arXiv subjects

Guillem Megias Homar

Publications and source records attributed to Guillem Megias Homar.

7 recordsLinked to original sources

arXiv2026

Development of a Retrieval-Augmented Generation Virtual Assistant for Enhanced Information Discovery at Rubin Observatory

The NSF-DOE Vera C. Rubin Observatory will generate petabytes of data through the Legacy Survey of Space and Time (LSST) over the next decade, enabling discoveries across a broad range of astrophysical fields. Alongside these data products, Rubin maintains a large but heterogeneous collection of supporting documentation, including operational guides, technical notes, and scientific papers. Because this material is distributed across multiple platforms and formats, staff and scientists often struggle to efficiently locate accurate, up-to-date information. Many resources also reside on internal systems, limiting the ability of general-purpose language models to provide reliable answers to Rubin-specific questions. To address these challenges, we explore the use of Retrieval Augmented Generation (RAG) to improve information discovery. We present a prototype RAG-based virtual assistant that delivers context-aware, factual, conversational access to Rubin's vast and heterogenous documentation ecosystem. The system integrates material from multiple sources and enables semantic search through a conversational interface, using Weaviate for embeddings, LangChain for query orchestration, and an OpenAI GPT model as the LLM backend. By grounding responses in domain-specific knowledge, the assistant reduces hallucinations, improves accuracy, and demonstrates the potential of RAG to enhance access to distributed knowledge, streamline workflows, and support effective use of LSST data products.

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arXiv2026

Laser-based metrology systems vs wavefront sensing techniques: a comparative overview between the Large Binocular Telescope and the Vera C. Rubin Observatory for the telescope alignment and collimation tracking

This work presents a comparative overview of the collimation and alignment strategies employed by two leading 8m-class facilities: the Large Binocular Telescope (LBT) and the Vera C. Rubin Observatory. While both telescopes share a challenging fast f-number of approximately f/1.2 (considering the LBT in its Prime Focus configuration), they have adopted reciprocal architectures for the initial optical alignment strategy and for maintaining collimation during the night. As an initial alignment strategy, the LBT relies on a Wavefront Sensing technique called Focal Plane Image Analysis. Conversely, the Vera C. Rubin Observatory baseline foresees the usage of a Laser Tracker system to establish the initial optical states. The strategies for preserving the optical alignment and maintaining the collimation against gravitational flexure and thermal drift during observations are instead reversed. Besides the use of open-loop corrections based on Look-Up Tables, common on both telescopes, the LBT utilizes a laser-based Telescope Metrology System to monitor the relative position of optics in real-time, applying the corrections between the exposures. In contrast, the Rubin Observatory employs a Curvature Wavefront Sensing technique, using dedicated detectors at the four corners of the focal plane. Rather than identifying a best strategy, this work aims to synthesize the strengths, limitations, and operational trade-offs of these complementary approaches, from the perspective of the next generation of Extremely Large Telescopes and their instruments.

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arXiv2026

PSF-like Alpha-Particle Events in LSST Images

Rare $α$-particle-induced charge clusters appear in LSST images as compact, PSF-like sources with a median FWHM of $0.\!\!^{\prime\prime}95$ and median ellipticity consistent with zero, closely resembling unresolved astrophysical point sources. These events are detected in both dark and science exposures at a rate of approximately $10^{-12}\ \mathrm{pixel}^{-1}\ \mathrm{s}^{-1}$. Their collected charge and morphology are consistent with energy deposition from $\sim$5 MeV $α$-particles in silicon CCDs, and their spatial distribution across the focal plane suggests a localized material origin, plausibly associated with trace radioactive contamination in the cryostat aluminum. Despite their deceptive appearance, we demonstrate that a simple broadness statistic based on fourth-order moments cleanly separates these events from stellar PSFs, enabling efficient rejection in coadded images and real-time alert streams. Such charge clusters do not impose an intrinsic bright-end contamination floor for Rubin transient searches, as genuine fast astrophysical events would exhibit characteristically different morphological signatures.

