Merging a Line Profile Series Into an One-Dimensional Dataset, an Applied Technique for Nonradial Pulsation Mode Diagnosis

Astronomy and Astrophysics – Astrophysics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

3

Scientific paper

A new method of nonradial pulsation mode identification is developed. This method is based on Fourier analysis of time series line profile variations that have been merged into a one-dimensional equally spaced dataset. In principle, this method is identical to that of two-dimensional Fourier transform of line profile time series, but it is much more convenient to use for most of astronomers who have experience in period analysis of light curves. The features of both temporal frequency and Doppler spatial frequency can be accurately retrieved. This method provides an easy way to carry out mode identification from line profiles and minimizes the uncertainty of mode determination caused by random noise. Comments and assessment of related methods of mode identification are given.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Merging a Line Profile Series Into an One-Dimensional Dataset, an Applied Technique for Nonradial Pulsation Mode Diagnosis does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Merging a Line Profile Series Into an One-Dimensional Dataset, an Applied Technique for Nonradial Pulsation Mode Diagnosis, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Merging a Line Profile Series Into an One-Dimensional Dataset, an Applied Technique for Nonradial Pulsation Mode Diagnosis will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-1428903

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.