Radioactive nuclei from cosmochronology to habitability

In addition to long-lived radioactive nuclei like U and Th isotopes, which have been used to measure the age of the Galaxy, also radioactive nuclei with half-lives between 0.1 and 100 million years (short-lived radionuclides, SLRs) were present in the early Solar System (ESS), as indicated by high-precision meteoritic analysis. We review the most recent meteoritic data and describe the nuclear reaction processes responsible for the creation of SLRs in different types of stars and supernovae. We show how the evolution of radionuclide abundances in the Milky Way Galaxy can be calculated based on their stellar production. By comparing predictions for the evolution of galactic abundances to the meteoritic data we can build up a time line for the nucleosynthetic events that predated the birth of the Sun, and investigate the lifetime of the stellar nursery where the Sun was born. We then review the scenarios for the circumstances and the environment of the birth of the Sun within such a stellar nursery that have been invoked to explain the abundances in the ESS of the SLRs with the shortest lives - of the order of million years or less. Finally, we describe how the heat generated by radioactive decay and in particular by the abundant 26Al in the ESS had important consequences for the thermo-mechanical and chemical evolution of planetesimals, and discuss possible implications on the habitability of terrestrial-like planets. We conclude with a set of open questions and future directions related to our understanding of the nucleosynthetic processes responsible for the production of SLRs in stars, their evolution in the Galaxy, the birth of the Sun, and the connection with the habitability of extra-solar planets.Comment: Review published in Progress in Particle and Nuclear Physics. The article is being published Open Access, access to the full article is not restricted in any way. Please download the final version of the paper at https://doi.org/10.1016/j.ppnp.2018.05.00

Infrared spectral analys is and paleo-environment reconstruction on Mars

Connecting surface imaging and topography with IR spectral mineral identification provides better possibility for paleo-environment reconstruction. A project to compile a database of such indicators is started, the system’s background is outlined

Possibility for albedo estimation of exomoons: Why should we care about M dwarfs?

Occultation light curves of exomoons may give information on their albedo and hence indicate the presence of ice cover on the surface. Icy moons might have subsurface oceans thus these may potentially be habitable. The objective of our paper is to determine whether next generation telescopes will be capable of albedo estimations for icy exomoons using their occultation light curves. The success of the measurements depends on the depth of the moon's occultation in the light curve and on the sensitivity of the used instruments. We applied simple calculations for different stellar masses in the V and J photometric bands, and compared the flux drop caused by the moon's occultation and the estimated photon noise of next generation missions with 5 $\sigma$ confidence. We found that albedo estimation by this method is not feasible for moons of solar-like stars, but small M dwarfs are better candidates for such measurements. Our calculations in the J photometric band show that E-ELT MICADO's photon noise is just about 4 ppm greater than the flux difference caused by a 2 Earth-radii icy satellite in a circular orbit at the snowline of an 0.1 stellar mass star. However, considering only photon noise underestimates the real expected noise, because other noise sources, such as CCD read-out and dark signal become significant in the near infrared measurements. Hence we conclude that occultation measurements with next generation missions are far too challenging, even in the case of large, icy moons at the snowline of small M dwarfs. We also discuss the role of the parameters that were neglected in the calculations, e.g. inclination, eccentricity, orbiting direction of the moon. We predict that the first albedo estimations of exomoons will probably be made for large icy moons around the snowline of M4 -- M9 type main sequence stars.Comment: 13 pages, 6 figures, accepted for publication in A&

Analysing high resolution digital Mars images using machine learning

The search for ephemeral liquid water on Mars is an ongoing activity. After the recession of the seasonal polar ice cap on Mars, small water ice patches may be left behind in shady places due to the low thermal conductivity of the Martian surface and atmosphere. During late spring and early summer, these patches may be exposed to direct sunlight and warm up rapidly enough for the liquid phase to emerge. To see the spatial and temporal occurrence of such ice patches, optical images should be searched for and checked. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the High Resolution Imaging Science Experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter space mission. Out of these, 37 images were identified with smaller ice patches, which were distinguishable by their brightness, colour and strong connection to local topographic shading. In this study, a convolutional neural network (CNN) is applied to find further images with potential water ice patches in the latitude band between -40{\deg} and -60{\deg}, where the seasonal retreat of the polar ice cap happens. Previously analysed HiRISE images were used to train the model, where each image was split into hundreds of pieces (chunks), expanding the training dataset to 6240 images. A test run conducted on 38 new HiRISE images indicates that the program can generally recognise small bright patches, however further training might be needed for more precise identification. This further training has been conducted now, incorporating the results of the previous test run. To retrain the model, 18646 chunks were analysed and 48 additional epochs were ran. In the end the model produced a 94% accuracy in recognising ice, 58% of these images showed small enough ice patches on them. The rest of the images was covered by too much ice or showed CO2 ice sublimation in some places