Table · dataset · 2026
Wave and energy characterisation in the atmosphere of sunspots
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32634120.v1
Wave motions have been detected throughout the solar atmosphere for many decades, and their ability to transport energy through the atmosphere has sparked wide investigation into the role of waves in the heating of the upper solar atmosphere.
Description
Strong predominantly vertical magnetic fields within sunspots provide ideal conduits for waves generated by subphotospheric p-modes to propagate upwards through the photosphere and chromosphere, where their energy may be dissipated.
In the study of atmospheric wave signatures, observations are acquired at a range of atmospheric heights by utilising a variety of spectral lines. However, weak chromospheric absorption lines are often temperature sensitive, and readily capture shock fronts as optically thin emission. This presence of multiple spectral components within line profiles makes it challenging to determine accurate plasma Doppler (line-of-sight) velocities, resulting in the underutilisation of available data.
Read the rest (5 more)
As well as studying wave signatures within solar observations, numerical magnetohydrodynamic (MHD) simulations of waves propagating through a model solar atmosphere are regularly utilised in the development of wave heating theories. It is typically assumed that the solar atmosphere is a fully ionised plasma, however, owing to a reduced temperature in the lower solar atmosphere, the plasma is often only partially ionised.
Due to the decoupled neutral and charged components within the partially ionised solar atmosphere, processes such as ambipolar diffusion occur. By modifying the underlying physics of MHD simulations, the role of ambipolar diffusion in propagating wave characteristics can be studied within an idealised partially ionised photosphere and chromosphere.<br><br>In the first study, a novel method is presented that uses machine learning to detect the presence of multiple spectral components within observed spectral line profiles.
Each spectral component within the profile is subsequently constrained through single or multiple Voigt fits, which allows active and quiescent components to be isolated for further analysis. A proof of concept study is presented, which benchmarks the application of the method to a Ca ɪɪ 8542 Å spectral imaging dataset, to assess its applicability for observational datasets typical of sunspot chromospheres. Minimisation tests are performed between the observed and fitted line profiles to verify the reliability of the results.
Median reduced chi-squared values of 1.03 are achieved for the umbral line profiles.<br><br>In the second study, the Mᴀɴᴄʜᴀ3D numerical code is employed to investigate the role of ambipolar diffusion in magnetoacoustic waves propagating through the atmosphere immediately above the umbra of a sunspot. Simulations are performed both with and without ambipolar diffusion, where the non-ideal MHD equations are solved for data-driven perturbations to the magnetostatic equilibrium.
Energy spectral densities are analysed, and evidence is presented suggesting that, within weakly ionised low density regions where the ambipolar diffusion coefficient is large, ambipolar diffusion has a key role in determining wave characteristics. It is therefore proposed that, when simulating and observing the lower solar atmosphere the effect of ambipolar diffusion is important and should be carefully considered.
Links
Where it is published
- DOI doi.org/10.17034/32634120.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Astronomy & Astrophysics · Astronomy & Astrophysics · Astronomy & Astrophysics · Computer Science & AI · Computer Science & AI · Computer Science & AI · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Machine learning · Machine learning · Machine learning · Ocean & Atmospheric Science · Ocean & Atmospheric Science · Ocean & Atmospheric Science · Physics · Physics · Physics · Solar physics · Solar physics · Solar physics
- Inferred from text
- Imaging 75%
Provenance · 3 source records, 26 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32634120 | 5 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32634120 | 5 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32634120 | 5 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:510108 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['solar physics'] |
| concepts[field].anzsrc:field:510108 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['solar physics'] |
| concepts[field].anzsrc:field:510108 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['solar physics'] |
| concepts[field].anzsrc:group:4611 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].local:field:astronomy | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:astronomy | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:astronomy | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:physics | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:physics | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:physics | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[modality].local:modality:imaging | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |