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dc.contributor.authorDe Keyser, J.
dc.contributor.authorCalders, S.
dc.contributor.authorLamy, H.
dc.contributor.authorVerbelen, F.
dc.contributor.authorKolenberg, K.
dc.date2026
dc.date.accessioned2026-02-17T12:34:18Z
dc.date.available2026-02-17T12:34:18Z
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/14598
dc.descriptionMeteor count rate time series collected during showers are a prime source of data for characterizing meteor streams. Doing so requires corrections for the sporadic meteor background, for the radiant positions in the sky, and for the specific properties of the observing equipment. This paper presents a suite of methods for analyzing meteor shower count rate time series using variants of the single- and double-exponential shower activity model. The approach relies on a least-squares fitting procedure. It is demonstrated that the basic version of the problem is often ill-posed. Constraining the fitting procedure by making assumptions about the sporadic background offers a partial remedy. To allow for a more robust solution, however, a generalization is developed that includes data from multiple observers. This makes the technique especially powerful when analyzing the data from forward scatter radio meteor networks. A Monte Carlo approach allows to establish confidence intervals on the results obtained. A selection of results illustrates the capabilities and limitations of these methods.
dc.languageeng
dc.titleDeriving meteor stream properties from meteor count rate time series in a multi-observer network
dc.typeArticle
dc.subject.frascatiPhysical sciences
dc.audienceScientific
dc.subject.freeMeteor stream
dc.subject.freeSporadic meteors
dc.subject.freeObservability function
dc.subject.freeForward scatter radio meteor echoes
dc.subject.freeBRAMS
dc.source.titlePlanetary and Space Science
dc.source.volume272
dc.source.pageA106246
Orfeo.peerreviewedYes
dc.identifier.doi10.1016/j.pss.2026.106246
dc.identifier.url


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