Real Observational Data Research Articles

Multisegment Overlap–Save Method for Coherent Dedispersion

Dispersion occurs due to the interstellar medium, which functions as a prism and causes different time delays in radio waves of varying frequencies. The coherent dedispersion technique is often used in pulsar and fast radio burst observations to mitigate this phenomenon. The widely recognized Overlap–Save approach enables this dedispersion algorithm to efficiently perform long linear convolution. However, with the present implementation, the necessary filter length for dedispersion can reach 100 million points or more. The corresponding fast Fourier transform (FFT) points should be larger than this value and a GPU cluster can be used to tackle this demanding process. This study presents the Multisegment Overlap–Save Method (MS-OSM) to effectively address this problem. Our algorithm divides signal bands into separate short segments based on frequency component delays during dedispersion. By using the short segments shuffling technique with the Overlap-Save structure, MS-OSM can greatly reduce the FFT and inverse FFT points required down to fewer than 65,536 points. To evaluate the performance of MS-OSM, a synthetic pulsar signal is created and verified using standard software tools like DSPSR and PRESTO. The results show that MS-OSM maintains the same resolution while reducing execution time and resource usage. The validation of MS-OSM is taken by processing real pulsar observation data.

Molecular spectral line data preprocessing container load grouping prediction algorithm based on EMD-LSTM

Unbalanced allocation of container resources in cluster environments is an urgent problem to be solved. Aiming at container load prediction and resource allocation strategy, this paper designs an astronomical data processing container load grouping prediction algorithm based on empirical mode decomposition-long short-term memory network, and proposes an adaptive recommendation value generation algorithm based on predicted load information, which can automatically allocate container computing resources according to the degree of load fluctuation. The load prediction accuracy is verified using simulated data and real astronomical observation data. The experimental results show that the algorithm proposed in this paper has higher prediction accuracy than the triple index method and the single long short-term memory network model. In the real-time preprocessing test of astronomical data, the recommendation value generation algorithm proposed in this paper can effectively improve the utilization efficiency of computing resources compared with the default strategy.

Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links

Research on augmentation and supplement systems for navigation systems has become a significant aspect in comprehensive positioning, navigation and timing (PNT) studies. The BeiDou-3 navigation satellite system (BDS-3) has constructed a dynamic inter-satellite network to gain more observation data than ground monitoring stations. Low Earth orbit (LEO) satellites have advantages in their kinematic velocity and information carrying rate and can be used as satellite-based monitoring stations for navigation satellites to make up for the distribution limitation of ground monitoring stations. This study constructs multi-source observation links with satellite-to-ground, inter-satellite and satellite-based observation data, proposes an orbit synchronization method for navigation satellites and LEO satellites and verifies the influence thereof on orbit accuracy with different observation data. The experimental results under conditions of real and simulated observation data showed the following: (1) With the support of satellite-based observation links, the orbit accuracy of the BDS-3 MEO satellites could be improved significantly, with a 78% improvement with the simulation data and a 76% improvement with the real data. When the navigation satellites leave the monitoring area of the ground monitoring stations, the accuracy reduction tendency of the orbit prediction could also be slowed down with the support of the LEO satellites and the accuracy could be maintained within centimeters. (2) Comparing the orbit accuracy with the support of the satellite-to-ground observation links, the orbit accuracy of the MEO satellites could be improved by 65.5%, 73.7% and 79.4% with the support of the 6, 12 and 60 LEO satellites, respectively. When the observation geometry and the covering multiplicity meet the basic requirement of orbit determination, the improvements to the orbit accuracy decrease with the growth of LEO satellite numbers. (3) The accuracy of orbit determination with the support of the LEO satellites or the inter-satellite links was at the centimeter level for both, verifying that inter-satellite links and satellite-based links can be used as each other’s backups for navigation satellites. (4) The accuracy of orbit determination with the multi-source observation links was also at the centimeter level, which was not better than the results with the support of the satellite-to-ground and inter-satellite links or the satellite-to-ground and satellite-based links.

