Chinese Space Research Articles

Forecasting Constraint on the f(R) Theory with the CSST SN Ia and BAO Surveys

The f(R) modified gravity theory can explain the accelerating expansion of the late Universe without introducing dark energy. In this study, we predict the constraint strength on the f(R) theory using the mock data generated from the Chinese Space Station Telescope (CSST) Ultra-Deep Field Type Ia supernova (SN Ia) survey and wide-field slitless spectroscopic baryon acoustic oscillation (BAO) survey. We explore three popular f(R) models and introduce a parameter b to characterize the deviation of the f(R) theory from the ΛCDM theory. The Markov Chain Monte Carlo method is employed to constrain the parameters in the f(R) models, and the nuisance parameters and systematic uncertainties are also considered in the model fitting process. Besides, we also perform model comparisons between the f(R) models and the ΛCDM model. We find that the constraint accuracy using the CSST SN Ia+BAO data set alone is comparable to or even better than the result given by the combination of the current relevant observations, and the CSST SN Ia+BAO survey can distinguish the f(R) models from the ΛCDM model. This indicates that the CSST SN Ia and BAO surveys can effectively constrain and test the f(R) theory.

Proud to be not beautiful: navigating affects in the rebellious body project of Chinese TuoMeiYi feminism

ABSTRACT In recent years, TuoMeiYi (escaping beauty duty) has emerged in the Chinese digital space as a distinct feminist proposition. Advocating for women to break free from the constraints of beauty, TuoMeiYi feminists strive to redefine what a woman should look like, and actively practice their anti-beauty stance by shaving their hair into buzz cuts. This everyday feminist politics attempts to reclaim women’s autonomy over their bodies by transforming their feminist body into a rebellious project. Diverging from the prevalent media discursive approach in Chinese digital feminism studies, this article examines TuoMeiYi feminists’ explorative body project through the lens of affect grounded in corporeal and phenomenological feminist theories. Using digital ethnography to explore an online TuoMeiYi forum, this article recognizes that pride and shame have been discursively mobilized as pivotal tools to sustain the anti-beauty feminist ideal. Yet, the construction of the proud feminist subject also involves a highly self-autonomous logic that tries to eliminate feminist embodied feelings. In the end, this article argues that it is not ‘feeling good’ but the ‘strange feelings’ that demonstrate the feminist body is neither a docile nor consciously autonomous entity, but a rebellious affective being capable of feeling in unplanned alternative ways.

Photometric redshift estimation for CSST Survey with LSTM Neural Networks

Abstract Accurate estimation of photometric redshifts (photo-zs) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own applications, advantages, and limitations. In this study, we propose a new approach that utilizes a deep learning model based on Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to predict photo-z. Unlike many existing machine learning models, our method requires only flux measurements from different observed filters as input. The model can automatically learn the complex relationships between the flux data across different wavelengths, eliminating the need for manually extracted or derived input features, thereby providing precise photo-z estimates. The effectiveness of our proposed model is evaluated using simulated data from the Chinese Space Station Telescope (CSST) sourced from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and the COSMOS catalog, considering anticipated instrument effects of the future CSST. Results from experiments demonstrate that our LSTM model, compared to commonly used template fitting and machine learning approaches, requires minimal input parameters and achieves high precision in photo-z estimation. For instance, when trained on the same dataset and provided only with photometric fluxes as input features, the proposed LSTM model yields one-third of the outliers fout observed with a Multi-Layer Perceptron Neural Network (MLP) model, while the normalized median absolute deviation $\rm \sigma _{NMAD}$ is only two-thirds that of the MLP model. This study presents a novel approach to accurately estimate photo-zs of galaxies using photometric data from large-scale survey projects.

Compact Laser-Induced Fluorescence Detector with Adjustable Laser Focal Spot for Multiple Purposes.

In many research fields, the demand for miniaturized laser-induced fluorescence (LIF) detection systems has been increasing. This work has developed a compact LIF detector, employing a laser diode as the excitation source and a photodiode as the photodetector with an adjustable laser focal spot, to meet the diverse requirements of various observation targets, such as capillaries, PCR tubes, and microfluidic chips. It features the functionalities of background fluorescence correction, the adaptive adjustment of the dynamic range, and constant power control for the laser. The influence of the excitation power on the detection limit was studied through experiments, and the configuration results for LED/LD as light sources and 487/450 nm wavelengths were compared and optimized. A fully integrated, compact, modular epifluorescence LIF detector was subsequently constructed, measuring 40 × 22 × 38 mm3 in total size, with a cost of USD 320, and achieving a detection limit of 0.4 nM for fluorescein sodium. Finally, the detector was integrated into a nucleic acid detection system with a microfluidic chip on the Chinese Space Station (CSS) and was also tested with PCR tubes and capillaries, proving its broad practicality and adaptability to various analytical systems.

