Reconciling space object observed and solar pressure albedo-areas via astrometric and photometric data fusion

There are many Resident Space Objects (RSOs) in the Geostationary Earth Orbit (GEO) regime, both operational and debris. The primary non-gravitational force acting on these RSOs is Solar Radiation Pressure (SRP), which is sensitive to the RSO’s area-to-mass ratio. Sparse observation data and mismodeling of non-gravitational forces has constrained the state of practice in tracking and characterizing RSOs. Accurate identification, characterization, tracking, and motion prediction of RSOs is a high priority research issue as it shall aid in assessing collision probabilities in the GEO regime, and orbital safety writ large. Previous work in characterizing RSOs has taken a preliminary step in exploiting fused astrometric and photometric data to estimate the RSO mass, shape, attitude, and size. This works, in theory, since angles data are sensitive to SRP albedo-area-to-mass ratio, and photometric data are sensitive to shape, attitude, and observed albedo-area. By fusing these two data types, mass and albedo-area both become observable parameters and can be estimated as independent quantities. However, previous work in mass and albedo-area estimation has not quantified and assessed the fundamental physical link between SRP albedo-area and observed albedo-area. The observed albedo-area is always a function of the SRP albedo-area along the line of sight of the observer. This is the physical relationship that this current research exploits. It is shown through simulation that due to this physical link, and through the fusion of astrometric and photometric data, it is possible to observe the mass of a space object when the area is not known. Results for data from 100 trajectories generated from randomly sampled initial conditions are shown. It is seen that even when the area of the object is not known, the uncertainty in mass can be lowered from an initial value of 800?kg to the range 500–700?kg for 72% of the samples, 200–500?kg for 13% of the samples, and 0–200?kg for 15% of the samples. It is further shown that although the uncertainties are large, the actual errors in mass are much lower, with the error RMS being less than 100?kg for 30% of the samples, between 100 and 200?kg for another 30%, and between 200 and 300?kg for 24% of the samples.     

A torus-shaped solar sail accelerated via thermal desorption of coating

A torus-shaped sail consists of a reflective membrane attached to an inflatable torus-shaped rim. The sail’s deployment from its stowed configuration is initiated by introducing inflation pressure into the toroidal rim with an attached circular flat membrane coated by heat-sensitive materials that undergo thermal desorption (TD) from a solid to a gas phase. Our study of the deployment and acceleration of the sail is split into three steps: at a particular heliocentric distance a torus-shaped sail is deployed by a gas inflated into the toroidal rim and the membrane is kept flat by the pressure of the gas; under heating by solar radiation, the membrane coat undergoes TD and the sail is accelerated via TD of coating and solar radiation pressure (SRP); when TD ends, the sail utilizes thrust only from SRP. We study the stability of the torus-shaped sail and deflection and vibration of the flat membrane due to the acceleration by TD and SRP.     

A statistical study of solar radio Type III bursts and space weather implication

Solar radio bursts (SRBs) are the signatures of various phenomenon that happen in the solar corona and interplanetary medium (IPM). In this article, we have studied occurrence of Type III bursts and their association with the Sunspot number. This study confirms that occurrence of Type III bursts correlate well with Sunspot number. Further, using the data obtained using e-CALLISTO network, we have investigated drift rates of isolated Type III bursts and duration of the group of Type III bursts. Since Type II, Type III and Type IV bursts are signatures of solar flares and/or CMEs, we can use the radio observations to predict space weather hazards. In this article, we have discussed two events that have caused near Earth radio blackouts. Since e-CALLISTO comprises more than 152 stations at different longitudes, we can use it to monitor the radio emissions from the solar corona 24 h a day. Such observations play a crucial role in monitoring and predicting space weather hazards within few minutes to hours of time.     

Quasi-periodic ripples in the heliosphere from 1 to 40 AU

This study extends the investigation of the ripples in the solar wind and the interplanetary magnetic field at L1 reported by Birch and Hargreaves (2020) to cover heliospheric distances from 1 to 40 AU, using data from the Voyager 2, Ulysses, Juno, Cassini, Themis and Apollo-12 spacecraft. The ripples were extracted from the source data using a bandpass filter which reduces the noise component of the source data while removing long-term trends. The ripples were found to propagate throughout the heliosphere with an average periodicity of 26 min, without significant attenuation relative to the background. They also permeated within the magnetospheres of Earth, Jupiter and Saturn with an average periodicity of 25 min, though with some attenuation relative to the solar wind, especially in the case of Jupiter. Within the planetary magnetospheres, the ripples were suppressed by the intense fields in close proximity to each planet, and though the distance varied at which this cutoff occurred, the flux density was very similar in all three cases.     

