Determination of magnetopause and bow shock shape based on convolutional neural network modelling of MESSENGER data

2021 ◽  
Author(s):  
Alexander Lavrukhin ◽  
David Parunakian ◽  
Dmitry Nevsky ◽  
Sahib Julka ◽  
Michael Granitzer ◽  
...  
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Bow Shock ◽  
Network Modelling ◽  
Machine Learning Methods ◽  
Magnetometer Data ◽  
Spacecraft Mission ◽  
Planetary Magnetic Field ◽  
Orbital Mission

<p><span id="E87">The magnetosphere of Mercury is relatively small and highly dynamic, mostly due to the weak planetary magnetic field. Varying solar wind conditions principally determine the location of both the </span><span id="E89">Hermean</span><span id="E91"> bow shock and magnetopause. In 2011 – 2015 MESSENGER spacecraft completed over 4000 orbits around Mercury, thus giving a data of more than 8000 crossings of bow shock and magnetopause of the planet, this makes it possible to study in detail the bow shock, the magnetopause and the </span><span id="E93">magnetosheath</span><span id="E95"> structures.</span></p> <p>In this work we determine crossings of the bow shock and the magnetopause of Mercury by applying machine learning methods to the MESSENGER magnetometer data. We attempt to identify the crossings during the whole duration of the orbital mission and model the average three-dimensional shapes of these boundaries. The results are compared with those previously obtained in other works.</p> <p><span id="E101">This work may be of interest for future Mercury research related to the </span><span id="E103">BepiColombo</span><span id="E105"> spacecraft mission, which will enter the orbit around the planet in December 2025.</span></p>

Automatic detection of magnetopause and bow shock crossing signatures in MESSENGER magnetometer data using Convolutional Neural Networks.

2021 ◽  
Author(s):  
Alexander Lavrukhin ◽  
David Parunakian ◽  
Dmitry Nevskiy ◽  
Sahib Julka ◽  
Michael Granitzer ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Three Dimensional ◽  
Activity Cycle ◽  
Solar Activity Cycle ◽  
Bow Shock ◽  
Cycle Phase ◽  
Small Subset ◽  
Magnetometer Data ◽  
Spacecraft Mission ◽  
Planetary Magnetic Field

<p>During its 2011-2015 lifetime the MESSENGER spacecraft completed more than 4000 orbits around Mercury, producing vast amounts of information regarding the planetary magnetic field and magnetospheric processes. During each orbit the spacecraft left and re-entered the Hermean magnetosphere, giving us information about more than 8000 crossings of the bow shock and the magnetopause of Mercury's magnetosphere. The information obtained from the magnetometer data offers the possibility to study in depth the structures of the bow shock and magnetopause current sheets and their shapes. In this work, we take a step in this direction by automatically detecting the crossings of bow-shock and magnetopause. To this end, we propose a five-class problem and train a Convolutional Neural Network based classifier using the magnetometer data. Our key experimental results indicate that an average precision and recall of at least 87% and 96% can be achieved on the bow hock and magnetopause crossings by using only a small subset of the data. We also model the average three-dimensional shape of these boundaries depending on the external interplanetary magnetic field . Furthermore, we attempt to clarify the dependence of the two boundary locations on the heliocentric distance of Mercury and on the solar activity cycle phase. This work may be of particular interest for future Mercury research related to the BepiColombo spacecraft mission, which will enter Mercury’s orbit around December 2025.</p>

Automatic detection of magnetopause and bow shock crossing signatures in MESSENGER magnetometer data

2020 ◽  
Author(s):  
Alexander Lavrukhin ◽  
David Parunakian ◽  
Dmitry Nevskiy ◽  
Ute Amerstorfer ◽  
Andreas Windisch ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Three Dimensional ◽  
Activity Cycle ◽  
Solar Activity Cycle ◽  
Bow Shock ◽  
Cycle Phase ◽  
Close Proximity ◽  
Magnetometer Data ◽  
Spacecraft Mission ◽  
Dimensional Shape

