arXiv subjects

M. -A. Carpine

Publications and source records attributed to M. -A. Carpine.

3 recordsLinked to original sources

arXiv2026

Pristine composition or size evolution: Can current dust models reproduce emissivities observed in nearby protostars?

Interstellar dust is a crucial asset in many astronomical observations. Characterising grains present in the dense gas and in star-forming environments is also key to constrain the pristine conditions for planetary formation. However, dust properties remain poorly characterised and are still debated: low dust emissivities observed in nearby protostars are not completely explained to this day. In this study, we aim to determine whether it is possible to retrieve the dust properties from multi-wavelength observations of the dust emission towards embedded protostars, and the extent to which current dust models can reproduce the observed values of the dust emissivity index in young protostars. We perform radiative transfer computations of the thermal dust emission from a model protostellar envelope, considering different dust optical properties commonly used in the community. This allows us to explore the effects of dust composition on the spectral index, to try and explain the variation in the emissivity index in nearby protostars observations. We find large variations in the spectral index as the sole result of different dust models, without the need for dust grain size evolution. However, our work does not allow us to reproduce the lowest emissivity index values found in some protostellar envelopes without including unexpectedly large millimetre-sized processed grains. We show that appropriate methods permits to measure the dust emissivity from observations of the spectral index at millimetre wavelengths with very little uncertainty. Variation in emissivity index between the different observed sources and the dust models most commonly used by the community implies that the intrinsic composition of dust is not sufficient to explain the lowest spectral index values. Thus, early dust evolution producing larger dust grains may have to be taken into account to obtain a complete picture.

astro-ph.GA↗

arXiv2025

From small dust to micron-sized aggregates: the influence of structure and composition on the dust optical properties

Models of astrophysical dust are key to understand several physical processes, from the role of dust grains as cooling agents in the ISM to their evolution in dense circumstellar disks, explaining the occurrence of planetary systems around many stars. Currently, most models aim at providing optical properties for dust grains in the diffuse ISM, and many do not account properly for complexity in composition and structure when dust is expected to evolve in dense astrophysical environments. Our purpose is to investigate, with a pilot sample of micron-size dust grains, the influence of dust structure, porosity, and composition when computing the optical properties of grown dust grains in the infrared and millimetre domains, where observations are widely used to constrain the dust properties. Starting from the small dust grains developed in the THEMIS 2.0 model, we use the Discrete Dipole Approximation to compute the optical properties of 1 um grains, varying the hypotheses made on their composition and structure. We look at the dust scattering, emission and extinction to isolate potential simplifications and unavoidable differences between grain structures. We note that both dust structure and porosity influence the dust properties in infrared and millimetre ranges, demonstrating that dust aggregates cannot be correctly approximated by compact or porous spheres. In particular, we show that the dust emissivity index in the millimetre can vary with fixed grain size. We shed light on the importance of taking the dust structure and porosity into account when interpreting observations in environments where dust grains may have evolved significantly. Efforts in carrying out physical models of grain growth, for instance, are required to establish realistic constraints on the structure of grown dust grains, and will be used in the future to build realistic dust models for the dense ISM.

astro-ph.GA↗

arXiv2024

Panopticon: a novel deep learning model to detect single transit events with no prior data filtering in PLATO light curves

To prepare for the analyses of the future PLATO light curves, we develop a deep learning model, Panopticon, to detect transits in high precision photometric light curves. Since PLATO's main objective is the detection of temperate Earth-size planets around solar-type stars, the code is designed to detect individual transit events. The filtering step, required by conventional detection methods, can affect the transit, which could be an issue for long and shallow transits. To protect transit shape and depth, the code is also designed to work on unfiltered light curves. We trained the model on a set of simulated PLATO light curves in which we injected, at pixel level, either planetary, eclipsing binary, or background eclipsing binary signals. We also include a variety of noises in our data, such as granulation, stellar spots or cosmic rays. The approach is able to recover 90% of our test population, including more than 25% of the Earth-analogs, even in the unfiltered light curves. The model also recovers the transits irrespective of the orbital period, and is able to retrieve transits on a unique event basis. These figures are obtained when accepting a false alarm rate of 1%. When keeping the false alarm rate low (<0.01%), it is still able to recover more than 85% of the transit signals. Any transit deeper than 180ppm is essentially guaranteed to be recovered. This method is able to recover transits on a unique event basis, and does so with a low false alarm rate. Thanks to light curves being one-dimensional, model training is fast, on the order of a few hours per model. This speed in training and inference, coupled to the recovery effectiveness and precision of the model make it an ideal tool to complement, or be used ahead of, classical approaches.

astro-ph.EP↗