The evolution of space syntax over the past two decades: evidence from China

ABSTRACT China is one of the most researched countries in space syntax. However, the application of the theory in China remains relatively unfamiliar to the international academic community. This study employed keyword analysis to review research themes in Chinese space syntax and used empirical content analysis to trace the evolution of relevant concepts, including spatial scales, study areas, software, models, and measures. By comparing with international studies, this study characterizes the research themes. It emerged that Chinese scholars favor using measures like integration to evaluate spatial accessibility, with the exploration of spaces’ cultural attributes also being a significant theme. Additionally, this study reveals that applied research propels Chinese space syntax, demonstrating characteristics of staged growth and a stratification phenomenon in the usage frequency of relevant concepts. It also identifies cross-regional research strengths and challenges within the field of Chinese space syntax. Ultimately, insights for improving future research and possible research directions are presented. This study provides evidence to analyze the evolution of space syntax theory from China and aids in improving the research framework of the theory. It provides a valuable reference for scholars and can promote the global dissemination of space syntax.

The Velocity Aberration Effect of the CSST Main Survey Camera* *This research has been generously supported by the National Natural Science Foundation of China (NSFC, Grant Nos. 12073047 and 12273077) and the National Key Research and Development (Grant No. 2022YFF0711500).

In this study, we conducted simulations to find the geometric aberrations expected for images taken by the Main Survey Camera of the Chinese Space Station Telescope (CSST) due to its motion. As anticipated by previous work, our findings indicate that the geometric distortion of light impacts the focal plane's apparent scale, with a more pronounced influence as the size of the focal plane increases. Our models suggest that the effect consistently influences the pixel scale in both the vertical and parallel directions. The apparent scale variation follows a sinusoidal distribution throughout one orbital period. Simulations reveal that the effect is particularly pronounced in the center of the Galaxy and gradually diminishes along the direction of ecliptic latitude. At low ecliptic latitudes, the total aberration leads to about a 0.94 pixel offset (a 20 minute exposure) and a 0.26 pixel offset (a 300 s exposure) at the edge of the field of view. Appropriate processings for the geometric effect during the CSST pre- and post-observation phases are presented.

The Completeness of Accreting Neutron Star Binary Candidates from the Chinese Space Station Telescope

Abstract A neutron star (NS) has many extreme physical conditions, and one may obtain some important information about an NS via accreting neutron star binary (ANSB) systems. The upcoming Chinese Space Station Telescope (CSST) provides an opportunity to search for a large sample of ANSB candidates. Our goal is to check the completeness of the potential ANSB samples from CSST data. In this paper, we generate some ANSBs and normal binaries under the CSST photometric system by binary evolution and binary population synthesis method and use a machine learning method to train a classification model. Although the Precision (94.56%) of our machine learning model is as high as before study, the Recall is only about 63.29%. The Precision/Recall is mainly determined by the mass transfer rate between the NSs and their companions. In addition, we also find that the completeness of ANSB samples from CSST photometric data by the machine learning method also depends on the companion mass and the age of the system. ANSB candidates with a low initial mass companion star (0.1 M ⊙ to 1 M ⊙) have a relatively high Precision (94.94%) and high Recall (86.32%), whereas ANSB candidates with a higher initial mass companion star (1.1 M ⊙ to 3 M ⊙) have similar Precision (93.88%) and quite low Recall (42.67%). Our results indicate that although the machine learning method may obtain a relatively pure sample of ANSBs, a completeness correction is necessary for one to obtain a complete sample.

