ABSTRACT
The star [Formula: see text] Pictoris harbors a young planetary system of about 20 million years old, which is characterized by the presence of a gaseous and dusty debris disk, at least two massive planets and many minor bodies. For more than thirty years, exocomets transiting the star have been detected using spectroscopy, probing the gaseous part of the cometary comas and tails. The detection of the dusty component of the tails can be performed through photometric observations of the transits. Since 2018, the Transiting Exoplanet Survey Satellite has observed [Formula: see text] Pic for a total of 156 days. Here we report an analysis of the TESS photometric data set with the identification of a total of 30 transits of exocomets. Our statistical analysis shows that the number of transiting exocomet events (N) as a function of the absorption depth (AD) in the light curve follows a power law in the form [Formula: see text], where [Formula: see text]. This distribution of absorption depth leads to a differential comet size distribution proportional to [Formula: see text], where [Formula: see text], showing a striking similarity to the size distribution of comets in the Solar system and the distribution of a collisionally relaxed population ([Formula: see text]).
ABSTRACT
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteriesâa current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.
