A Statistical Analysis of Radar Blackout Events at Mars

2020 ◽  
Author(s):  
Mark Lester ◽  
Beatriz Sanchez-Cano ◽  
Hannah Biddle ◽  
Daniel Potts ◽  
Pierre-Louis Blelly ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Solar Cycle ◽  
Signal Detection ◽  
Solar Maximum ◽  
Martian Atmosphere ◽  
Data Sets ◽  
Surface Mode ◽  
Mars Express ◽  
Additional Mode ◽  
Cycle Dependence

<p>The loss of signal detection by the sub surface radars currently operational on Mars Express and Mars Reconnaissance Orbiter can be evidence of enhanced ionisation at lower altitudes in the Martian atmosphere as a result of solar energetic particles penetrating to these altitudes.  The MARSIS instrument on Mars Express and SHARAD on MRO operate at different frequencies, with MARSIS up to 5 MHz and SHARAD between 10 and 20 MHZ.  In addition MARSIS can operate in an additional mode as an Active Ionospheric Sounder, although here we focus only on the sub surface mode.  We present an analysis of the data during the lifetimes of both instruments, extending from 2005 to 2018.  Here we identify the radar blackouts as either total or partial and investigate their occurrence as a function of solar cycle.  We find a clear solar cycle dependence with more events occurring during the solar maximum years, as expected.  However, we also note the duration of events is often much longer than expected, in excess of several days, sometimes reaching 10 – 14 days.  Investigation of other data sets, notably from the MAVEN SEP instrument complements the analysis.  We finally compare our observations at Mars with similar observations at Earth.</p>

scholarly journals Solar cycle variations in the ionosphere of Mars as seen by multiple Mars Express data sets

2016 ◽  
Vol 121 (3) ◽  
pp. 2547-2568 ◽  
Author(s):  
B. Sánchez-Cano ◽  
M. Lester ◽  
O. Witasse ◽  
S. E. Milan ◽  
B. E. S. Hall ◽  
...  
Keyword(s):  
Solar Cycle ◽  
Data Sets ◽  
Mars Express

scholarly journals Total electron content in the Martian atmosphere: A critical assessment of the Mars Express MARSIS data sets

2015 ◽  
Vol 120 (3) ◽  
pp. 2166-2182 ◽  
Author(s):  
B. Sánchez-Cano ◽  
D. D. Morgan ◽  
O. Witasse ◽  
S. M. Radicella ◽  
M. Herraiz ◽  
...  
Keyword(s):  
Total Electron Content ◽  
Martian Atmosphere ◽  
Critical Assessment ◽  
Electron Content ◽  
Data Sets ◽  
Total Electron ◽  
Mars Express

scholarly journals The solar-cycle variation of M(3000)F2 and its correlation with that of f0F2

1998 ◽  
Vol 41 (4) ◽  
Author(s):  
S. S. Kouris
Keyword(s):  
Statistical Analysis ◽  
Solar Cycle ◽  
Measurement Data ◽  
Data Sets ◽  
Empirical Relationships ◽  
Solar Cycle Variation ◽  
Cycle Variation ◽  
Best Fit ◽  
Ionospheric Sounding

Using hourly monthly-median measured values from nine long-standing ionospheric sounding stations with data sets extending over several decades, best-fit empirical relationships are established for M (3000)F2 with different solar and ionospheric indices representative of state of the solar cycle. The statistical analysis shows that there is no difference in the degree of correlation in using one index over another. Comparisons are also made with similar relationships for monthly median f0F2 determined from the corresponding measurement data sets and the degree of correlation between the two ionospheric parameters is established.

scholarly journals On the Prediction of Solar Cycles

Solar Physics ◽  
2021 ◽  
Vol 296 (1) ◽  
Author(s):  
V. Courtillot ◽  
F. Lopes ◽  
J. L. Le Mouël
Keyword(s):  
Solar Activity ◽  
Solar Cycle ◽  
Transfer Function ◽  
Singular Spectrum Analysis ◽  
Activity Cycle ◽  
Solar Activity Cycle ◽  
Planetary Motion ◽  
Data Sets ◽  
Solar Cycles ◽  
Jovian Planets

