Hunting for overlooked eccentric eclipsing binaries from ASAS-3 survey

We present the results of searching for new candidates of eclipsing binaries with eccentric orbits in the ASAS Catalog of Variable Stars (ACVS) using publicly available data from the All Sky Automated Survey (ASAS) and the Transiting Exoplanet Survey Satellite (TESS). Unsupervised machine learning techniques were applied to find anomalies among the light curves of eclipsing binaries. The light curves modeling were performed using JKTEBOP code. The pulsation analysis was done with FAMIAS. We identified 19 new eclipsing binary candidates with non-zero eccentricities in the ACVS, including 10 candidates with eccentricities e ≥ 0.1. Estimates of eccentricities are given. We also report on possible presence of the small-amplitude stellar pulsations at least in two of the reported systems.     

Transiting exoplanets from the CoRoT space mission Resolving the nature of transit candidates for the LRa03 and SRa03 fields

CoRoT is a space telescope which aims at studying internal structure of stars and detecting extrasolar planets. We present here a list of transits detected in the light curves of stars observed by CoRoT in two fields in the anti-center direction: the LRa03 one observed during 148 days from 3 October 2009 to 1 March 2010 followed by the SRa03 one from the 5 March 2010 to the 29 March 2010 during 25 days. 5329 light curves for the LRa03 field and 4169 for the SRa03 field were analyzed by the detection team of CoRoT. Then some of the selected exoplanetary candidates have been followed up from the ground. In the LRa03 field, 19 exoplanet candidates have been found, 8?remain unsolved. No secured planet has been found yet. In the SRa03 field, there were 11 exoplanetary candidates among which 6 cases remain unsolved and 3 planets have been found: CoRoT-18b, CoRoT-19b, CoRoT-20b.     

Machine Leaning-Based Investigation of the Associations between CMEs and Filaments

In this work we study the association between eruptive filaments/prominences and coronal mass ejections (CMEs) using machine learning-based algorithms that analyse the solar data available between January 1996 and December 2001. The support vector machine (SVM) learning algorithm is used for the purpose of knowledge extraction from the association results. The aim is to identify patterns of associations that can be represented using SVM learning rules for the subsequent use in near real-time and reliable CME prediction systems. Timing and location data in the US National Geophysical Data Center (NGDC) filament catalogue and the Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph (SOHO/LASCO) CME catalogue are processed to associate filaments with CMEs. In the previous studies, which classified CMEs into gradual and impulsive CMEs, the associations were refined based on the CME speed and acceleration. Then the associated pairs were refined manually to increase the accuracy of the training dataset. In the current study, a data-mining system is created to process and associate filament and CME data, which are arranged in numerical training vectors. Then the data are fed to SVMs to extract the embedded knowledge and provide the learning rules that can have the potential, in the future, to provide automated predictions of CMEs. The features representing the event time (average of the start and end times), duration, type, and extent of the filaments are extracted from all the associated and not-associated filaments and converted to a numerical format that is suitable for SVM use. Several validation and verification methods are used on the extracted dataset to determine if CMEs can be predicted solely and efficiently based on the associated filaments. More than 14?000 experiments are carried out to optimise the SVM and determine the input features that provide the best performance.     

基于图像相减和随机森林的AST3巡天暂现源及变源搜寻方法

AST3-2 (Antarctic Survey Telescopes)光学巡天望远镜位于南极大陆最高点冰穹A,其产生的大量观测数据对数据处理的效率提出了较高要求.同时南极通信不便,数据回传有诸多困难,有必要在南极本地实现自动处理AST3-2观测数据,进行变源和暂现源观测的数据处理,但是受到低功耗计算机的限制,数据的快速自动处理的实现存在诸多困难.将已有的图像相减方案同机器学习算法相结合,并利用AST3-2 2016年观测数据作为测试样本,发展一套的暂现源及变源的筛选方法成为可行的选择.该筛选方法使用图像相减法初步筛选出可能的变源,再用主成分分析法抽取候选源的特征,并选择随机森林作为机器学习分类器,在测试中对正样本的召回率达到了97%,验证了这种方法的可行性,并最终在2016年观测数据中探测出一批变星候选体.     

An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.     

