HD 180642 (V1449 Aql) frequency tables (Degroote+, 2009)

Bibcode
2009yCat..35060111D
Degroote, P.; Briquet, M.; Catala, C.; Uytterhoeven, K.; Lefever, K.; Morel, T.; Aerts, C.; Carrier, F.; Auvergne, M.; Baglin, A.; Michel, E.
Bibliographical reference

VizieR On-line Data Catalog: J/A+A/506/111. Originally published in: 2009A&A...506..111D

Advertised on:
8
2009
Number of authors
11
IAC number of authors
0
Citations
0
Refereed citations
0
Description
The tables represent the results of the frequency analysis of various models: under assumption of stable, long-lasting and independent frequencies with constant amplitudes and phases, we arrive at the list in table1.dat. When assuming dependency in the form of combination frequencies, we arrive at the list presented in table2.dat. When allowing for time-dependent amplitudes and phases, we finally get tables3-10. (3 data files).
Type
Non-refereed

It may interest you

  • Non-refereed
    Tales of stellar & binary co-evolution, told by stellar oscillations
    Stellar binarity and stellar oscillations are mostly seen and studied as two separate phenomena. Sometimes, one process is even complicating the analysis of the other beyond the liking of one or the other research community. However, the complete picture painted by the physical constraints provided by the combination of both processes represents a
    Beck, Paul G.

    Advertised on:

    10
    2024
    Bibcode
    2024bss..confE..35B
    Citations
    0
  • Non-refereed
    Convection, rotation, and magnetic activity of solar-like stars from asteroseismology
    During the last decade, our understanding of stellar physics and evolution has undergone a tremendous revolution thanks to asteroseismology. Space missions such as CoRoT, Kepler, K2, and TESS have already been observing millions of stars providing high-precision photometric data. With these data, it is possible to study the convection of stars
    Mathur, Savita

    Advertised on:

    0
    2024
    Bibcode
    2024IAUS..365..195M
    Citations
    0
  • Non-refereed
    The Multimodal Universe: 100 TB of Machine Learning Ready Astronomical Data
    We present the Multimodal Universe, a new framework collating over 100 TB of multimodal astronomical data for its first release, spanning images, spectra, time series, tabular and hyper-spectral data. This unified collection enables a wide variety of machine learning (ML) applications and research across astronomical domains. The dataset brings
    Angeloudi, Eirini et al.

    Advertised on:

    12
    2024
    Bibcode
    2024RNAAS...8..301A
    Citations
    0