“A strong diewei” – A critical investigation of gendered neological metaphors on Weibo

Contributing to the growing literature on discursive strategies against male-dominated gender discourse in the Chinese digital space (e.g., Chen and Gong, 2023), this study investigates a popular neological metaphor – diewei (“爹味” − literally, a smell of father, figuratively, a sense of fatherhood) coined by Chinese microblogging users on Weibo. Similar to mansplaining in English, diewei was originally adopted to describe men’s patronizing, condescending, and overconfident speech style (Bridges, 2017). Drawing on the cultural reference to Chinese fatherhood, diewei represents irony against the authoritative role of the father privileged by patrilineal Chinese family ethics, employed to evaluate others’ overbearing speech styles, attitudes, and behaviors. From a dataset of 198 Weibo posts, we identified three strategic adoptions of diewei based on linguistic and communicative functions. These include (1) markers of masculine essence, (2) metapragmatic commentaries, and (3) personal labels. We then adopt critical discourse analysis (CDA) to explore how gender relationships are discursively produced, represented, and resisted in the diewei discourse through the above adoptions. Our findings suggest that diewei instantiates the pragmatic expansion of gendered metaphors at the expense of dominant masculinity, constituting feminist irony against the authoritative fatherhood in China’s digital space.

Improving Cosmological Constraints by Inferring the Formation Channel of Extreme-mass-ratio Inspirals

Extreme-mass-ratio inspirals (EMRIs) could be detected by space-borne gravitational-wave (GW) detectors, such as the Laser Interferometer Space Antenna (LISA), TianQin, and Taiji. Localizing EMRIs by GW detectors can help us select candidate host galaxies, which can be used to infer the cosmic expansion history. In this paper, we demonstrate that the localization information can also be used to infer the formation channel of EMRIs, and can hence allow us to extract more precisely the redshift probability distributions. By conducting mock observations of the EMRIs that can be detected by TianQin and LISA, as well as the galaxies that can be provided by the future Chinese Space Station Telescope, we find that TianQin can constrain the Hubble–Lemaître constant H 0 to a precision of ∼3%–8% and the dark energy equation-of-state parameter w 0 to ∼10%–40%. The TianQin+LISA network, by increasing the localization accuracy, can improve the precisions of H 0 and w 0 to ∼0.4%–7% and ∼4%–20%, respectively. Then, considering an illustrative case in which all EMRIs originate in active galactic nuclei (AGNs), and combining the mock EMRI observation with a mock AGN catalog, we show that TianQin can recognize the EMRI–AGN correlation with ∼1300 detections. The TianQin+LISA network can reduce this required number to ∼30. Additionally, we propose a statistical method to directly estimate the fraction of EMRIs produced in AGNs, f agn, and show that observationally deriving this value could significantly improve the constraints on the cosmological parameters. These results demonstrate the potentials of using EMRIs as well as galaxy and AGN surveys to improve the constraints on cosmological parameters and the formation channel of EMRIs.

Automatic Search for Low-surface-brightness Galaxies from Sloan Digital Sky Survey Images Using Deep Learning

Low-surface-brightness (LSB) galaxies play a crucial role in our understanding of galaxy evolution and dark matter cosmology. However, efficiently detecting them in large-scale surveys is challenging, due to their dim appearance. In this study, we propose a two-step detection method based on deep learning to address this issue. First, an object detection model called GalCenterNet was designed to detect LSB galaxy candidates in astronomical images. The model was trained using a data set of 665 Sloan Digital Sky Survey (SDSS) images, which contained 667 LSB galaxies. On the test set, the model achieved an accuracy of 95.05% and a recall of 96.00%. Next, an anomaly detection technique known as Deep Support Vector Data Description was applied to identify abnormal sources, thus refining the LSB candidates. By applying the two-step detection method to SDSS images, we have obtained a sample of 37,536 LSB galaxy candidates. This wide-area sample contains diverse and abundant LSB galaxies, which are valuable for studying the properties of LSB galaxies and the role that the environment plays in their evolution. The proposed detection method enables end-to-end detection from the SDSS images to the final detection results. This approach will be further employed to efficiently identify objects in the upcoming Chinese Survey Space Telescope sky survey.

Forecast of cosmological constraints with superluminous supernovae from the Chinese Space Station Telescope

Forecast of cosmological constraints with superluminous supernovae from the Chinese Space Station Telescope

Spatial informality, urban regularization, and social resistance: Tianqiao as a public space for the poor, 1911–1937

ABSTRACT This study examines Tianqiao, the largest commercial and entertainment centre, which underwent a significant transformation into the most popular public space in Republican Beijing. Spatial informality has often been overlooked in planning history, due to its deviation from traditional disciplinary norms, tools, and materials, yet it can offer valuable insights into Chinese urban public spaces and their social values. Drawing on a wide range of sources, including government documents, newspapers, social surveys, and personal records, this paper elucidates how everyday life and popular culture emerged as effective strategies for the poor population to exert their political power and passively resist the state's incomplete urban regularization efforts. This exemplifies a distinct type of Chinese public space that acts as a mediator between the state and society, particularly from a bottom-up perspective of urban modernization.