Experimental and numerical study on data-driven prediction for wildfire spread incorporating adaptive observation error adjustment

In recent wildfire prediction research, data assimilation (DA) methods like Ensemble Kalman filtering have gained traction for integrating observation data to enhance prediction accuracy. Most previous studies trusted that the observation data were accurate, and set a small observation error, which causes unreliable predicted results for scenarios with large observation error. To tackle this, our study introduced a method that iteratively adjusted the potential range of observation errors by comparing observation and simulation data over time. We conducted a 30-m experiment and kilometer-scale numerical simulations. Unlike prior research, we adopted larger error ranges (the similarity index with true data ranges from 0.6 to 1) for both real and synthetic observation data. In the experiment, to increase the complexity of fire spread, a heterogeneous fuel arrangement was employed. Irregular flame fronts appeared due to incomplete combustion and were difficult to replicate in simulations. Better accuracy was achieved using real observation data to revise predictions. Furthermore, to improve the applicability of the algorithm, numerical simulations were designed to consider observation error changing over time or not. The Root Mean Square Errors for the fire front prediction using the proposed method remained lower than that of the traditional DA approach.

HaloFlow. I. Neural Inference of Halo Mass from Galaxy Photometry and Morphology

We present HaloFlow, a new machine-learning approach for inferring the mass of host dark matter halos, M h , from the photometry and morphology of galaxies (https://github.com/changhoonhahn/haloflow/). HaloFlow uses simulation-based inference with normalizing flows to conduct rigorous Bayesian inference. It is trained on state-of-the-art synthetic galaxy images from Bottrell et al. that are constructed from the IllustrisTNG hydrodynamic simulation and include realistic effects of the Hyper Suprime-Cam Subaru Strategy Program observations. We design HaloFlow to infer M h and stellar mass, M *, using grizy band magnitudes, morphological properties quantifying characteristic size, concentration and asymmetry, total measured satellite luminosity, and number of satellites. We demonstrate that HaloFlow infers accurate and unbiased posteriors of M h . Furthermore, we quantify the full information content in the photometric observations of galaxies in constraining M h . With magnitudes alone, we infer M h with σlogMh∼0.115 and 0.182 dex for field and group galaxies. Including morphological properties significantly improves the precision of M h constraints, as does total satellite luminosity: σlogMh∼0.095 and 0.132 dex. Compared to the standard approach using the stellar-to-halo mass relation, we improve M h constraints by ∼40%. In subsequent papers, we will validate and calibrate HaloFlow with galaxy–galaxy lensing measurements on real observational data.

An Indian real-world experience of intravenous fosnetupitant-palonosetron (IV NEPA) in preventing delayed and extended delayed CINV.

12070 Background: Although the reports suggest 15-25% cancer patients receiving highly- and moderately emetogenic chemotherapy (HEC and MEC) experience nausea and vomiting beyond day 5, majority of clinical trials focus on delayed chemotherapy induced nausea and vomiting (CINV) only during the first 5 days of treatment. Here we present a real world observational data with fixed combination of IntraVenous (Fos)NEtupitant and Palonosetron (IV NEPA) in preventing nausea/vomiting in delayed and extended delayed phase among patients receiving HEC/MEC. Methods: An open label, single arm, multicentre, phase IV prospective trial was conducted at six centres following individual Institutional Ethics Committee approval (CTRI/2023/04/051951). Patients in acute (0-24hrs), delayed (>24-120hrs), extended delayed (>120-240hrs) phases and extended overall phase (0-240hrs) were assessed for complete response (CR) (no vomiting and no need of rescue medication), complete protection (CP) [CR + no significant nausea (5mm to <25mm) on visual analogue scale], complete control (CC) [CR + no nausea (<5mm)] and safety. Results: Among 178 patients (Males-114, Females-64), 90 (50.56%) received HEC and 88 (49.44%) received MEC regimen. Cisplatin-Paclitaxel (19.10%) and Carboplatin-Paclitaxel (35.39%) were the most common HEC and MEC regimens respectively. Overall, CR in delayed and extended delayed phases were 93.26% and 96.07% respectively. Furthermore, CP and CC in delayed and extended delayed phases were 92.13% and 96.07% and 81.46% and 90.45% respectively (Table). In general, IV NEPA was well tolerated, as only 9.55% of patients experienced adverse events with 95.83% of them being mild in nature. Headache (2.25%) and injection site reactions (1.68%) were the most common adverse effect reported. Conclusions: IV NEPA demonstrated high efficacy and good tolerability in a real world Indian setting, exhibiting response rates of >92% in both delayed and extended delayed phases in patients receiving HEC/MEC regimens. Clinical trial information: CTRI/2023/04/051951. [Table: see text]