On the occurrence and strength of multi-frequency multi-GNSS Ionospheric Scintillations in Indian sector during declining phase of solar cycle 24

This study presents unique perspectives of occurrence and strength of low latitude ionospheric scintillations on multiple signals of Global Navigation Satellite System (GNSS) and its frequency dependence using continuous observation records of 780 nights. A robust comparative analysis is performed using scintillation index, S4 and its variation during pre-midnight and post-midnight duration from a GNSS receiver located at Waltair (17.7°N, 83.3°E), India, covering period from July 2014 to August 2016. The results, generally exhibit the impact of declining phase of solar cycle 24 on occurrence and strength of scintillations, which, however, is evidently different over different frequencies transmitted from different GNSS systems. A deeper quantitative analysis uniquely reveals that apart from the solar cycle and seasonal effects, the number of visible satellites of a selected GNSS markedly affect the occurrence and also the strength. Processing scheme of adopting 6 hourly time windows of pre-midnight and post-midnight brought a novel result that the strength and occurrence of strong scintillations decrease with declining solar activity during pre-midnight hours but remarkably increase for moderate and weak scintillations during post-midnight. The physical processes that dominate the post-midnight equatorial ionosphere are invoked to explain such variations that are special during declining solar activity. Finally, inter-GNSS signal analysis in terms of the effect of strong, moderate and weak scintillations is presented with due consideration of number of satellite passes affected and frequency dependence of mean S4. The quantitative results of this study emphasize for the first time effect of low latitude scintillation on GNSS signals in Indian zone under changing background solar and seasonal conditions.     

Combining MHD and kinetic modelling of solar flares

Solar flares are explosive events in the solar corona, representing fast conversion of magnetic energy into thermal and kinetic energy, and hence radiation, due to magnetic reconnection. Modelling is essential for understanding and predicting these events. However, self-consistent modelling is extremely difficult due to the vast spatial and temporal scale separation between processes involving thermal plasma (normally considered using magnetohydrodynamic (MHD) approach) and non-thermal plasma (requiring a kinetic approach). In this mini-review we consider different approaches aimed at bridging the gap between fluid and kinetic modelling of solar flares. Two types of approaches are discussed: combined MHD/test-particle (MHDTP) models, which can be used for modelling the flaring corona with relatively small numbers of energetic particles, and hybrid fluid-kinetic methods, which can be used for modelling stronger events with higher numbers of energetic particles. Two specific examples are discussed in more detail: MHDTP models of magnetic reconnection and particle acceleration in kink-unstable twisted coronal loops, and a novel reduced-kinetic model of particle transport in converging magnetic fields.     

A triangle voting algorithm based on double feature constraints for star sensors

A novel autonomous star identification algorithm is presented in this study. In the proposed algorithm, each sensor star constructs multi-triangle with its bright neighbor stars and obtains its candidates by triangle voting process, in which the triangle is considered as the basic voting element. In order to accelerate the speed of this algorithm and reduce the required memory for star database, feature extraction is carried out to reduce the dimension of triangles and each triangle is described by its base and height. During the identification period, the voting scheme based on double feature constraints is proposed to implement triangle voting. This scheme guarantees that only the catalog star satisfying two features can vote for the sensor star, which improves the robustness towards false stars. The simulation and real star image test demonstrate that compared with the other two algorithms, the proposed algorithm is more robust towards position noise, magnitude noise and false stars.     

Fostering research aptitude among high school students through space weather competition

Cultivating research culture at an early stage is important for capacity building in a community. The high school level is the appropriate stage for research to be introduced because of students’ competitive nature. Participation in the space weather competition is one of the ways in which research aptitude can be fostered in high school students in Malaysia. Accordingly, this paper presents how research elements were introduced to the students at the high school level through their participation in the space weather competition. The competition required the students to build a system to detect the presence of solar flares by utilizing VLF signals reflected from the ionosphere. The space weather competition started off with proposal writing for the space weather related project where the students were required to execute extensive literature review on the given topic. Additionally, the students were also required to conduct the experiments and analyse the data. Results obtained from data analysis were then validated by the students through various other observations that they had to carry out. At the end of the competition, students were expected to write a comprehensive technical report. Through this competition, the students learnt how to conduct research in accordance to the guidelines provided through the step by step approach exposed to them. Ultimately, this project revealed that the students were able to conduct research on their own with minimal guidance and that participation in the competition not only generated enjoyment in learning but also their interest in science and research.     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

The impact of coronal mass ejection on the horizontal geomagnetic fields and the induced geoelectric fields

This work investigates the influence of coronal mass ejection (CME) on the time derivatives of horizontal geomagnetic and geoelectric fields, proxy parameters for identifying GICs. 16 events were identified for the year 2003 from the CORONAS-PHOTON spacecraft. Five of the events (May 29, June 9, October 28, October 29, and November 4) were extensively discussed over four magnetic observatories, were analyzed using the time derivatives of the horizontal geomagnetic (dH/dt) and geoelectric (E_H) fields obtained from data of the INTERMAGNET network. It was observed that energy distributions of the wavelet power spectrum of the horizontal geoelectric field are noticed at the nighttime on both 29 May and 9 June 2003 across the stations. Daytime and nighttime intensification of energy distribution of the wavelet power spectrum of the horizontal geoelectric field are observed on both 28 and 29 October 2003 due to strong westward electrojet. The 4 November 2003 event depicts daytime amplification of energy distributions of the wavelet power spectrum across the stations. The highest correlation magnitude is obtained in the event of 4 November 2003 between dH/dt and E_H relationships during the intense solar flare of class X 17.4. We observed that the correlation magnitude between dH/dt and E_H increases with increase in CME activity. We concluded that the response of the surface impedance model for different stations plays a key role in determining the surface electric field strength, due to large electric field changes at different stations.