A Cosmic Quest for New Worlds. Characterising Exoplanet Signals via Radial Velocity and Transit Photometry.
Doctoral thesis, 2023
Since the first unambiguous detection of a planet around a Sun-like star, the
interest in the new and exciting field of exoplanets has grown immensely. New
and exciting developments are seen at a pace unparalleled for most subfields
of astronomy. In this thesis, I describe the two most successful techniques for
exoplanet detection and characterisation – transits and radial velocities – and
the challenges commonly encountered in extracting the planets from the data.
Transit photometry allows us to measure the planet radius, while radial
velocity measurements give us the planet’s minimum mass. These methods’
true strength, however, manifests in their combination as it allows us to estimate
the true mass, which, together with the radius, gives us the planet’s bulk
density. This is a powerful quantity, which allows us to construct models and
make predictions about the structure and composition of a planet’s interior,
as well as its atmosphere. Zeroing in on the latter two is currently one of the
biggest challenges for exoplanet characterisation.
I describe the process of detecting a planet in a stellar light curve, and
how transits and radial velocities are modelled together in order to determine
the planet parameters. This is then followed by the ideal theoretical
approach, which can be used to study a system in practice. However, the current
challenges in exoplanet characterisation surpass the ideal case, leading us
to explore more complex models. I then discuss the biggest nemesis to planet
discovery, particularly in radial velocity timeseries – stellar activity, and the
problem of its often stochastic manifestation. A special focus is given to one
method for its mitigation – modelling the radial velocities alongside activity
indicators. This is the core concept of multi-dimensional Gaussian process
regression, particularly with the quasi-periodic covariance function, which is
used in a large part of this work.
Finally, the last part if the thesis shows that while the ideal planet case can
sometimes be applicable for quiet stars, as is the case of the TOI-2196 system,
extending to non-parametric models, such as Gaussian processes, can help us
to detect planets in complicated datasets, as demonstrated by the cases of the
TOI-1260, TOI-733, TOI-776 and TOI-1416 systems.
interest in the new and exciting field of exoplanets has grown immensely. New
and exciting developments are seen at a pace unparalleled for most subfields
of astronomy. In this thesis, I describe the two most successful techniques for
exoplanet detection and characterisation – transits and radial velocities – and
the challenges commonly encountered in extracting the planets from the data.
Transit photometry allows us to measure the planet radius, while radial
velocity measurements give us the planet’s minimum mass. These methods’
true strength, however, manifests in their combination as it allows us to estimate
the true mass, which, together with the radius, gives us the planet’s bulk
density. This is a powerful quantity, which allows us to construct models and
make predictions about the structure and composition of a planet’s interior,
as well as its atmosphere. Zeroing in on the latter two is currently one of the
biggest challenges for exoplanet characterisation.
I describe the process of detecting a planet in a stellar light curve, and
how transits and radial velocities are modelled together in order to determine
the planet parameters. This is then followed by the ideal theoretical
approach, which can be used to study a system in practice. However, the current
challenges in exoplanet characterisation surpass the ideal case, leading us
to explore more complex models. I then discuss the biggest nemesis to planet
discovery, particularly in radial velocity timeseries – stellar activity, and the
problem of its often stochastic manifestation. A special focus is given to one
method for its mitigation – modelling the radial velocities alongside activity
indicators. This is the core concept of multi-dimensional Gaussian process
regression, particularly with the quasi-periodic covariance function, which is
used in a large part of this work.
Finally, the last part if the thesis shows that while the ideal planet case can
sometimes be applicable for quiet stars, as is the case of the TOI-2196 system,
extending to non-parametric models, such as Gaussian processes, can help us
to detect planets in complicated datasets, as demonstrated by the cases of the
TOI-1260, TOI-733, TOI-776 and TOI-1416 systems.
Exoplanet
transits
radial velocities
planetary systems
EA
Opponent: Charles Beichman, Executive Director of the NASA Exoplanet Science Institute (NExScI), USA
Author
Iskra Georgieva
Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics
We are now living in a fascinating era in which we know that
the stars in the sky are orbited by planets. In this thesis, I take
the reader on a cosmic quest for new worlds by introducing
the process of detection and characterisation of five exoplanet
systems. I describe the challenges we encounter along
the way and elaborate on ways to solve them, with a special
focus on the multi-dimensional Gaussian process approach
for modelling stellar activity in radial velocity timeseries.
the stars in the sky are orbited by planets. In this thesis, I take
the reader on a cosmic quest for new worlds by introducing
the process of detection and characterisation of five exoplanet
systems. I describe the challenges we encounter along
the way and elaborate on ways to solve them, with a special
focus on the multi-dimensional Gaussian process approach
for modelling stellar activity in radial velocity timeseries.
Subject Categories
Physical Sciences
Astronomy, Astrophysics and Cosmology
Fusion, Plasma and Space Physics
Roots
Basic sciences
ISBN
978-91-7905-874-6
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5340
Publisher
Chalmers
EA
Opponent: Charles Beichman, Executive Director of the NASA Exoplanet Science Institute (NExScI), USA