<p>The magnetosphere of Mercury is rather small and highly dynamic, due to its weak internal magnetic field and its close proximity to the Sun. The changing solar wind conditions principally determine the locations of both the Hermean bow shock and magnetopause. In 2011 – 2015 MESSENGER spacecraft completed more than 4000 orbits around Mercury, thus giving a data of more than 8000 crossings of bow shock and magnetopause of the planet. This makes it possible to study in detail the bow shock, the magnetopause and the magnetosheath structures.</p> <p>In this work, we determine crossings of the bow shock and the magnetopause of Mercury by applying machine learning methods to the MESSENGER magnetometer data. We try to identify the crossings for the complete orbital mission and model the average three-dimensional shape of these boundaries depending on the external interplanetary magnetic field (IMF). Further, we try to clarify the dependence of the two boundary locations on the heliocentric distance of Mercury and on the solar activity cycle phase. Also, we study the effect of the IMF partial penetration into the Hermean magnetosphere. The results are compared with the obtained previously in other works.</p> <p>This work may be of interest for future Mercury research related to the BepiColombo spacecraft mission, which will enter the orbit around the planet at December 2025.</p>

scholarly journals Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses

2014 ◽  
Vol 34 ◽  
pp. 55-63
Author(s):  
Joseph M. Caswell
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Decomposition ◽  
Cosmic Ray ◽  
Wavelet Neural Networks ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Wide Range ◽  
Artificial Neural ◽  
Planetary Magnetic Field

Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present study, a combination of wavelet neural networks was adapted from previous research in order to predict daily average values of cosmic ray impulses 30 days in advance. Additional comparison of both neural network and linear regression modelling with and without wavelet decomposition was conducted for further demonstration of increased accuracy with wavelet neural networks in a simple input-output fitting model

scholarly journals DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Adil Al-Azzawi ◽  
Anes Ouadou ◽  
Highsmith Max ◽  
Ye Duan ◽  
John J. Tanner ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Training Data ◽  
Human Intervention ◽  
Protein Particle ◽  
Particle Shapes

Abstract Background Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Results Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention. Conclusions Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.

scholarly journals DeepCryoPicker: Fully Automated Deep Neural Network for Single Protein Particle Picking in cryo-EM

2019 ◽  
Author(s):  
Adil Al-Azzawi ◽  
Anes Ouadou ◽  
Max R Highsmith ◽  
John J. Tanner ◽  
Ye Duan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Human Intervention ◽  
Cryo Electron Microscopy ◽  
Protein Particle ◽  
Particle Shapes ◽  
Automated Methods

Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio (SNR) of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker and RELION, with the significant advantage of not requiring human intervention.

Conformation of Ribosomes from Escherichia Coli

1974 ◽  
Vol 32 ◽  
pp. 210-211
Author(s):  
M. Boublik ◽  
W. Hellmann ◽  
F. Jenkins
Keyword(s):  
Binding Sites ◽  
Ribosomal Proteins ◽  
Fluorescent Dyes ◽  
Protein Biosynthesis ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Dimensional Structure ◽  
Complete Understanding ◽  
And Function

The present knowledge of the three-dimensional structure of ribosomes is far too limited to enable a complete understanding of the various roles which ribosomes play in protein biosynthesis. The spatial arrangement of proteins and ribonuclec acids in ribosomes can be analysed in many ways. Determination of binding sites for individual proteins on ribonuclec acid and locations of the mutual positions of proteins on the ribosome using labeling with fluorescent dyes, cross-linking reagents, neutron-diffraction or antibodies against ribosomal proteins seem to be most successful approaches. Structure and function of ribosomes can be correlated be depleting the complete ribosomes of some proteins to the functionally inactive core and by subsequent partial reconstitution in order to regain active ribosomal particles.

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