Power System State Estimation Based on Fusion of PMU and SCADA Data

This paper introduces a novel hybrid filtering algorithm that leverages the advantages of Phasor Measurement Units (PMU) to address state estimation challenges in power systems. The primary objective is to integrate the benefits of PMU measurements into the design of traditional power system dynamic estimators. It is noteworthy that PMUs and Supervisory Control and Data Acquisition (SCADA) systems typically operate at different sampling rates in power system estimation, necessitating synchronization during the filtering process. To address this issue, the paper employs a predictive interpolation method for SCADA measurements within the framework of the Extended Kalman Filter (EKF) algorithm. This approach achieves more accurate estimates, closer to real observation data, by averaging the KL distribution. The algorithm is particularly well-suited for state estimation tasks in power systems that combine traditional and PMU measurements. Extensive simulations were conducted on the IEEE-14 and IEEE-30 test systems, and the results demonstrate that the fused estimator outperforms individual estimators in terms of estimation accuracy.

Simplified models of wind-wave relationships in China's shallow-water coasts based on SWAN+ADCIRC simulations

The study of the shallow-water wind-wave relationship holds considerable importance in the realm of coastal engineering construction. However, existing empirical formulas for China's coastal areas face predictive accuracy challenges due to the discontinuity and insufficient temporal and spatial resolution of conventional observations, such as buoy and satellite data. Therefore, this study emphasizes two representative shallow-water areas along China's coast, the Pearl River Estuary and Bohai Bay. Based on the advanced SWAN + ADCIRC wave-current coupling model, numerical models for calculating wave evolution are established, calibrated and validated by real observation data. From these numerical foundations, a new model capturing shallow-water wind-wave relationship is proposed, demonstrating substantive applicability specifically tailored to the estuary and bay regions. The accuracy and applicability of the model are validated by comparing with real observation data. In addition, probabilistic models are introduced in this study accounting for the randomness of the significant wave height. These enhancements allow for refined quantification of significant wave height occurrence probabilities relative to specified wind speeds, offering informed predictive parameters, including upper and lower limits.

A Data-driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes

In recent years, there has been a gradual increase in the performance of complementary metal oxide semiconductor (CMOS) cameras. These cameras have gained popularity as a viable alternative to charge-coupled device cameras in a wide range of applications. One particular application is the CMOS camera installed in small space telescopes. However, the limited power and spatial resources available on satellites present challenges in maintaining ideal observation conditions, including temperature and radiation environment. Consequently, images captured by CMOS cameras are susceptible to issues such as dark current noise and defective pixels. In this paper, we introduce a data-driven framework for mitigating dark current noise and bad pixels for CMOS cameras. Our approach involves two key steps: pixel clustering and function fitting. During the pixel clustering step, we identify and group pixels exhibiting similar dark current noise properties. Subsequently, in the function fitting step, we formulate functions that capture the relationship between dark current and temperature, as dictated by the Arrhenius law. Our framework leverages ground-based test data to establish distinct temperature–dark current relations for pixels within different clusters. The cluster results could then be utilized to estimate the dark current noise level and detect bad pixels from real observational data. To assess the effectiveness of our approach, we have conducted tests using real observation data obtained from the Yangwang-1 satellite, equipped with a near-ultraviolet telescope and an optical telescope. The results show a considerable improvement in the detection efficiency of space-based telescopes.

Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care.

