A new machine-learning framework to generate star cluster models

openThe birthplaces of stars are complex places, where turbulent interstellar gas collapses and fragments into star-forming cores, giving rise to non-trivial substructure. While the formation process can be modelled with hydrodynamical simulations, these are quite expensive in terms of computational resources. Moreover, primordial star clusters that are still embedded in their parent gas cloud are hard to constrain observationally. In this context, most efforts aimed at simulating the dynamical evolution of star clusters assume simplified initial conditions, such as truncated Maxwellian models. We aim to improve on this state-of-the-art by introducing a set of tools to generate realistic initial conditions for star clusters by training an appropriate class of machine learning models on a limited set of hydrodynamical simulations. In particular, we will exploit a new approach based on Gaussian process (GP) models, which have the advantage of differentiability and of being more tractable, allowing for seamless inclusion in a downstream machine learning pipeline for e.g. inference purposes. The proposed learning framework is a two-step process including the model training and the sampling of new stellar clusters based on the inference results. We investigate different sampling approaches in order to find samplers that are able to generate realistic realizations.The birthplaces of stars are complex places, where turbulent interstellar gas collapses and fragments into star-forming cores, giving rise to non-trivial substructure. While the formation process can be modelled with hydrodynamical simulations, these are quite expensive in terms of computational resources. Moreover, primordial star clusters that are still embedded in their parent gas cloud are hard to constrain observationally. In this context, most efforts aimed at simulating the dynamical evolution of star clusters assume simplified initial conditions, such as truncated Maxwellian models. We aim to improve on this state-of-the-art by introducing a set of tools to generate realistic initial conditions for star clusters by training an appropriate class of machine learning models on a limited set of hydrodynamical simulations. In particular, we will exploit a new approach based on Gaussian process (GP) models, which have the advantage of differentiability and of being more tractable, allowing for seamless inclusion in a downstream machine learning pipeline for e.g. inference purposes. The proposed learning framework is a two-step process including the model training and the sampling of new stellar clusters based on the inference results. We investigate different sampling approaches in order to find samplers that are able to generate realistic realizations

Photometry of the Didymos System across the DART Impact Apparition

On 2022 September 26, the Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, the satellite of binary near-Earth asteroid (65803) Didymos. This demonstrated the efficacy of a kinetic impactor for planetary defense by changing the orbital period of Dimorphos by 33 minutes. Measuring the period change relied heavily on a coordinated campaign of lightcurve photometry designed to detect mutual events (occultations and eclipses) as a direct probe of the satellite’s orbital period. A total of 28 telescopes contributed 224 individual lightcurves during the impact apparition from 2022 July to 2023 February. We focus here on decomposable lightcurves, i.e., those from which mutual events could be extracted. We describe our process of lightcurve decomposition and use that to release the full data set for future analysis. We leverage these data to place constraints on the postimpact evolution of ejecta. The measured depths of mutual events relative to models showed that the ejecta became optically thin within the first ∼1 day after impact and then faded with a decay time of about 25 days. The bulk magnitude of the system showed that ejecta no longer contributed measurable brightness enhancement after about 20 days postimpact. This bulk photometric behavior was not well represented by an HG photometric model. An HG 1 G 2 model did fit the data well across a wide range of phase angles. Lastly, we note the presence of an ejecta tail through at least 2023 March. Its persistence implied ongoing escape of ejecta from the system many months after DART impact