First radio evidence for ubiquitous magnetic reconnections and impulsive heating in the quiet solar corona 

Author(s):  
Surajit Mondal ◽  
Divya Oberoi ◽  
Ayan Biswas ◽  
Shabbir Bawaji ◽  
Ujjaini Alam ◽  
...  

<p>It has been a long standing problem as to how the solar corona can maintain its million K temperature, while the photosphere, which is the lowest layer of the solar atmosphere, is only at a temperature of 5800 K. A very promising theory to explain this is the “nanoflare” hypothesis, which suggests that numerous flares of energies ~10<sup>24</sup> ergs are always happening in the solar corona, and maintain its million K temperature. However, detecting these nanoflares directly is challenging with the current instrumentation as they are hypothesised to occur at very small spatial, temporal and energy scales. These nanoflares are expected to produce nonthermal electrons, which are expected to emit in the radio band. These nonthermal emissions are often brighter than their thermal counterparts and might be detectable with current radio instruments. Due to their importance multiple searches for these nonthermal emissions have been done, but thus far they have been  limited to active regions. The quiet corona is also hot, and often comprises the bulk of the coronal region, so it is equally important to understand the physical processes which maintain this medium at MK temperatures. We describe the results from our effort to use the data from the Murchison Widefield Array (MWA) to search for impulsive radio emissions in the quiet solar corona. By pushing the detection threshold of nonthermal emission by about two orders of magnitude lower than previous studies, we have uncovered ubiquitous very impulsive nonthermal emissions from the quiet sun. We refer to these emissions as Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs). Using independent observations spanning very different solar conditions we show that WINQSEs are present throughout the quiet corona at all times. Their occurrence rate lies in the range of many hundreds to about a thousand per minute, implying that on average order 10 or so WINQSEs are present in every 0.5 s MWA image. Preliminary estimates suggest that WINQSEs have a bandwidth of ~2 MHz. Buoyed by  their possible connection to the hypothesised “nanoflares”, we are pursuing several projects to characterise and understand them. These include developing machine learning algorithms to identify WINQSEs in radio images and characterise their morphologies; exploring the ability of the present generation EUV and X-ray instruments to estimate the energy corresponding to the brightest of WINQSEs; and attempting very high time resolution imaging to explore their temporal structure. In this talk, I will present the results from the past and ongoing projects about WINQSEs and argue that these might be a key step towards detecting “nanoflares” and the resolution of the coronal heating problem.</p><p> </p><p> </p>

1994 ◽  
Vol 144 ◽  
pp. 431-434
Author(s):  
M. Minarovjech ◽  
M. Rybanský

AbstractThis paper deals with a possibility to use the ground-based method of observation in order to solve basic problems connected with the solar corona research. Namely:1.heating of the solar corona2.course of the global cycle in the corona3.rotation of the solar corona and development of active regions.There is stressed a possibility of high-time resolution of the coronal line photometer at Lomnický Peak coronal station, and use of the latter to obtain crucial observations.


2007 ◽  
Vol 3 (S247) ◽  
pp. 243-250
Author(s):  
I. Ballai ◽  
M. Douglas

AbstractObservations in EUV lines of the solar corona revealed large scale propagating waves generated by eruptive events able to travel across the solar disk for large distances. In the low corona, CMEs are known to generate, e.g. EIT waves which can be used to sample the coronal local and global magnetic field. This contribution presents theoretical models for finding values of magnetic field in the quiet Sun and coronal loops based on the interaction of global waves and local coronal loops as well as results on the generation and propagation of EIT waves. The physical connection between local and global solar coronal events (e.g. flares, EIT waves and coronal loop oscillations) will also be explored.


2021 ◽  
Vol 923 (2) ◽  
pp. L33
Author(s):  
Dmitrii Y. Kolotkov ◽  
Valery M. Nakariakov ◽  
Robin Holt ◽  
Alexey A. Kuznetsov

Abstract We present the first multiwavelength simultaneous detection of quasi-periodic pulsations (QPPs) in a superflare (more than a thousand times stronger than known solar flares) on a cool star, in soft X-rays (SXRs, with XMM-Newton) and white light (WL, with Kepler). It allowed for the first ever analysis of oscillatory processes in a stellar flare simultaneously in thermal and nonthermal emissions, conventionally considered to come from the corona and chromosphere of the star, respectively. The observed QPPs have periods 1.5 ± 0.15 hr (SXR) and 3 ± 0.6 hr (WL), and correlate well with each other. The unique relationship between the observed parameters of QPPs in SXR and WL allowed us to link them with oscillations of the electric current in the flare loop, which directly affect the dynamics of nonthermal electrons and indirectly (via ohmic heating) the thermal plasma. These findings could be considered in favor of the equivalent LCR contour model of a flare loop, at least in the extreme conditions of a stellar superflare.


