Astrobiology research in Finland

This page will present astrobiology oriented research projects in Finland.

Structure of bacteriophage PM2.

The origin of viruses
In the group of Professor Jaana Bamford we study the origin and evolution of viruses and the impact of viruses on the early evolution of life. The research group is part of the Finnish Centre of Excellence in Virus Research.

The very first viruses have emerged hand in hand with cellular life and thus understanding of the origin of viruses is closely related to the origin of life. What were the ecological and environmental conditions where viruses and cells arose? What was the influence of early viruses on the emergence and evolution of cells? How life might evolve in absence of viruses (on an alien planet for example)? To these and other questions we seek answers by studying the viruses of bacteria and archaea and by simulating the evolution of viruses and cells in vitro and in silico.
Group homepage


Radial velocity curve of HD 154345 with observations and their errorbars.

Enhanced exoplanet observations with astrometric snapshots
Detecting exoplanets is a tricky business. Out of approximately three hundred known exoplanets virtually all have been detected using the radial velocity method. However, the actual mass of the planet orbiting a distant star cannot be calculated – not even in theory – using only the radial velocity measurements because the orbital inclination of the planet with respect to the observer remains unknown. Hence, only the lower limit of the planetary mass is accessible.

To some extent, this problem will be solved by astrometry: measuring the position of the host star on the sky. To date, and despite serious trials, astrometric observations have not been accurate enough in being able to resolve the extremely small wobble of distant stars, caused by the gravitational interactions with a planetary companion. Fortunately, accurate space telescopes and other large telescopes with adaptive optics will be able to succeed in the near future. It has been commonly assumed that detecting an exoplanet using either of these methods, radial velocity or astrometry, requires measurements covering the whole orbital period of the planet. For instance, detecting a Jupiter analog orbiting a nearby star would require measurements covering a period of twelve years. In our research, we tested this assumption in detail.

In the project between the Departmant of Mathematics and Statistics at the University of Helsinki and Tuorla observatory at the University of Turku we applied probability theory to the problem of having two sources of measurements available. Our theoretical calculations show that when both radial velocity and astrometric observations are available, the orbital parameters and planetary mass could be accessible within one fourth of the orbital period. In the case of a Jupiter analog again, measurements made using both of these methods and covering a timeline of only three years would be sufficient for a detection. This is far less than the orbital period of twelve years.

We have also found out that with currently available radial velocity measurements, the observational timeline needed for astrometry is even shorter. In the case of HD 154345 b, the first known Jupiter analog approximately sixty lightyears away, the available radial velocity measurements cover a timeline of ten years. Our calculations show that using high precision astrometry, the orbital inclination, and hence the true mass of this planetary companion would be accessible within a single year. Therefore astrometric measurements need to cover just ten percent fraction of the total orbital period; saving tremendous amounts of telescope time and other resources.

Technically, the research has been made by generating simulated radial velocity and astrometric measurement sets whose observational timeline varied. These sets are analyzed using the Markov chain Monte Carlo technique which yields the full posterior probability densities for both orbital parameters and planetary mass. Further, using the probability theory in the form of Bayesian inference of two different sources of data it is possible to calculate parameter probability densities given both sources of measurements. Using this approach it is possible to extract more information from two or more measurements sets than would be possible when analyzing them separately. The first results will be published in Astronomy and Astrophysics.
Additional information will be given by M.Sc. Mikko Tuomi

This page was last modified by  Samuli Kotiranta  on  14 Jul 2009