Recent Talks

1. “Desarrollo de una librería en C para la integración de PUS (Packet Utilization Standard) en nuevas misiones espaciales” por  Carlos Colodro Conde
2. “Arquitectura del software embedido de la nueva versión de cámaras espaciales de IACTEC (DRAGO-3)” por  David Rodríguez Muñoz
3. “PROCEDIR - Extracción automática de solicitudes de procesos selectivos” por Juan José Herrera Martín
4. “Desarrollo ABAP en SAP: cuando el estándar no es suficiente” por  Ricardo Díaz Campos
5. “Assessment of HMI integration strategies in multi-controller beckhoff systems” por  Irene Chen Cordero León
6. ”Diseño de un sistema de adquisición para los equipos de medida de presión del laboratorio LABIC” por Rafael Alcántara Linares
7. “AuriGLOBES: Auriga GLOBular clustErs cosmological zoom-in Simulations” por  Pablo Daniel Contreras Guerra
8. “Infraestructura y plataforma de desarollo para EST” por Josué Barrera Martín
9. “SAOS: Solar Adaptive Optics Simulator” por Nicolás Adrián Rodríguez Linares
10. “Experiencias y lecciones aprendidas en desarrollo de GUIs para instrumentación astrofísica” por José Marco de la Rosa
11. “Software de Control en Tiempo Real para Sistemas de Óptica Adaptativa: Integración de Espejos Deformables y Tip-Tilt” por  Kevin Martín Chinea y  Rafael Melgar Hernández
12. “Del Papel al Pipeline: Estrategias de DevSecOps con GitLab para Automatizar y Unificar el SDLC” por  Antonio Alejandro Matta Gómez
13. “Machine Learning-driven autonomous control for the Small Exo-Life Finder (SELF)” por  Natalia Arteaga Marrero
14. “Mapping of exoplanet surfaces with the ExoLife Finder (ELF)” por  Max Dobat
15. “IAc-Support: Construcción de un Sistema RAG Multilingüe y Privado para Soporte Técnico de telescopios usando Modelos LLM” por  Olga Zamora
16. “Arquitectura del Sistema de Control del EST” por Fernando Merlos García

Presentamos TelescoPyCo, una suite para el control de cámaras de todo el cielo y el control/robotización de telescopios. TelescoPyCo está escrita en Python y hace uso de las librerías de código abierto Indi para el control de cúpulas, cámaras, monturas, ruedas de filtros, etc. El software también es capaz de controlar placas Arudino/RPI para el encendido/apagado de instrumentación o sensores, así como para su lectura. TelescoPyCo contiene paquetes para programas de observación, reducción y calibración fotométrica, y visualización y manejo de base de datos e imágenes. Tiene un modo de ingeniería para control manual en caso de necesidad a través de una interfaz web, por lo que es accesible desde cualquier dispositivo.

In the past decades, studies of the Milky Way have entered a golden era of large-scale surveys. The Gaia satellite provides precise positions, proper motions, and parallaxes for billions of stars, while ground-based spectroscopic surveys, such as LAMOST, deliver radial velocities and metallicities for nearly ten million stars. In addition, medium- and narrow-band photometric surveys, as well as Gaia’s slitless spectroscopic surveys, provide atmospheric parameters for hundreds of millions of stars. This talk will first summarize our efforts in measuring stellar parameters from these surveys, and then present our work on understanding the assembly history of the Milky Way—particularly its early phase, when the proto-Galaxy formed—and its dark matter distribution, based on the measured parameters of a very large number of stars.

Massive stars play a vital role in shaping the cosmic matter cycle and driving galaxy evolution, chemically enriching their host galaxy through their powerful stellar winds. Understanding the physical processes behind these mass-loss events is key to producing accurate model predictions. Despite its importance, stellar atmosphere modelling poses several challenges. In this talk, we will explore these challenges accross a range of massive stellar types, from OB main-sequence stars to evolved red supergiants and Wolf-Rayets. With these insights, I will further discuss the consequences of mass outflows on stellar evolution and the surrounding environment.

