Probability model for cumulative solar proton event fluences

Xapsos, M.A.; Summers, G.P.; Barth, J.L.; Stassinopoulos, E. G.; Burke, E.A.

doi:10.1109/23.856469

Cited by 114 publications

(52 citation statements)

References 15 publications

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“…Combined with the difficulty in reliably predicting the future sunspot number this technique is very difficult to justify. The ESP (Emission of Solar Protons) cumulative fluence model [ Xapsos et al , 2000] is based purely on the yearly fluence fitted with a lognormal distribution assuming that events are Poisson distributed and therefore the result for 1‐year fluence can be extrapolated to different mission lengths using simple formulae. Related models for worst case event flux [ Xapsos et al , 1998] and worst case event fluence [ Xapsos et al , 1999] also use a Poisson assumption to obtain their results.…”

Section: Introductionmentioning

confidence: 99%

Time distributions of solar energetic particle events: Are SEPEs really random?

Jiggens

Gabriel

2009

J. Geophys. Res.

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[1] Solar energetic particle events (SEPEs) can exhibit flux increases of several orders of magnitude over background levels and have always been considered to be random in nature in statistical models with no dependence of any one event on the occurrence of previous events. We examine whether this assumption of randomness in time is correct. Engineering modeling of SEPEs is important to enable reliable and efficient design of both Earth-orbiting and interplanetary spacecraft and future manned missions to Mars and the Moon. All existing engineering models assume that the frequency of SEPEs follows a Poisson process. We present analysis of the event waiting times using alternative distributions described by Lévy and time-dependent Poisson processes and compared these with the usual Poisson distribution. The results show significant deviation from a Poisson process and indicate that the underlying physical processes might be more closely related to a Lévy-type process, suggesting that there is some inherent ''memory'' in the system. Inherent Poisson assumptions of stationarity and event independence are investigated, and it appears that they do not hold and can be dependent upon the event definition used. SEPEs appear to have some memory indicating that events are not completely random with activity levels varying even during solar active periods and are characterized by clusters of events. This could have significant ramifications for engineering models of the SEP environment, and it is recommended that current statistical engineering models of the SEP environment should be modified to incorporate long-term event dependency and short-term system memory.

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Section: Introductionmentioning

confidence: 99%

Time distributions of solar energetic particle events: Are SEPEs really random?

Jiggens

Gabriel

2009

J. Geophys. Res.

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“…This can be obtained using standard on-line models and tools. We use the CREME96 model (Tylka et al 1997) for cosmic rays and the SPace ENVironment Information System (SPENVIS) together with the Emission of solar Protons (ESP) total-fluence model (Xapsos et al 1999(Xapsos et al , 2000 for solar protons. Spacecraft shielding stops a significant fraction of the lowerenergy particles (i.e., mostly the solar protons).…”

Section: Galactic Cosmic Rays and Solar Protonsmentioning

confidence: 99%

Detecting stars, galaxies, and asteroids withGaia

Bruijne

Allen

Azaz

et al. 2015

A&A

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Context. Gaia is Europe's space astrometry mission, aiming to make a three-dimensional map of 1000 million stars in our Milky Way to unravel its kinematical, dynamical, and chemical structure and evolution.Aims. We present a study of Gaia's detection capability of objects, in particular non-saturated stars, double stars, unresolved external galaxies, and asteroids. Gaia's on-board detection software autonomously discriminates stars from spurious objects like cosmic rays and solar protons. For this, parametrised criteria of the shape of the point spread function are used, which need to be calibrated and tuned. This study aims to provide an optimum set of parameters for these filters. Methods. We developed a validated emulation of the on-board detection software, which has 20 free, so-called rejection parameters which govern the boundaries between stars on the one hand and sharp (high-frequency) or extended (low-frequency) events on the other hand. We evaluate the detection and rejection performance of the algorithm using catalogues of simulated single stars, resolved and unresolved double stars, cosmic rays, solar protons, unresolved external galaxies, and asteroids. Results. We optimised the rejection parameters, improving -with respect to the functional baseline -the detection performance of single stars and of unresolved and resolved double stars, while, at the same time, improving the rejection performance of cosmic rays and of solar protons. The optimised rejection parameters also remove the artefact of the functional-baseline parameters that the reduction of the detection probability of stars as a function of magnitude already sets in before the nominal faint-end threshold at G = 20 mag. We find, as a result of the rectangular pixel size, that the minimum separation to resolve a close, equal-brightness double star is 0.23 arcsec in the along-scan and 0.70 arcsec in the across-scan direction, independent of the brightness of the primary. To resolve double stars with ∆G > 0 mag, larger separations are required. We find that, whereas the optimised rejection parameters have no significant impact on the detectability of pure de Vaucouleurs profiles, they do significantly improve the detection of pure exponential-disk profiles, and hence also the detection of unresolved external galaxies with intermediate profiles. We also find that the optimised rejection parameters provide detection gains for asteroids fainter than 20 mag and for fast-moving near-Earth objects fainter than 18 mag, although this gain comes at the expense of a modest detection-probability loss for bright, fast-moving near-Earth objects. The major side effect of the optimised parameters is that spurious ghosts in the wings of bright stars essentially pass unfiltered.

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“…The model employs satellite measurements for three complete solar cycles (20-22) with a truncated power-law distribution and the maximum entropy method (Xapsos et al 1999b(Xapsos et al , 2000. It is able to predict cumulative solar proton fluences and worst-case solar proton events as functions of mission duration and user confidence level.…”

Section: Sep Flux and Fluence Modelsmentioning

confidence: 99%

Dynamics of the Earth’s Particle Radiation Environment

et al. 2009

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The physical processes affecting the dynamics of the Earth's particle radiation environment are reviewed along with scientific and engineering models developed for its description. The emphasis is on models that are either operational engineering models or R. Vainio ( ) R. Vainio et al. models presently under development for this purpose. Three components of the radiation environment, i.e., galactic cosmic rays (GCRs), solar energetic particles (SEPs) and trapped radiation, are considered separately. In the case of SEP models, we make a distinction between statistical flux/fluence models and those aimed at forecasting events. Models of the effects of particle radiation on the atmosphere are also reviewed. Further, we summarize the main features of the models and discuss the main outstanding issues concerning the models and their possible use in operational space weather forecasting. We emphasize the need for continuing the development of physics-based models of the Earth's particle radiation environment, and their validation with observational data, until the models are ready to be used for nowcasting and/or forecasting the dynamics of the environment.

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Probability model for cumulative solar proton event fluences

Cited by 114 publications

References 15 publications

Time distributions of solar energetic particle events: Are SEPEs really random?

Time distributions of solar energetic particle events: Are SEPEs really random?

Detecting stars, galaxies, and asteroids withGaia

Dynamics of the Earth’s Particle Radiation Environment

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