AbstractThis article deals with the prediction of the upcoming solar activity cycle, Solar Cycle 25. We propose that astronomical ephemeris, specifically taken from the catalogs of aphelia of the four Jovian planets, could be drivers of variations in solar activity, represented by the series of sunspot numbers (SSN) from 1749 to 2020. We use singular spectrum analysis (SSA) to associate components with similar periods in the ephemeris and SSN. We determine the transfer function between the two data sets. We improve the match in successive steps: first with Jupiter only, then with the four Jovian planets and finally including commensurable periods of pairs and pairs of pairs of the Jovian planets (following Mörth and Schlamminger in Planetary Motion, Sunspots and Climate, Solar-Terrestrial Influences on Weather and Climate, 193, 1979). The transfer function can be applied to the ephemeris to predict future cycles. We test this with success using the “hindcast prediction” of Solar Cycles 21 to 24, using only data preceding these cycles, and by analyzing separately two 130 and 140 year-long halves of the original series. We conclude with a prediction of Solar Cycle 25 that can be compared to a dozen predictions by other authors: the maximum would occur in 2026.2 (± 1 yr) and reach an amplitude of 97.6 (± 7.8), similar to that of Solar Cycle 24, therefore sketching a new “Modern minimum”, following the Dalton and Gleissberg minima.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

Recent and Planned Developments of the Program OxCal

Radiocarbon ◽  
2013 ◽  
Vol 55 (2) ◽  
pp. 720-730 ◽  
Author(s):  
Christopher Bronk Ramsey ◽  
Sharen Lee
Keyword(s):  
Statistical Analysis ◽  
Statistical Methods ◽  
Software Package ◽  
Model Averaging ◽  
Data Sets ◽  
Radiocarbon Dates ◽  
New Approach ◽  
New Models ◽  
Multiphase Models ◽  
Deposition Models

OxCal is a widely used software package for the calibration of radiocarbon dates and the statistical analysis of 14C and other chronological information. The program aims to make statistical methods easily available to researchers and students working in a range of different disciplines. This paper will look at the recent and planned developments of the package. The recent additions to the statistical methods are primarily aimed at providing more robust models, in particular through model averaging for deposition models and through different multiphase models. The paper will look at how these new models have been implemented and explore the implications for researchers who might benefit from their use. In addition, a new approach to the evaluation of marine reservoir offsets will be presented. As the quantity and complexity of chronological data increase, it is also important to have efficient methods for the visualization of such extensive data sets and methods for the presentation of spatial and geographical data embedded within planned future versions of OxCal will also be discussed.

Statistical analysis of coastal morphological data sets over seasonal to decadal time scales

2008 ◽  
Vol 55 (7-8) ◽  
pp. 581-600 ◽  
Author(s):  
Aart Kroon ◽  
Magnus Larson ◽  
Iris Möller ◽  
Hiromune Yokoki ◽  
Grzegorz Rozynski ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Time Scales ◽  
Morphological Data ◽  
Data Sets

Applied plant science experimental design and statistical analysis using the SAS® OnDemand for Academics

2021 ◽  
Keyword(s):  
Statistical Analysis ◽  
Agricultural Production ◽  
Science Research ◽  
Plant Science ◽  
Data Sets ◽  
Statistical Analysis System ◽  
Key Topics ◽  
Analysis System ◽  
User Friendly ◽  
Enormous Number

Abstract The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide. The enormous number of design and analysis options available for correctly implementing, analyzing and interpreting research can be overwhelming. Statistical Analysis System (SAS®) is the most widely used statistical software in the world and SAS® OnDemand for Academics is now freely available for academic insttutions. This is a user-friendly guide to statistics using SAS® OnDemand for Academics, ideal for facilitating the design and analysis of plant science experiments. It presents the most frequently used statistical methods in an easy-to-follow and non-intimidating fashion, and teaches the appropriate use of SAS® within the context of plant science research. This book contains 21 chapters that covers experimental designs and data analysis protocols; is presented as a how-to guide with many examples; includes freely downloadable data sets; and examines key topics such as ANOVA, mean separation, non-parametric analysis and linear regression.

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