Stellar activity is ubiquitous in late-type stars
with outer convection zones.
These stars constitute the
vast majority of stars in the Galaxy and, therefore,
also the most numerous type of
potential planet host
star. Our own Sun is one of them.
Late-type stars generate magnetic
fields through the interaction of rotational and
convective motion. These fields give rise to
a plethora of effects such as
starspots and flares. They also provide the bulk of the energy required to heat their
chromospheres and coronae, where temperatures reach
millions of degrees.
The hot plasma in the chromosphere and corona
radiates ultraviolet and X-ray
plays an important role in the
physics of protoplanetary disks and planetary
atmospheres. This high-energy radiation is only observationally available
instruments such as the Hubble Space Telescope or eROSITA onboard the Spectr-RG satellite.
The atmosphere of a planet comprises only a negligible fraction of its mass. In the case of the Earth, it is only one part in a million that forms the entire atmosphere, and, nonetheless, its importance for life on Earth is paramount.
It is the atmosphere that regulates energy and mass exchange between the planetary interior, the surface, and space. The greenhouse effect is a prominent example of its regulating power. Without it, the Earth would freeze over. With more of it, potentially catastrophic heating may ensue. All of this, without a change in insolation.
Active stars immerse the atmospheres of their planets in strong high-energy radiation fields. The deteriorating effect of this radiation may have been responsible for the loss of water on Venus, and
it is now thought that in some planetary systems the level of ionizing radiation is strong enough to drive planetary mass into space, eventually even leading to the complete erosion of entire planetary bodies.
Interpreting data with errors is essential in
science. How to do that, however, is an amazingly
contentious problem in itself. The frequentist and Bayesian school provide partly complementary approaches to such problems.
While the Bayesian approach was the first to be formalized particularly by P.-S. Laplace, frequentist methods dominated scientific practice in the 20th century. The 21st century witnesses another shift in the balance with Bayesian techniques gaining ground particularly in problems of estimation.
Understanding the link between empirical data, knowledge, and decision is critical in science. Deeper statistical insight and proper methods are crucial. The growing number of tasks involving gigantic data sets in astronomy and elsewhere is often only manageable employing machine learning techniques.