Principal component analysis for estimating parameters of the L1287 dense core by fitting model spectral maps into observed ones
L. E. Pirogov, P. M. Zemlyanukha

TL;DR
This paper presents a new algorithm combining principal component analysis and k-nearest neighbors to estimate physical parameters of a star-forming core by fitting spectral maps, providing insights into the core's contraction dynamics.
Contribution
The paper introduces an innovative method that integrates PCA and k-NN for parameter estimation from spectral maps, applied to the L1287 core, improving accuracy and understanding of star formation processes.
Findings
Estimated density profile with a power-law index of -1.7
Derived turbulent velocity profile with a power-law index of -0.4
Found contraction velocity profile with a power-law index of -0.1
Abstract
An algorithm has been developed for finding the global minimum of a multidimensional error function by fitting model spectral maps into observed ones. Principal component analysis is applied to reduce the dimensionality of the model and the coupling degree between the parameters, and to determine the region of the minimum. The k-nearest neighbors method is used to calculate the optimal parameter values. The algorithm is used to estimate the physical parameters of the contracting dense star-forming core of L1287. Maps in the HCO+(1-0), H13CO+(1-0), HCN(1-0), and H13CN(1-0) lines, calculated within a 1D microturbulent model, are fitted into the observed ones. Estimates are obtained for the physical parameters of the core, including the radial profiles of density (), turbulent velocity (), and contraction velocity (). Confidence…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
