arXiv subjects

Pau Grèbol-Tomàs

Publications and source records attributed to Pau Grèbol-Tomàs.

3 recordsLinked to original sources

arXiv2025

Assessing the metal and rare earth element mining potential of undifferentiated asteroids through the study of carbonaceous chondrites

Undifferentiated asteroids, particularly the parent bodies of carbon-rich chondrite groups, might be promising candidates for future space resource utilization due to their primitive composition and potential to host valuable metals and rare earth elements. However, our understanding of their bulk elemental composition remains limited, as most data are derived from reflectance spectra with low mineralogical resolution. Sample return missions have started to change that, as returned materials are already available to study. Still the available meteorites provide a valuable source of information about the diversity of undifferentiated asteroids in the interplanetary space. To improve compositional insights, we conducted Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and ICP-AES (Inductively coupled Plasma Atomic Emission Spectroscopy) analyses on a representative suite of carbonaceous chondrites. These meteorites, considered analogs of undifferentiated asteroids, preserve materials from the early solar system and provide a geochemical record of their parent bodies. Our results highlight the abundance and distribution of transition metals, siderophile elements, and rare earth elements across several chondrite groups. These findings support the view that C-type asteroids may serve as viable sources of critical materials, while also informing future mission planning, extraction strategies, and the development of new technologies for low-gravity resource operations.

astro-ph.EP↗

arXiv2024

The 18 May 2024 Iberian superbolide from a sunskirting orbit: USG space sensors and ground-based independent observations

On 18 May 2024, a superbolide traversed the western part of the Iberian Peninsula, culminating its flight over the Atlantic Ocean and generating significant media attention. This event was caused by a weak carbonaceous meteoroid of 1 m, entering the atmosphere at 40.4 km$\,$s$^{-1}$ with an average slope of 8.5$^\circ$. The luminous phase started at 133 km and ended at an altitude of 54 km. The meteoroid's heliocentric orbit had an inclination of 16.4$^\circ$, a high eccentricity of 0.952, a semi-major axis of 2.4 au, and a short perihelion distance of 0.12 au. The superbolide was recorded by multiple ground-based stations of the Spanish Meteor Network (SPMN) and the European Space Agency (ESA), as well as by the U.S. Government (USG) sensors from space. Due to the absence of observable deceleration, we successfully reconciled satellite radiometric data with a purely dynamic atmospheric flight model, constraining the meteoroid's mass and coherently fitting its velocity profile. Our analysis shows a good agreement with the radiant and velocity data reported by the Center for Near-Earth Object Studies (CNEOS), with a deviation of 0.56$^\circ$ and 0.1 km$\,$s$^{-1}$, respectively. The presence of detached fragments in the lower part of the luminous trajectory suggests that the meteoroid was a polymict carbonaceous chondrite, containing higher-strength macroscopic particles in its interior due to collisional gardening, or a thermally processed C-type asteroid. The orbital elements indicate that the most likely source is the Jupiter-Family Comet (JFC) region, aligning with the SOHO comet family, as its sunskirting orbit is decoupled from Jupiter. This event provides important information to characterize the disruption mechanism of near-Sun objects.

astro-ph.EP↗

arXiv2023

Deep machine learning for meteor monitoring: advances with transfer learning and gradient-weighted class activation mapping

In recent decades, the use of optical detection systems for meteor studies has increased dramatically, resulting in huge amounts of data being analyzed. Automated meteor detection tools are essential for studying the continuous meteoroid incoming flux, recovering fresh meteorites, and achieving a better understanding of our Solar System. Concerning meteor detection, distinguishing false positives between meteor and non-meteor images has traditionally been performed by hand, which is significantly time-consuming. To address this issue, we developed a fully automated pipeline that uses Convolutional Neural Networks (CNNs) to classify candidate meteor detections. Our new method is able to detect meteors even in images that contain static elements such as clouds, the Moon, and buildings. To accurately locate the meteor within each frame, we employ the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. This method facilitates the identification of the region of interest by multiplying the activations from the last convolutional layer with the average of the gradients across the feature map of that layer. By combining these findings with the activation map derived from the first convolutional layer, we effectively pinpoint the most probable pixel location of the meteor. We trained and evaluated our model on a large dataset collected by the Spanish Meteor Network (SPMN) and achieved a precision of 98\%. Our new methodology presented here has the potential to reduce the workload of meteor scientists and station operators and improve the accuracy of meteor tracking and classification.

astro-ph.EP↗