Los sensores difractivos consisten en una máscara difractiva situada en el plano focal común de un sistema óptico 4-f. Tras este sistema se coloca una cámara que detecta múltiples versiones de la pupila de entrada, cada una con sus frecuencias espaciales modificadas, a partir de las cuales podemos extraer las pendientes del frente de onda. Este tipo de sensor es compacto, robusto y ofrece alta resolución. Su principal desventaja es la necesidad de una elevada intensidad luminosa, lo que limita su uso en óptica adaptativa, aunque no representa un problema en aplicaciones de comunicaciones ópticas. En esta presentación se introduce el fundamento de los sensores difractivos y se muestran algunos ejemplos. Además, se demuestra que la recuperación del frente de onda es sencilla y puede realizarse casi en tiempo real. Aunque se trata de una tecnología en desarrollo, presenta perspectivas muy prometedoras en comparación con otros sensores más consolidados.

Over the past decade, gamma-ray observations from space-based instruments such as Fermi-LAT and ground-based arrays including H.E.S.S., MAGIC, and VERITAS have provided an increasingly detailed view of supernova remnants (SNRs). Several dozen SNRs have now been detected across the GeV–TeV energy range, revealing a diverse population shaped by their surrounding environments and evolutionary stages. The catalog of gamma-ray bright remnants continues to expand with new discoveries. Observations by HAWC and LHAASO have even identified a few Galactic PeVatron candidates, although a direct link to individual SNRs remains under investigation.

Beyond isolated remnants, gamma-ray detections from novae, as well as from star-forming regions and stellar clusters, underscore the ubiquity of shock-powered emission in explosive and turbulent environments. In particular, the collective action of multiple supernovae and stellar winds within massive star-forming regions appears capable of sustaining efficient particle acceleration, potentially bridging the gap between classical SNR shocks and Galactic PeVatrons. This review highlights recent gamma-ray results that provide new insight into the radiative signatures, acceleration efficiency, and energetic processes associated with shocks in both isolated and collective astrophysical systems.

In this talk we present the status of the Gran Telescopio Canarias AO system first on-sky results, focusing on the lessons learned and the next steps to unleash its full potential. We also present the status of the Laser Guide Star System upgrade, and the first science camera for diffraction limited imaging, GRANCAIN. The installation of this kind of facilities in every major telescope prove that Adaptive Optics (AO) has reached the maturity of a well-established technique in use by a variety of instruments, from astronomy to optical communications, microscopy, vision, laser machining…. It is necessary to address the upcoming needs of the community, getting ahead doing pioneering research in AO. We present the Adaptive Optics Laboratory created at the Instituto de Astrofísica de Canarias (IAC) as part of the CELESTE (Cutting Edge Leap to Excellence in Space and Optics Technologies) initiative. The knowledge and experience acquired through the GTCAO project and the rest of the AO activities at IAC have naturally driven us to the creation of the AO group. CELESTE gives us the right frame to do so, channelling excellence and contributing to further develop this technique.

The remarkable success of recent cosmology in pinpointing a consistent concordance model relies on several foundational assumptions, including homogeneity, isotropy of the universe, and scale invariance, among others.  Increasingly precise cosmological observations, such as the exquisite measurements of the CMB sky from the ESA Planck space mission, have opened up the possibility of robust, independent tests of these assumptions. I will present key results from my research programs on this theme and review the current status of the research.

First in the lecture series on deep learning for astronomy. This series is designed to provide clear and accessible introductions to key deep learning techniques, with examples of their applications to astronomical data and research. In the first session we shall provide a brief history of deep learning and introduce the intuition and basic math behind NNs, together with examples related to data fitting with basic NN (slides and code provided in .https://github.com/cwestend/IACDEEP_introNN/)