Author(s):  
Yong-Jin Jung ◽  
Kyoung-Woo Cho ◽  
Jong-Sung Lee ◽  
Chang-Heon Oh

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model.


2018 ◽  
Vol 13 (S340) ◽  
pp. 181-182
Author(s):  
Rohit Sharma ◽  
Divya Oberoi ◽  
Akshay Suresh ◽  
Mihir Arjunwadkar

An improved understanding of the solar corona is crucial for making progress on long-standing problems like coronal heating and the origin of the solar wind. Metrewave radio emissions arise in the coronal regions and form a unique diagnostic probe of this, otherwise hard to study region. The background radio emission at these wavelengths comes from the slowly varying thermal free-free emission and on it are superposed a variety of nonthermal emissions arising from a range of plasma emission processes. The latter are coherent in nature and hence lead to a much larger observational contrast, as compared to that in EUV or X-ray, for emissions involving similar energetics. One of the prevalent hypotheses for explaining coronal heating is based on the presence of an energetically weak population of ‘nanoflares’ (Parker 1988). A necessary requirement for nanoflares based coronal heating to be effective is that their occurrence rate slopes must be <-2 (Hudson 1991). There is hence a lot of interest in studies of weak nonthermal emissions. Existing studies in EUV and X-ray bands have detected ‘microflares’ with slopes >-2 (e.g. Hannah et al. 2011). Some of the weak meterwave emissions detected are, however, believed to correspond to energies in the ‘picoflare’ range (Ramesh et al. 2013). It is hence, very interesting to study weak nonthermal emissions at metric wavelengths.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ernesto Lee ◽  
Furqan Rustam ◽  
Wajdi Aljedaani ◽  
Abid Ishaq ◽  
Vaibhav Rupapara ◽  
...  

Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction.


1970 ◽  
Vol 1 (7) ◽  
pp. 304-305 ◽  
Author(s):  
K. V. Sheridan

In this preliminary study the first two-dimensional pictures showing detailed features of the quiet Sun and weak but moderately stable structure at metre wavelengths are presented. The observations were made at 80 MHz with the Culgoora radioheliograph.


1989 ◽  
Vol 104 (2) ◽  
pp. 67-69
Author(s):  
B.A. Burnasheva ◽  
R.E. Gershberg ◽  
A.M. Zvereva ◽  
I.V. Ilyin ◽  
N.I. Shakhovskaya ◽  
...  

AbstractWhile monitoring the flare star EV Lac with high time resolution using the Space Astrophysical Station ASTRON, a rather strong flare was recorded. During this event, flare emissions were detected in the C IV (λ1550 Å) UV line, in the narrow band continuum at λ2434 Å (28 Å bandwidth) and in the wide wavelength range from 1700 Å to 6500 Å, all emission enhancements taking place within 10 s. About 50 s after the flare start, a fast and very powerful burst of the C IV line took place.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ferdousi Sabera Rawnaque ◽  
Khandoker Mahmudur Rahman ◽  
Syed Ferhat Anwar ◽  
Ravi Vaidyanathan ◽  
Tom Chau ◽  
...  

Abstract Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.


2021 ◽  
Author(s):  
David Malaspina ◽  
Lynn Wilson ◽  
Robert Ergun ◽  
Stuart Bale ◽  
John Bonnell ◽  
...  

&lt;p&gt;Recent studies of the solar wind sunward of 0.25 AU using the Parker Solar Probe spacecraft reveal that that solar wind can be bimodal, alternating between near quiescent regions with low turbulent fluctuation amplitudes and Parker-like magnetic field direction and regions of highly turbulent plasma and magnetic field fluctuations associated with &amp;#8216;switchbacks&amp;#8217; of the radial magnetic field. &amp;#160;&lt;/p&gt;&lt;p&gt;The quiescent solar wind regions are highly unstable to the formation of plasma waves near the electron cyclotron frequency (fce), possibly driven by strahl electrons, which carry the solar wind heat flux, and may provide one of the most direct particle diagnostics of the solar corona at the source of the solar wind. &amp;#160;These waves are most intense near ~0.7 fce and ~fce. The near-fce waves are found to become more intense and more frequent closer to the Sun, and statistical evidence indicates that their occurrence rate is related to the sunward drift of the core electron population. &amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we examine high time resolution burst captures of these waves, demonstrating that each wave burst contains several distinct wave types, including electron Bernstein waves and extremely narrow band waves that are highly sensitive to the magnetic field orientation. Using properties of these waves we provide evidence to support the identification of their likely plasma wave modes and the instabilities responsible for generating these waves. &amp;#160;By understanding the driving instabilities responsible for these waves, we infer their ability to modify electron distribution functions in the quiescent near-Sun solar wind. &amp;#160;&lt;/p&gt;


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