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arXiv2025

Enabling Early Transient Discovery in LSST via Difference Imaging with DECam

We present SLIDE, a pipeline that enables transient discovery in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), using archival images from the Dark Energy Camera (DECam) as templates for difference imaging. We apply this pipeline to the recently released Data Preview 1 (DP1; the first public release of Rubin commissioning data) and search for transients in the resulting difference images. The image subtraction, photometry extraction, and transient detection are all performed on the Rubin Science Platform. We demonstrate that SLIDE effectively extracts clean photometry by circumventing poor or missing LSST templates. We identified 29 previously unreported transients, 12 of which would not have been detected based on the DP1 DiaObject catalog. SLIDE will be especially useful for transient analysis in the early years of LSST, when template coverage will be largely incomplete or when templates may be contaminated by transients present at the time of acquisition. We present multiband light curves for a sample of known transients, along with new transient candidates identified through our search. Finally, we discuss the prospects of applying this pipeline during the main LSST survey. Our pipeline is broadly applicable and will support studies of all transients with slowly evolving phases.

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arXiv2024

The Active Optics System on the Vera C. Rubin Observatory: Optimal Control of Degeneracy Among the Large Number of Degrees of Freedom

The Vera C. Rubin Observatory is a unique facility for survey astronomy that will soon be commissioned and begin operations. Crucial to many of its scientific goals is the achievement of sustained high image quality, limited only by the seeing at the site. This will be maintained through an Active Optics System (AOS) that controls optical element misalignments and corrects mirror figure error to minimize aberrations caused by both thermal and gravitational distortions. However, the large number of adjustment degrees of freedom available on the Rubin Observatory introduces a range of degeneracies, including many that are \textit{noise-induced} due to imperfect measurement of the wavefront errors. We present a structured methodology for identifying these degeneracies through an analysis of image noise level. We also present a novel scaling strategy based on Truncated Singular Value Decomposition (TSVD) that mitigates the degeneracy, and optimally distributes the adjustment over the available degrees of freedom. Our approach ensures the attainment of optimal image quality, while avoiding excursions around the noise-induced subspace of degeneracies, marking a significant improvement over the previous techniques adopted for Rubin, which were based on an Optimal Integral Controller (OIC). This new approach is likely to also yield significant benefits for all telescopes that incorporate large numbers of degrees of freedom of adjustment.

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arXiv2024

Using AI for Wavefront Estimation with the Rubin Observatory Active Optics System

The Vera C. Rubin Observatory will, over a period of 10 years, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an Active Optics System (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wavefront. We have designed and integrated a deep learning model for wavefront estimation into the AOS pipeline. In this paper, we compare the performance of this deep learning approach to Rubin's baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the deep learning approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images, and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the deep learning model is 40x faster, the median error 2x better under ideal conditions, 5x better in the presence of vignetting by the Rubin camera, and 14x better in the presence of blending in crowded fields. In addition, the deep learning model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.

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arXiv2023

Prompt Detection of Fast Optical Bursts with the Vera C. Rubin Observatory

The transient optical sky has remained largely unexplored on very short timescales. While there have been some experiments searching for optical transients from minutes to years, none have had the capability to distinguish millisecond Fast Optical Bursts (FOB). Such very fast transients could be the optical counterparts of Fast Radio Bursts (FRB), the prompt emission from $γ$-Ray Bursts (GRB), or other previously unknown phenomena. Here, we investigate a novel approach to the serendipitous detection of FOBs, which relies on searching for anomalous spatial images. In particular, due to their short duration, the seeing distorted images of FOBs should look characteristically different than those of steady sources in a standard optical exposure of finite duration. We apply this idea to simulated observations with the Vera C. Rubin Observatory, produced by tracing individual photons through a turbulent atmosphere, and down through the optics and camera of the Rubin telescope. We compare these simulated images to steady-source star simulations in 15 s integrations, the nominal Rubin exposure time. We report the classification accuracy results of a Neural Network classifier for distinguishing FOBs from steady sources. From this classifier, we derive constraints in duration-intensity parameter space for unambiguously identifying FOBs in Rubin observations. We conclude with estimates of the total number of detections of FOB counterparts to FRBs expected during the 10-year Rubin Legacy Survey of Space and Time (LSST).

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