Acute Kidney Injury (AKI) is a common and severe complication in patients diagnosed with sepsis. It is associated with higher mortality rates, prolonged hospital stays, increased utilization of medical resources, and financial burden on patients' families. This study aimed to establish and validate predictive models using machine learning algorithms to accurately predict the occurrence of AKI in patients diagnosed with sepsis. This retrospective study utilized real observational data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. It included patients aged 18 to 90 years diagnosed with sepsis who were admitted to the ICU for the first time and had hospital stays exceeding 48 hours. Predictive models, employing various machine learning algorithms including Light Gradient Boosting Machine (LightGBM), EXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression (LR), were developed. The dataset was randomly divided into training and test sets at a ratio of 4:1. A total of 10,575 sepsis patients were included in the analysis, of whom 8,575 (81.1%) developed AKI during hospitalization. A selection of 47 variables was utilized for model construction. The models derived from LightGBM, XGBoost, RF, DT, ANN, SVM, and LR achieved AUCs of 0.801, 0.773, 0.772, 0.737, 0.720, 0.765, and 0.776, respectively. Among these models, LightGBM demonstrated the most superior predictive performance. These machine learning models offer valuable predictive capabilities for identifying AKI in patients diagnosed with sepsis. The LightGBM model, with its superior predictive capability, could aid clinicians in early identification of high-risk patients.

Accuracy of ALMA estimates of young disk radii and masses

Context. Protoplanetary disks, which are the natural consequence of the gravitational collapse of the dense molecular cloud cores, host the formation of the known planetary systems in our universe. Substantial efforts have been dedicated to investigating the properties of these disks in the more mature Class II stage, either via numerical simulations of disk evolution from a limited range of initial conditions or observations of their dust continuum and line emission from specific molecular tracers. The results coming from these two standpoints have been used to draw comparisons. However, few studies have investigated the main limitations at work when measuring the embedded Class 0/I disk properties from observations, especially in a statistical fashion. Aims. In this study, we provide a first attempt to compare the accuracy of some critical disk parameters in Class 0/I systems, as derived on real ALMA observational data, with the corresponding physical parameters that can be directly defined by theoreticians and modellers in numerical simulations. The approach we follow here is to provide full post-processing of the numerical simulations and apply it to the synthetic observations the same techniques used by observers to derive the physical parameters. Methods. We performed 3D Monte Carlo radiative transfer and mock interferometric observations of the disk populations formed in a magnetohydrodynamic (MHD) simulation model of disk formation through the collapse of massive clumps with the tools RADMC-3D and CASA, respectively, to obtain their synthetic observations. With these observations, we re-employed the techniques commonly used in disk modelling from their continuum emissions to infer the properties that would most likely be obtained with real interferometers. We then demonstrated how these properties may vary with respect to the gas kinematics analyses and dust continuum modelling. Results. Our modelling procedure, based on a two-component model for the disk and the envelope, shows that the disk sizes can be properly recovered from observations with sufficient angular resolutions, with an uncertainty of a factor ≈1.6–2.2, whereas their masses cannot be accurately measured. Overall, the masses are predominantly underestimated for larger, more massive disks by a median factor of ≈2.5, and even up to 10 in extreme cases, with the conversion from flux to dust mass under the optically thin assumption. We also find that the single Gaussian fittings are not a reliable modelling technique for young, embedded disks characterised by a strong presence of the envelopes. Thus, such an approach is to be used with caution. Conclusions. The radiative transfer post-processing and synthetic observations of MHD simulations offer genuine help in linking important observable properties of young planet-forming disks to their intrinsic values in simulations. Further extended investigations that tackle the caveats of this study, such as the lack of variation in the dust composition and distribution, dust-to-gas ratio, and other shortcomings in the numerical models, would be essential for setting constraints on our understanding of disk and planet formations.

