Principal Component Analysis of computed emission lines from proto-stellar jets
A.H. Cerqueira, J. Reyes-Iturbide, F. De Colle, M.J. Vasconcelos

TL;DR
This paper demonstrates that Principal Component Analysis (PCA) applied to synthetic spectro-imaging data can help distinguish between rotation and precession effects in protostellar jets, aiding understanding of jet formation mechanisms.
Contribution
The study introduces a PCA-based diagnostic method to identify rotation signatures in protostellar jets using synthetic data, providing a benchmark for real observations.
Findings
PCA can differentiate rotation from precession effects in jet data.
Effective at inclinations up to 45 degrees for disentangling jet features.
Successfully recovered rotation profiles at low inclination angles (<15 degrees).
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…
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