thousand of variable stars from the survey, including transiting exoplanet candidates, spotted stars, eclipsing binaries, pulsating stars, and so on. In addition, we have also captured around a thousand of asteroids of the solar system in the survey fields and produced their light curves due to spins.

Euclid: Cosmological forecasts from the void size function

The Euclid mission – with its spectroscopic galaxy survey covering a sky area over 15 000 deg2 in the redshift range 0.9 < z < 1.8 – will provide a sample of tens of thousands of cosmic voids. This paper thoroughly explores for the first time the constraining power of the void size function on the properties of dark energy (DE) from a survey mock catalogue, the official Euclid Flagship simulation. We identified voids in the Flagship light-cone, which closely matches the features of the upcoming Euclid spectroscopic data set. We modelled the void size function considering a state-of-the art methodology: we relied on the volume-conserving (Vdn) model, a modification of the popular Sheth & van de Weygaert model for void number counts, extended by means of a linear function of the large-scale galaxy bias. We found an excellent agreement between model predictions and measured mock void number counts. We computed updated forecasts for the Euclid mission on DE from the void size function and provided reliable void number estimates to serve as a basis for further forecasts of cosmological applications using voids. We analysed two different cosmological models for DE: the first described by a constant DE equation of state parameter, w, and the second by a dynamic equation of state with coefficients w0 and wa. We forecast 1σ errors on w lower than 10% and we estimated an expected figure of merit (FoM) for the dynamical DE scenario FoMw0, wa = 17 when considering only the neutrino mass as additional free parameter of the model. The analysis is based on conservative assumptions to ensure full robustness, and is a pathfinder for future enhancements of the technique. Our results showcase the impressive constraining power of the void size function from the Euclid spectroscopic sample, both as a stand-alone probe, and to be combined with other Euclid cosmological probes.

The statistical properties of early-type stars from LAMOST DR8

Context. Massive binary stars play a crucial role in many astrophysical fields. Investigating the statistical properties of massive binary stars is essential to trace the formation of massive stars and constrain the evolution of stellar populations. However, no consensus has been achieved on the statistical properties of massive binary stars, mainly due to the lack of a large and homogeneous sample of spectroscopic observations. Aims. We study the intrinsic binary fraction fbin and distributions of mass ratio f(q) and orbital period f(P) of early-type stars (comprised of O-, B-, and A-type stars) and investigate their dependences on effective temperature Teff, stellar metallicity [M/H], and the projection velocity vsini, based on the homogeneous spectroscopic sample from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release Eight (DR8). Methods. We collected 886 early-type stars, each with more than six observations from the LAMOST DR8, and divided the sample into subgroups based on their derived effective temperature (Teff), metallicity ([M/H]), and projected rotational velocity (vsini). Radial velocity measurements were archived from a prior study. A set of Monte Carlo simulations, following distributions of f(P)∝Pπ and f(q)∝qγ were applied to the observed binary fraction to correct for any observational biases. The uncertainties of the derived results induced by the sample size and observation frequency are examined systematically. Results. We found that fbin increases with increasing Teff. For stars in groups of B8-A, B4-B7, O-B3, the binary fractions are fbin = 48% ± 10%, 60%±10%, and 76%±10%, respectively. The binary fraction is positively correlated with metallicity for spectra in the sample, with derived values of fbin = 44% ± 10%, 60%±10%, and 72%±10% for spectra with metallicity ranges of [M/H] < −0.55, −0.55 ≤ [M/H] < −0.1, to [M/H] ≥ −0.1. Over all the vsini values we considered, the fbin have constant values of ∼50%. It seems that the binary population is relatively evenly distributed over a wide range of vsini values, while the whole sample shows that most of the stars are concentrated at low values of vsini (probably from strong wind and magnetic braking of single massive stars) and at high values of vsini (likely from the merging of binary stars). Stellar evolution and binary interaction may be partly responsible for this. In the case of samples with more than six observations, we derived π = −0.9 ± 0.35, −0.9 ± 0.35, and −0.9 ± 0.35, and γ = −1.9 ± 0.9, −1.1 ± 0.9, and −2 ± 0.9 for stars of types O-B3, B4-B7, and B8-A, respectively. There are no correlations found between π(γ) and Teff, nor for π(γ) and [M/H]. The uncertainties of the distribution decrease toward a larger sample size with higher observational cadence.