基于光变曲线的空间碎片基本形状分类

将未编目的空间碎片正确分类是空间态势感知的重要组成部分. 基于光变曲线, 通过仿真和实测实验, 探讨了空间碎片基本类型的机器学习分类方法. 在数据集中的仿真光变来自形状或材料不同的4类碎片, 实测光变从Mini-Mega TORTORA (MMT)数据库中提取, 实验以深度神经网络作为分类模型, 并和其他机器学习方法进行了比较. 结果显示深度卷积网络优于其他算法, 在仿真实验中对不同材料的圆柱体都能准确识别, 对其余两类卫星的识别率在90%左右; 实测实验中对火箭体和失效卫星的2分类准确率超过99%, 然而在进一步的型号/平台分类中, 准确率有所降低.     

The Basic Shape Classification of Space Debris with Light Curves

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower.     

Solar system,exoplanets, and anthropic principle

It is shown that the planetary distances of the Solar System are distributed according to the L ₀ resonance, where L ₀ = cP ₀ = 19.24 a.u. is the wavelength of the “cosmological oscillation” of the Universe (whose nature is unknown). Here, c is the speed of light and P ₀ = 160 min is the period of pulsations of the Sun and the Universe, which turned out to be equal to 1/9 of the mean terrestrial day. Exoplanets do not exhibit the L ₀ resonance; instead, they demonstrate on average a spatial resonance on a scale of 14.8 a.u., pointing to a mechanism of formation of exoplanetary systems which differs from the commonly accepted one (by the capture of “mesoplanets,” rather than from near-star nebulae). This indicates that the L ₀ resonance is a specific feature just of the Solar System. The L ₀ (P ₀) aspect of the anthropic principle, realized only near the Sun, distinguishes our planetary system from a number of observed exoplanetary systems. This fact makes the anthropic principle in its strong formulation more evident, localizing its effectiveness. Probably, it is closely related to the appearance of life on the Earth, which unexpectedly, sadly, and charmingly makes any talks on extraterrestrial civilizations devoid of any prospect.     

Increasing Lifetime of Recurrent Sunspot Groups Within the Greenwich Photoheliographic Results

Long-lived (>20 days) sunspot groups extracted from the Greenwich Photoheliographic Results (GPR) are examined for evidence of decadal change. The problem of identifying sunspot groups that are observed on consecutive solar rotations (recurrent sunspot groups) is tackled by first constructing manually an example dataset of recurrent sunspot groups and then using machine learning to generalise this subset to the whole GPR. The resulting dataset of recurrent sunspot groups is verified against previous work by A. Maunder and other Royal Greenwich Observatory (RGO) compilers. Recurrent groups are found to exhibit a slightly larger value for the Gnevyshev?–?Waldmeier Relationship than the value found by Petrovay and van Driel-Gesztelyi (Solar Phys. 51, 25, 1977), who used recurrence data from the Debrecen Photoheliographic Results. Evidence for sunspot-group lifetime change over the previous century is observed within recurrent groups. A lifetime increase of a factor of 1.4 between 1915 and 1940 is found, which closely agrees with results from Blanter et al. (Solar Phys. 237, 329, 2006). Furthermore, this increase is found to exist over a longer period (1915 to 1950) than previously thought and provisional evidence is found for a decline between 1950 and 1965. Possible applications of machine-learning procedures to the analysis of historical sunspot observations, the determination of the magnetic topology of the solar corona and the incidence of severe space–weather events are outlined briefly.     

On the global stability of single‐planet systems

In this paper, we estimate the global stability properties of single‐planet systems by using a catalogue of stability maps. The data of the catalogue were used to generate probability values on the mass parameter–eccentricity plane for the occurrence of stable orbits. We showed that the probability data can be well approximated by a second order surface. Using the resulted formula the likelihood of finding Earth‐like planets in single‐planet systems can be easily estimated. As an example, we derived estimations for four known exoplanetary systems. Our formula can be useful in selecting target stars for future space missions. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The Rotational Periods of Three C-Type Asteroids 431, 521 and 524

The determination for asteroids’ spin parameters is very important for the physical study of asteroids and their evolution. Sometimes, the low amplitude of light curves and kinds of systematic errors in photometric data prevent the determination of the asteroids’ spin period. To solve such a problem, we introduced the de-correlation methods developed in searching for exoplanetary transit signal into the asteroid’s data reduction in this paper. By applying the principle of Collier Cameron (MNRAS 373:799–810, 2006) and Tamuz et al. (MNRAS 356:1466–1470, 2005)’s, we simulated the systematic effects in photometric data of asteroid, and removed those simulated errors from photometric data. Therefore the S/N of intrinsic signals of three selected asteroids are enhanced significantly. As results, we derived the new spin periods of 18.821 ± 0.011 h, 28.202 ± +0.071 h for (431) and (521) respectively, and refined the spin period of (524) as 14.172 ± 0.016 h.     

