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• W3103026990 abstract "A very important issue concerning protostellar jets is the mechanism behind their formation. Obtaining information on the region at the base of a jet can shed light into the subject and some years ago this has been done through a search for a rotational signature at the jet line spectrum. The existence of such signatures, however, remains controversial. In order to contribute to the clarification of this issue, in this paper we show that the Principal Component Analysis (PCA) can potentially help to distinguish between rotation and precession effects in protostellar jet images. We apply the PCA to synthetic spectro-imaging datacubes generated as an output of numerical simulations of protostellar jets. In this way we generate a benchmark to which a PCA diagnostics of real observations can be confronted. Using the computed emission line profiles for [O I]6300A and [S II]6716A, we recover and analyze the effects of rotation and precession in tomograms generated by PCA. We show that different combinations of the eigenvectors can be used to enhance and to identify the rotation features present in the data. Our results indicate that the PCA can be useful for disentangling rotation from precession in jets with an inclination of the jet with respect to the plane of the sky as high as 45 degrees. We have been able to recover the initially imposed rotation jet profile for models at moderate inclination angle (< 15 degrees) and without precession (abridged)." @default.
• W3103026990 created "2020-11-23" @default.
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• W3103026990 date "2015-07-15" @default.
• W3103026990 modified "2023-10-14" @default.
• W3103026990 title "PRINCIPAL COMPONENT ANALYSIS OF COMPUTED EMISSION LINES FROM PROTOSTELLAR JETS" @default.
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• W3103026990 doi "https://doi.org/10.1088/0004-6256/150/2/45" @default.
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