Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-28T03:42:24.665Z Has data issue: false hasContentIssue false

Applying Historical Observations to Study Transient Phenomena

Published online by Cambridge University Press:  03 March 2020

Elizabeth Griffin*
Affiliation:
Herzberg Astronomy & Astrophysics Research Centre, Victoria, BC, Canada email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Astronomy has an enviable wealth of historical observations. Some verge on the archaeological, and display rare events such as novæ and supernovæ; others range up to 100 or more years in age, and bear unique information about events that will never repeat in detail. Yet most astronomers today know little of those resources and the scientific potential which they harbour, so rather infrequent use is made today of those historical data. The problem is that historical data were perforce obtained in analogue formats, and because of those formats the data too tend to be regarded as hailing from a culture whose scientific significance is passé. But the medium is not the message! Astronomy’s archives of photographic observations constitute an irreplaceable resource. The change in technology from analogue to electronic recording in the late 20th Century was abrupt, and it left most of today’s astronomers unable to handle and use photographic data, and led to a general skepticism of the value of photographic observations for present-day studies of variability in the cosmos. But that is precisely what older data can do; in particular, the older the data the more reliable the base-line against which one can measure new trends, refine orbital parameters, discern period modulations, etc.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Allen, C., Ruelas-Mayorga, A., Sánchez. L. J., & Costero, R. 2018, MNRAS, 481, 3953 Google Scholar
Griffin, R. E. M., 2005, PASP, 117, 885 CrossRefGoogle Scholar
Griffin, R. E. M., & Stencel, R. E. 2013, PASP, 125, 775 Google Scholar
Mickaelian, A. M., et al. 2016, in: A. M. Mickaelian, A. Lawrence & T. Yu. Magakian. (Eds.) Astronomical Surveys and Big Data, ASPCS, 505, 262 Google Scholar
Yu, Y., Zhao, J. H., Tang, Z.-H., & Shang, Z. J. 2017, RAA, 17, 28 Google Scholar