Periastron precession measurements in transiting extrasolar planetary systems at the level of general relativity

Transiting exoplanetary systems are surpassingly important among the planetary systems since they provide the widest spectrum of information for both the planet and the host star. If a transiting planet is on an eccentric orbit, the duration of transits T _D is sensitive to the orientation of the orbital ellipse relative to the line of sight. The precession of the orbit results in a systematic variation in both the duration of individual transit events and the observed period between successive transits,   P _obs  . The periastron of the ellipse slowly precesses due to general relativity and possibly the presence of other planets in the system. This secular precession can be detected through the long-term change in   P _obs  (transit timing variations, TTV) or in T _D (transit duration variations, TDV). We estimate the corresponding precession measurement precision for repeated future observations of the known eccentric transiting exoplanetary systems (XO-3b, HD 147506b, GJ 436b and HD 17156b) using existing or planned space-borne instruments. The TDV measurement improves the precession detection sensitivity by orders of magnitude over the TTV measurement. We find that TDV measurements over a approximately 4 yr period can typically detect the precession rate to a precision well exceeding the level predicted by general relativity.     

Research on Pulsar Candidate Identification Method Based on Deep Residual Neural Network

As the next-generation radio astronomical telescopes continuously improve and develop, the pulsar survey will produce millions of pulsar candidates, which pose considerable challenges for pulsar identification and classification. The rapidly evolving artificial intelligence (AI) techniques are being used for pulsar identification and discovery of new pulsars. Using the pulsar data set observed with the Parkes telescope, namely the High Time Resolution Universe Survey (HTRUS), a 14-layer deep residual network has been designed (called the Residual Network, ResNet) for pulsar candidate classifications. In the HTRUS sample data, the number of non-pulsar candidates (i.e., negative samples) is much larger than that of pulsar candidates (i.e., positive samples). The imbalance between the positive and negative samples is prone to result in model misjudgement. By using the over-sampling technique to enhance the data of positive samples in the training set and adjusting the ratio of positive and negative samples, we have solved this imbalance problem. In the training process, the hyperparameters are adjusted by means of 5-fold cross validation to build the model. The test results indicate that the model can achieve a high precision (98%) and recall (100%), the F1-score can reach 99%, and that the implementation of each sample test needs only 7 ms, it has provided a feasible approach for the future big-data analysis of pulsars.     

Protostellar classification using supervised machine learning algorithms

Classification of young stellar objects (YSOs) into different evolutionary stages helps us to understand the formation process of new stars and planetary systems. Such classification has traditionally been based on spectral energy distribution (SED) analysis. An alternative approach is provided by supervised machine learning algorithms, which can be trained to classify large samples of YSOs much faster than via SED analysis. We attempt to classify a sample of Orion YSOs (the parent sample size is 330) into different classes, where each source has already been classified using multiwavelength SED analysis. We used eight different learning algorithms to classify the target YSOs, namely a decision tree, random forest, gradient boosting machine (GBM), logistic regression, naïve Bayes classifier, \(k\)-nearest neighbour classifier, support vector machine, and neural network. The classifiers were trained and tested by using a 10-fold cross-validation procedure. As the learning features, we employed ten different continuum flux densities spanning from the near-infrared to submillimetre wavebands (\(\lambda= 3.6\mbox{--}870~\upmu\mbox{m}\)). With a classification accuracy of 82% (with respect to the SED-based classes), a GBM algorithm was found to exhibit the best performance. The lowest accuracy of 47% was obtained with a naïve Bayes classifier. Our analysis suggests that the inclusion of the \(3.6~\upmu\mbox{m}\) and \(24~\upmu\mbox{m}\) flux densities is useful to maximise the YSO classification accuracy. Although machine learning has the potential to provide a rapid and fairly reliable way to classify YSOs, an SED analysis is still needed to derive the physical properties of the sources (e.g. dust temperature and mass), and to create the labelled training data. The machine learning classification accuracies can be improved with respect to the present results by using larger data sets, more detailed missing value imputation, and advanced ensemble methods (e.g. extreme gradient boosting). Overall, the application of machine learning is expected to be very useful in the era of big astronomical data, for example to quickly assemble interesting target source samples for follow-up studies.     

