Volume 626, June 2019
|Number of page(s)||19|
|Section||Interstellar and circumstellar matter|
|Published online||21 June 2019|
ROHSA: Regularized Optimization for Hyper-Spectral Analysis★
Application to phase separation of 21 cm data
AIM, CEA, CNRS, Université Paris-Saclay, Université Paris Diderot,
Sorbonne Paris Cité,
2 Institut d’Astrophysique Spatiale, CNRS UMR 8617, Université Paris-Sud 11, Batiment 121, 91405 Orsay, France
3 Laboratoire des Signaux et Systèmes (CNRS, CentraleSupélec, University of Paris-Sud), Université Paris-Saclay, 91192 Gif-sur-Yvette, France
4 LIP6, Université Pierre et Marie Curie-Paris 6, UMR7606, 4 Place Jussieu Paris cedex 05, 75252 Paris, France
5 Instituto de Radioastronomía y Astrofísica, Universidad Nacional Autónoma de México, 58089 Morelia, Mexico
Accepted: 29 April 2019
Context. Extracting the multiphase structure of the neutral interstellar medium is key to understanding star formation in galaxies. The radiative condensation of the diffuse warm neutral medium producing a thermally unstable lukewarm medium and a dense cold medium is closely related to the initial step leading the atomic-to-molecular (HI-to-H2) transition and the formation of molecular clouds. Up to now, the mapping of these phases out of 21 cm emission hyper-spectral cubes has remained elusive mostly due to the velocity blending of individual cold structures present on a given line of sight. As a result, most of the current knowledge about the HI phases rests on a small number of absorption measurements on lines of sight crossing radio sources.
Aims. The goal of this work is to develop a new algorithm to perform separation of diffuse sources in hyper-spectral data. Specifically the algorithm was designed in order to address the velocity blending problem by taking advantage of the spatial coherence of the individual sources. The main scientific driver of this effort was to extract the multiphase structure of the HI from 21 cm line emission only, providing a means to map each phase separately, but the algorithm developed here should be generic enough to extract diffuse structures in any hyper-spectral cube.
Methods. We developed a new Gaussian decomposition algorithm named ROHSA based on a multi-resolution process from coarse to fine grid. ROHSA uses a regularized nonlinear least-square criterion to take into account the spatial coherence of the emission and the multiphase nature of the gas simultaneously. In order to obtain a solution with spatially smooth parameters, the optimization is performed on the whole data cube at once. The performances of ROHSA were tested on a synthetic observation computed from numerical simulations of thermally bi-stable turbulence. We apply ROHSA to a 21 cm observation of a region of high Galactic latitude from the GHIGLS survey and present our findings.
Results. The evaluation of ROHSA on synthetic 21 cm observations shows that it is able to recover the multiphase nature of the HI. For each phase, the power spectra of the column density and centroid velocity are well recovered. More generally, this test reveals that a Gaussian decomposition of HI emission is able to recover physically meaningful information about the underlying three-dimensional fields (density, velocity, and temperature). The application on a real 21 cm observation of a field of high Galactic latitude produces a picture of the multiphase HI, with isolated, filamentary, and narrow (σ ~ 1−2 km s−1) structures, and broader (σ ~ 4−10 km s−1), diffuse, and space-filling components. The test-case field used here contains significant intermediate-velocity clouds that were well mapped out by the algorithm. As ROHSA is designed to extract spatially coherent components, it performs well at projecting out the noise.
Conclusions. In this paper we introduce ROHSA, a new algorithm that performs a separation of diffuse sources in hyper-spectral data on the basis of a Gaussian decomposition. The algorithm makes no assumption about the nature of the sources, except that each one has a similar line width. The tests we made shows that ROHSA is well suited to decomposing complex 21 cm line emission of regions of high Galactic latitude, but its design is general enough that it could be applied to any hyper-spectral data type for which a Gaussian model is relevant.
Key words: ISM: clouds / ISM: kinematics and dynamics / ISM: structure / methods: data analysis / methods: numerical / methods: observational
ROHSA is available in free access via the following web page: https://github.com/antoinemarchal/ROHSA
© A. Marchal et al. 2019
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.