Identifying Exoplanet Candidates Using WaveCeptionNet

In this study, we propose a wavelet-transform-based light curve representation method and a CNN model based on Inception-v3 for fast classification of light curves, enabling the quick discovery of potentially interesting targets from massive data. Experimental results on real observation data from the TESS showed that our wavelet processing method achieved about a 32-fold dimension reduction, while largely removing noise. We fed the wavelet-decomposed components of light curves into our improved Inception-v3 CNN model, achieving an accuracy of about 95%. Furthermore, our model achieves F1-scores of 95.63%, 95.93%, 95.65%, and 89.60% for eclipsing binaries, planet candidates, variable stars, and instrument noise, respectively. The precision rate of planet candidates identification reaches 96.49%, and the recall rate reaches 95.38% in the test set. The results demonstrate the effectiveness of our method for light curve.

Constructing the three-dimensional extinction density maps using V-net

ABSTRACT One of the major challenges we face is how to quickly and accurately create the three-dimensional (3D) density distributions of interstellar dust in the Milky Way using extinction and distance measurements of large samples of stars. In this study, we introduce a novel machine-learning approach that utilizes a convolution neural network, specifically a V-net, to infer the 3D distribution of dust density. Experiments are performed within two regions located towards the Galactic anticentre. The neural network is trained and tested using 10 000 simulations of dust density and line-of-sight extinction maps. Evaluation of the test sample confirms the successful generation of dust density maps from extinction maps by our model. Additionally, the performance of the trained network is evaluated using data from the literature. Our results demonstrate that our model is capable of capturing detailed dust density variations and can recover dust density maps while reducing the ‘fingers of god’ effect. Moving forward, we plan to apply this model to real observational data to obtain the fine distribution of dust at large and small scales in the Milky Way.

Synchronous Retrieval of Wheat Cab and LAI from UAV Remote Sensing: Application of the Optimized Estimation Inversion Framework

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R2 = 0.835, RMSE = 14.357; for LAI, R2 = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training.

Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data

Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier’s International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model’s accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm.

PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework

Time-domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions. Given the urgency of conducting follow-up observations for such objects, the development of an algorithm capable of detecting them and determining their magnitudes and positions has become imperative. Leveraging the advancements in deep neural networks, we present PNet, an end-to-end framework designed not only to detect celestial objects and extract their magnitudes and positions, but also to estimate the photometric uncertainty. PNet comprises two essential steps. First, it detects stars and retrieves their positions, magnitudes, and calibrated magnitudes. Subsequently, in the second phase, PNet estimates the uncertainty associated with the photometry results, serving as a valuable reference for the light-curve classification algorithm. Our algorithm has been tested using both simulated and real observation data, demonstrating the ability of PNet to deliver consistent and reliable outcomes. Integration of PNet into data-processing pipelines for time-domain astronomy holds significant potential for enhancing response speed and improving the detection capabilities for celestial objects with variable positions and magnitudes.

Impact of cardiovascular events and major bleeding in mortality in patients with atrial fibrillation and active lung cancer: data from a multicentre, retrospective, observational study

