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Abstract In order to better characterize the rich supernova remnant (SNR) population of M83 (NGC 5236), we have obtained high-resolution (∼85 km s−1) spectra of 119 of the SNRs and SNR candidates in M83 with Gemini/GMOS, as well as new spectra of the young SNRs B12-174a and SN 1957D. Most of the SNRs and SNR candidates have [Sii]:Hαratios that exceed 0.4. Combining these results with earlier studies we have carried out with MUSE and at lower spectroscopic resolution with GMOS, we have confirmed a total of 238 emission nebulae to be SNRs on the basis of their [Sii]:Hαratios, about half of which have emission lines that show velocity broadening greater than 100 km s−1, providing a kinematic confirmation that they are SNRs and not Hiiregions. Looking at the entire sample, we find a strong correlation between velocity widths and the line ratios of [Oi]λ6300:Hα, [Nii]λ6584:Hα, and [Sii]λλ6716, 6731:Hα. The density-sensitive [Sii]λ6716:λ6731 line ratio is strongly correlated with SNR diameter, but not with the velocity width. We discuss these results in the context of previously published shock models.
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Tunable High‐Resolution Synthetic Aperture Radar Imaging
Abstract We have recently introduced a modification of the multiple signal classification method for synthetic aperture radar. This method incorporates a user‐defined parameter,ϵ, that allows for tunable quantitative high‐resolution imaging. However, this method requires relatively large single‐to‐noise ratios (SNR) to work effectively. Here, we first identify the fundamental mechanism in that method that produces high‐resolution images. Then we introduce a modification to Kirchhoff Migration (KM) that uses the same mechanism to produce tunable, high‐resolution images. This modified KM method can be applied to low SNR measurements. We show simulation results that demonstrate the features of this method.
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- Award ID(s):
- 1840265
- PAR ID:
- 10443826
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Radio Science
- Volume:
- 57
- Issue:
- 11
- ISSN:
- 0048-6604
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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