Static analysis and contact angle hysteresis study of bubbles in Chinese space station tank models under different gravity effects

In most space shuttle fuel tanks, a central column is used to secure the Propellant Management Devices. This study focuses on the distribution of fluids in such tanks. Microgravity experiments are conducted on the Chinese Space Station, and annular bubbles surrounding the central column have been observed for the first time. An in-depth study is carried out on the distribution and profile of these bubbles using perturbation methods and the Young–Laplace equation. Theoretical values for the gas–liquid interface morphology of annular bubbles under different gravity levels are obtained and compared with numerical simulation results, showing substantial agreement. The phenomenon of contact angle hysteresis of bubbles under gravity conditions was studied through simulation and theoretical analysis. Detailed analysis of the characteristics of contact angle hysteresis and corresponding drag resistance using the Wenzel model was carried out. Based on this, a numerical calculation program based on the shooting method was developed to obtain the morphology of the same bubble under different gravities. Furthermore, it was found that the theoretical maximum Bond number for circular bubbles suspended on the central column is 2, and it was observed that bubbles with equilibrium contact angles closer to 90° exhibit greater upward displacement of their centroids under varying gravity, providing theoretical support for bubble management in aerospace engineering.

Development of the Microbial Online Monitoring Module (MOMM) for the Chinese Space Station

Development of the Microbial Online Monitoring Module (MOMM) for the Chinese Space Station

Estimating the Atmospheric Parameters of Early-type Stars from the Chinese Space Station Telescope (CSST) Slitless Spectra Survey

The measurement of atmospheric parameters is fundamental for scientific research using stellar spectra. The Chinese Space Station Telescope (CSST), scheduled to be launched in 2024, will provide researchers with hundreds of millions of slitless spectra for stars during a 10 yr survey. And machine learning has unparalleled efficiency in processing large amounts of data compared to manual processing. Here we studied the stellar parameters of early-type stars (effective temperature T eff > 15,000 K) based on the design indicators of the CSST slitless spectrum and the machine learning algorithm, Stellar LAbel Machine. We used the Potsdam Wolf–Rayet (POWR) synthetic spectra library for cross validation. Then we tested the reliability of machine learning results by using the Next Generation Spectrum Library (NGSL) from Hubble Space Telescope observation data. In an ideal case for full-wavelength spectra without any noise, the average absolute value of the relative deviation is 2.7% (800 K) for T eff, and 3.5% (0.11 c.g.s) for surface gravity logg . We use the spectra with the impact of interstellar extinction (AV = 0, 0.5, 1, 1.5, 2 mag) and radial velocity (RV = −50, −30, 0, 30, 50 km s−1) from the POWR library as the test set. When RV = 0 km s−1 and AV = 0 mag, the average value and standard deviation for 3 wavelength ranges (2550–4050 Å (R = 287); 4050–6300 Å (R = 232); 6300–10000 Å (R = 207)) are –66 ± 3351 K, 550 ± 3536 K, and 356 ± 3616 K for T eff, and 0.004 ± 0.224 c.g.s, –0.024 ± 0.246 c.g.s, and 0.01 ± 0.212 c.g.s for logg . When using the observed data from NGSL as the testing samples, the deviation of T eff is less than 5% (1500 K), and the deviation of logg is less than 11% (0.33 c.g.s). In addition, we also test the influence of shifting of spectra on the parameters’ accuracy. The deviation of T eff for the case with a shift of 5 Å and 10 Å are 3.6% (1100 K) and 4.3% (1300 K), respectively; the deviation of logg are 4.2% (0.13 c.g.s) and 5.1% (0.15 c.g.s). These results demonstrate that we can obtain relatively accurate stellar parameters of a population of early-type stars with the CSST slitless spectra and a machine-learning method.