Abstract Background Lung cancer has a poor prognosis for most patients, as it is frequently diagnosed in advanced tumour stages. Real world observational data on the impact of major cardiovascular events (MACE) and major bleedings (MB) in the prognosis of patients with atrial fibrillation (AF) and active lung cancer are very limited. Purpose Our aim was to investigate the impact of MACE and MB in mortality in this population. Methods We used data from CANAC-FA Registry (Active Cancer and AF, in Spanish, CÁNcer ACtivo y Fibrilación Auricular), an observational, multicentre, retrospective study. The medical records of all subjects attended at the outpatient oncology clinics solely or mainly attending lung cancer patients from January 1st, 2017 to December 31st, 2019 in five tertiary university hospitals in the south of Spain were reviewed. The first visit to the oncology clinic with AF diagnosis during the first year after the lung cancer detection was considered the basal visit. Follow up period ended at December 31st, 2021. MACE (hospital admission for cardiovascular causes) and MB (International Society of Thrombosis and Haemostasis definition) were registered, and impact on survival was assessed for the whole series and according to tumour stage. Results Among 6984 patients, 269 presented active lung cancer and AF (3.9%). Mean age was 71±8 years, and 91% were male. Tumour stage was I, II, III and IV in 11%, 11%, 34% and 45% of the study sample, respectively. Anticoagulants were prescribed to 84% of the patients. After up to 46 months of maximum follow-up, 33 patients presented 40 MACE (13 heart failure admissions, 9 pulmonary embolisms, 5 strokes, 5 severe symptomatic arrhythmias, 4 deep vein thrombosis, 2 transient ischemic attacks, 2 systemic embolisms and 1 acute coronary syndrome), 16 patients had a MB and 186 died. However, two years’ mortality was similar in those patients with MACE or MB in follow-up versus those without them, in the whole of series (79% versus 73%, p=0.79, figure A), and in those with advanced cancer stages (III-IV, 89% versus 85%, p=0.39, figure B). In spite of that, in those patients with early tumour stages (I-II), two years’ mortality was significantly higher in those who suffered MACE or MB than in those free of both of them (85% versus 25%, p=0.01, figure C), and this difference remained after adjusting by other independent predictors of mortality (Hazard Ratio 11.08 [2.69-45.58], p=0.001). Conclusions In patients with AF and active lung cancer, patients with MACE and MB in follow up had similar mortality than those without them in the subgroup with advanced cancer stages. However, mortality was significantly higher in patients with these complications versus those without them in the subgroup with early cancer stages. This information could be useful for individualizing therapeutic efforts in this population.Impact of events in survival

Image restoration with point-spread function regularization and active learning

ABSTRACT Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.

A multifidelity approach to robust orbit determination

This paper presents an algorithm for the preprocessing of observation data aimed at improving the robustness of orbit determination tools. Two objectives are fulfilled: obtain a refined solution to the initial orbit determination problem and detect possible outliers in the processed measurements. The uncertainty on the initial estimate is propagated forward in time and progressively reduced by exploiting sensor data available in said propagation window. Differential algebra techniques and a novel automatic domain splitting algorithm for second-order Taylor expansions are used to efficiently propagate uncertainties over time. A multifidelity approach is employed to minimize the computational effort while retaining the accuracy of the propagated estimate. At each observation epoch, a polynomial map is obtained by projecting the propagated states onto the observable space. Domains that do no overlap with the actual measurement are pruned thus reducing the uncertainty to be further propagated. Measurement outliers are also detected in this step. The refined estimate and retained observations are then used to improve the robustness of batch orbit determination tools. The effectiveness of the algorithm is demonstrated for a geostationary transfer orbit object using synthetic and real observation data from the TAROT network.

Application of Sparse Regularization in Spherical Radial Basis Functions-Based Regional Geoid Modeling in Colorado

Spherical radial basis function (SRBF) is an effective method for calculating regional gravity field models. Calculating gravity field models with high accuracy and resolution requires dense basis functions, resulting in complex models. This study investigated the application of sparse regularization in SRBFs-based regional gravity field modeling. L1-norm regularization, also known as the least absolute shrinkage selection operator (LASSO), was employed in the parameter estimation procedure. LASSO differs from L2-norm regularization in that the solution obtained by LASSO is sparse, specifically with a portion of the parameters being zero. A sparse model would be advantageous for improving the numerical efficiency by reducing the number of SRBFs. The optimization problem of the LASSO was solved using the fast iterative shrinkage threshold algorithm, which is known for its high efficiency. The regularization parameter was selected using the Akaike information criterion. It was specifically tailored to the L1-norm regularization problem. An approximate covariance matrix of the estimated parameters in the sparse solution was analytically constructed from a Bayesian viewpoint. Based on the remove–compute–restore technique, a regional geoid model of Colorado (USA) was calculated. The numerical results suggest that the LASSO adopted in this study provided competitive results compared to Tikhonov regularization; however, the number of basis functions in the final model was less than 25% of the Tikhonov regularization. Without significantly reducing model accuracy, the LASSO solution provides a very simple model. This is the first study to apply the LASSO to SRBFs-based modeling of the regional gravity field in real gravity observation data.