arXiv subjects

Facundo Perez Paolino

Publications and source records attributed to Facundo Perez Paolino.

2 recordsLinked to original sources

arXiv2026

Conspicuous Gas, Cryptic Dust: Spectroscopy and Chromaticity of Complex Periodic Variables

Complex periodic variables (CPVs) are young low-mass stars whose light curves show periodic dips indicative of transiting corotating material. The origin and composition of this material are unclear. Here we present new optical and near-infrared spectroscopy and photometry of four CPVs from Magellan, Keck, Hale, MuSCAT1, MuSCAT2, Tierras, KeplerCam, and TESS. The spectra imply that CPVs host magnetically bound circumstellar plasma clumps, on the basis of sinusoidal-in-time Balmer emission out of transit, and Balmer dimming during transit. Yet large night-to-night changes in circumstellar hydrogen emissivity occur without clear changes in light curve morphology, suggesting that the sharp flux dips are caused not by circumstellar plasma but by dust. Optical chromaticities (depth proportional to $λ^{-β}$, with $β$ = 0.79 $\pm$ 0.16) support this, but the power law breaks in the near-infrared, where a single power law under-predicts the depths observed at 2.1 microns. We therefore favor dips caused by dusty plasma clumps with opaque cores and optically thin halos, though we cannot rule out models in which the dust properties vary per-star or per-epoch. Observations at wavelengths greater than 2 microns and less than 0.4 microns would test this interpretation and clarify the dust's origin.

astro-ph.SR↗

arXiv2026

AstroAlertBench: Evaluating the Accuracy, Reasoning, and Honesty of Multimodal LLMs in Astronomical Classification

Modern astronomical observatories generate a massive volume of multimodal data, creating a critical bottleneck for expert human review. While multimodal large language models (LLMs) have shown promise in interpreting complex visual and textual inputs, their ability to perform specialized scientific classification while providing interpretable reasoning remains understudied. We introduce AstroAlertBench, a comprehensive multimodal benchmark designed to evaluate LLM performance in astronomical event review along a three-stage logical chain: metadata grounding, scientific reasoning, and hierarchical classification over five categories. We use a pilot sample of 1,500 real-world alerts from the Zwicky Transient Facility (ZTF), a wide-field survey that scans the northern sky to detect transient astronomical events. On this dataset, we benchmark 13 frontier closed-source and open-weight LLMs that support visual input. Our results reveal that high accuracy does not always align with model ``honesty,'' defined as the ability to self-evaluate its reasoning, which affects its reliability as a real-world assistant. We further initialize a human-in-the-loop evaluation protocol as a precursor to future community-scale participation. Together, AstroAlertBench provides a framework for developing calibrated and interpretable astronomical assistants.

astro-ph.IM↗