基于深层残差网络的脉冲星候选体分类方法研究

随着下一代射电天文望远镜的不断改进和发展,脉冲星巡天观测将发现数百万个脉冲星候选体,这给脉冲星的识别和新脉冲星的发现带来了巨大挑战,迅速发展的人工智能技术可用于脉冲星识别.使用Parkes望远镜的脉冲星数据集(The High Time Resolution Universe Survey,HTRUS),设计了一个14层深的残差网络(Residual Network,ResNet)进行脉冲星候选体分类.在HTRUS数据样本中,存在非脉冲星候选体(负样本)的数目远远大于脉冲星候选体(正样本)数目的样本非均衡问题,容易产生模型误判.通过使用过采样技术对训练集中的正样本进行数据增强,并调整正负样本的比例,解决了正负样本非均衡问题.训练过程中,使用5折交叉验证来调节超参数,最终构建出模型.测试结果表明,该模型能够取得较高的精确度(Precision)和召回率(Recall),分别为98%和100%,F1分数(F1-score)能够达到99%,每个样本检测完成只需要7 ms,为未来脉冲星大数据分析提供了一个可行的办法.     

Dependence of DOLP on Coronal Electron Temperature,Speed, and Structure

We study the predictive capabilities of magnetic-feature properties (MF) generated by the Solar Monitor Active Region Tracker (SMART: Higgins et al. in Adv. Space Res. 47, 2105, 2011) for solar-flare forecasting from two datasets: the full dataset of SMART detections from 1996 to 2010 which has been previously studied by Ahmed et al. (Solar Phys. 283, 157, 2013) and a subset of that dataset that only includes detections that are NOAA active regions (ARs). The main contributions of this work are: we use marginal relevance as a filter feature selection method to identify the most useful SMART MF properties for separating flaring from non-flaring detections and logistic regression to derive classification rules to predict future observations. For comparison, we employ a Random Forest, Support Vector Machine, and a set of Deep Neural Network models, as well as lasso for feature selection. Using the linear model with three features we obtain significantly better results (True Skill Score: TSS = 0.84) than those reported by Ahmed et al. (Solar Phys. 283, 157, 2013) for the full dataset of SMART detections. The same model produced competitive results (TSS = 0.67) for the dataset of SMART detections that are NOAA ARs, which can be compared to a broader section of flare-forecasting literature. We show that more complex models are not required for this data.

     

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012?–?2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude \({>}\,\mbox{M1}\) and \({>}\,\mbox{C1}\) within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy \(\mathrm{ACC}=0.93(0.00)\), true skill statistic \(\mathrm{TSS}=0.74(0.02)\), and Heidke skill score \(\mathrm{HSS}=0.49(0.01)\) for \({>}\,\mbox{M1}\) flare prediction with probability threshold 15% and \(\mathrm{ACC}=0.84(0.00)\), \(\mathrm{TSS}=0.60(0.01)\), and \(\mathrm{HSS}=0.59(0.01)\) for \({>}\,\mbox{C1}\) flare prediction with probability threshold 35%.     

On the extension of the Laplace-Lagrange secular theory to order two in the masses for extrasolar systems

We study the secular evolution of several exoplanetary systems by extending the Laplace-Lagrange theory to order two in the masses. Using an expansion of the Hamiltonian in the Poincaré canonical variables, we determine the fundamental frequencies of the motion and compute analytically the long-term evolution of the Keplerian elements. Our study clearly shows that, for systems close to a mean-motion resonance, the second order approximation describes their secular evolution more accurately than the usually adopted first order one. Moreover, this approach takes into account the influence of the mean anomalies on the secular dynamics. Finally, we set up a simple criterion that is useful to discriminate between three different categories of planetary systems: (i) secular systems (HD 11964, HD 74156, HD 134987, HD 163607, HD 12661 and HD 147018); (ii) systems near a mean-motion resonance (HD 11506, HD 177830, HD 9446, HD 169830 and $\upsilon $ υ  Andromedae); (iii) systems really close to or in a mean-motion resonance (HD 108874, HD 128311 and HD 183263).     

Stability of Co-Orbital Motion in Exoplanetary Systems

The stability of co-orbital motions is investigated in such exoplanetary systems, where the only known giant planet either moves fully in the habitable zone, or leaves it for some part of its orbit. If the regions around the triangular Lagrangian points are stable, they are possible places for smaller Trojan-like planets. We have determined the nonlinear stability regions around the Lagrangian point L₄ of nine exoplanetary systems in the model of the elliptic restricted three-body problem by using the method of the relative Lyapunov indicators. According to our results, all systems could possess small Trojan-like planets. Several features of the stability regions are also discussed. Finally, the size of the stability region around L₄ in the elliptic restricted three-body problem is determined as a function of the mass parameter and eccentricity.