Study on Liquid Climbing Behavior During Filling Process in Tank Models Aboard the Chinese Space Station

Study on Liquid Climbing Behavior During Filling Process in Tank Models Aboard the Chinese Space Station

Imputation of missing photometric data and photometric redshift estimation for CSST

ABSTRACT Accurate photometric redshift (photo-z) estimation requires support from multiband observational data. However, in the actual process of astronomical observations and data processing, some sources may have missing observational data in certain bands for various reasons. This could greatly affect the accuracy and reliability of photo-z estimation for these sources, and even render some estimation methods unusable. The same situation may exist for the upcoming Chinese Space Station Telescope (CSST). In this study, we employ a deep learning method called generative adversarial imputation networks (GAIN) to impute the missing photometric data in CSST, aiming to reduce the impact of data missing on photo-z estimation and improve estimation accuracy. Our results demonstrate that using the GAIN technique can effectively fill in the missing photometric data in CSST. Particularly, when the data missing rate is below 30 per cent, the imputation of photometric data exhibits high accuracy, with higher accuracy in the g, r, i, z, and y bands compared to the NUV and u bands. After filling in the missing values, the quality of photo-z estimation obtained by the widely used easy and accurate Zphot from Yale (eazy) software is notably enhanced. Evaluation metrics for assessing the quality of photo-z estimation, including the catastrophic outlier fraction (fout), the normalized median absolute deviation ($\rm {\sigma _{NMAD}}$), and the bias of photometric redshift (bias), all show some degree of improvement. Our research will help maximize the utilization of observational data and provide a new method for handling sample missing values for applications that require complete photometry data to produce results.

A Framework for Rapidly Predicting the Dynamics of Flexible Solar Arrays in the China Space Station with a Verification Based on On-Orbit Measurement Data

The Chinese space station is a complex structure with large flexible appendages. Obtaining the on-orbit response characteristics of such a structure under different working conditions is a traditional and classic challenge in the field of dynamics. To address the on-orbit dynamics of the China Space Station, the basic equations for dynamic reduction, assembly and data recovery of linear and nonlinear substructures are derived based on the reduction and recovery theory, and a fast coupling analysis framework for flexible systems with nonlinear attachments is formed. This coupling analysis framework is adopted to quickly acquire the dynamic response of the China Space Station during in-orbit operation, thereby guiding the design. Taking SZ-15 radial docking to the Chinese Space Station as the object, the substructure of six nonlinear flexible arrays is reduced, the full flexible dynamic equation of the space station is assembled, and the response of each part of the flexible wing during the docking process is analyzed and recovered. By designing a reasonable and reliable flexible wing test scheme in-orbit, the acceleration at the root and top of the flexible wing during the docking of SZ-15 is obtained. The measured data in-orbit show that the acceleration analysis results of the typical parts of the flexible wing have a good agreement, which verifies the correctness of the fast coupling analysis framework of the flexible system. Hence, the dynamic coupling characteristics analysis of the main structure of the space station and the flexible wing based on this method can better guide the rationality of the design of the dynamic characteristics of the Chinese Space Station.

Analysis of the Psychological Characteristics of the Audience in Contemporary Chinese Theater Space

Since the beginning of 2023, the number of theatre performances in China has increased year by year, and a new round of " theatre defense war " is about to be opened. Whether the operators can keep up with the favorable policies, take advantage of the wind, grasp the new market competition situation, and make timely market response has become a key factor. This paper focuses on the current audience viewing psychology dimension for data sampling and analysis, in order to put forward constructive suggestions. The purpose of this project research is based on the current situation investigation and data analysis, so that the theater can better optimize management in the future. By analyzing the audience 's views and opinions on the theater, it helps the theater to make more realistic changes.

Survey of Teachers’ Perception of Future School Learning Space

The cultivation of students’ core competencies is a worldwide issue. Transforming the learning environment to promote innovative pedagogy has become a trend. As a critical role in students’ cultivation, teachers’ perception of learning space has the direct effect on learning outcomes. This study aims to investigate the teachers’ perception of the new learning space. A teacher’s perception of future school learning space scale is developed to collect the data, which includes 8 subscales and 35 items, 355 teachers participated in the study. Result of Descriptive statistics and Independent-samples t-tests indicated that: (a) Future School Learning Space Scale is reliable and effective; (b) teachers’ perception of Comfortability of learning space, Constructive Learning, Differentiation Learning are relative higher, Cloud Technology and Connectivity are both lower; (c) Teachers in different group, such as gender, teaching experience and type of school have significant difference in their perception of future learning space. Implications for Chinese future learning space design and construction have discussed.