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Sara Marques

Publications and source records attributed to Sara Marques.

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Parameter resolution of near-Earth asteroids using LISA

The detection of near-Earth asteroids (NEAs) is critical for planetary defense and for advancing our understanding of the small-body population in the Solar System. Traditional detection techniques, including optical and radar, are the cornerstone of NEA discoveries. However, these are limited by observational constraints. We investigate whether the Laser Interferometer Space Antenna (LISA), a future space-borne gravitational wave detector, might detect and constrain the parameters of NEAs through their gravitational effect on the spacecraft test masses during a close approach at the minimum orbital intersection distance. We modeled the gravitational perturbations of the test masses on the LISA spacecraft exerted by a passing NEA. Based on this, we computed its signal-to-noise ratio (S/N) to determine detectability using the LISA requirements. Furthermore, we used the Fisher information formalism to estimate the uncertainties on the state vector and mass of the asteroid given it is detected. These estimates provide a first-order assessment of the LISA capability to resolve NEA parameters under favorable encounter geometries. The Fisher information study showed that the fractional uncertainty on the asteroid mass scales as the inverse of the S/N. Consequently, a detection with an S/N $\geq 5$ yields a mass determination with an uncertainty of $\sim20\%$ at most. In contrast, the state vectors exhibit considerably larger uncertainties as they depend significantly on the geometry of the close approach. Nevertheless, for very high S/N cases, this precision may be comparable to the uncertainties obtained by some of the current observational methods. The analysis of the correlation matrices confirms that each close encounter produces a specific signal and provides a practical means to assign independent error bars to the recovered state vector.

astro-ph.EP

A transformer-based generative model for planetary systems

Numerical calculations of planetary system formation are very demanding in terms of computing power. These synthetic planetary systems can however provide access to correlations, as predicted in a given numerical framework, between the properties of planets in the same system. Such correlations can, in return, be used in order to guide and prioritize observational campaigns aiming at discovering some types of planets, as Earth-like planets. Our goal is to develop a generative model which is capable of capturing correlations and statistical relationships between planets in the same system. Such a model, trained on the Bern model, offers the possibility to generate large number of synthetic planetary systems with little computational cost, that can be used, for example, to guide observational campaigns. Our generative model is based on the transformer architecture which is well-known to efficiently capture correlations in sequences and is at the basis of all modern Large Language Models. To assess the validity of the generative model, we perform visual and statistical comparisons, as well as a machine learning driven tests. Finally, as a use case example, we consider the TOI-469 system, in which we aim at predicting the possible properties of planets c and d, based on the properties of planet b (the first that has been detected). We show using different comparison methods that the properties of systems generated by our model are very similar to the ones of the systems computed directly by the Bern model. We also show in the case of the TOI-469 system, that using the generative model allows to predict the properties of planets not yet observed, based on the properties of the already observed planet. We provide our model to the community on our website www.ai4exoplanets.com.

astro-ph.EP