- Question: - kinematic motions within H II regions have not been systematically investigated in a large sample with a rather high angular resolution.
- Aims: - Invistigating the kinematic properties of Galactic H II regions using radio recombination line (RRL) emission
- new method: - ATCA and VLA observations. Pixel-by-pixel Gaussian fit to the data cubes.
- progress: - They discover velocity gradients in 178 (42%) of the nebulae. About 15% of the sources also have an RRL width spatial distribution that peaks toward the center of the nebula. They explained that the velocity gradients inherited angular momentum from their parent molecular clouds.
- comments:
- It is also argued that the bipolar outflow can create the velocity gradients. It would need more specific models to distinguish the outflow and rotation models.
- Question:
- The measurement of supermassive black holes in type II AGN is difficult because of the heavy extinction which makes commonly used optical Halpha lines useless.
- Aims:
- Investigate if the radio RRL can be detected toward the broad emission line region caused by the black holes.
- Progress:
- They detected narrow radio RRLs that should arise from star formation regions. However, they are unable to detect the broad RRLs in the nearest type II AGN galaxy.
- comments:
- One has taken good bandpass calibrator to make sure the bandpass is reliable. SKA/ngVLA and ALMA are only the players in this direction. but what if JWST with infrared RRLs?
- Question:
- TMRT Q band line survey leads to the detection of U lines which are not assigned before.
- Aims:
- figure out what the U lines are.
- Method:
- They match the U lines with the RRL of C and O which recombinate from CIII and OIII.
- Progress:
- They suggests that the U lines could be due to C and O with higher order of ionizations.
- Comments:
- This is a good approach. Quite a few U lines could be due to RRLs in high-mass star formation regions. If confirmed, this would indicate that such RRLs can be used to study the C/O abundances.
- Question:
- The ideal spectral averaging method depends on one's science goals and the available information about one's data. Including low-quality data in the average can decrease the signal-to-noise ratio (SNR), which may necessitate an optimization method or a consideration of different weighting schemes
- Aims:
- figure out which averaging method is best for improving the SNR.
- progress:
- weighting by the signal divided by the variance ("intensity-noise weighting"), weighting by the inverse of the variance ("noise weighting"), and uniform weighting. It appears that intensity-noise weighting method gives the best results. That is try to use SNR as the weight for the averaging.