Recent Talks

Third 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.

Second 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.

 1) Deep imaging reveals hidden structures and a turbulent past in one of the largest spiral galaxies in the Universe 

Deep imaging reveals hidden structures and a turbulent past in one of the largest spiral galaxies in the Universe: In this work, we present deep, multi-band optical imaging of Malin 2, a prototypical giant low surface brightness (GLSB) galaxy, using the Two-Meter Twin Telescope (TTT) at the Teide Observatory. Our g- and r-band data reach depths of 30.3 and 29.5 mag arcsec⁻², allowing us to trace the stellar disk of Malin 2 out to ∼110 kpc for the first time. We detect new diffuse stellar features, including a northwestern overdensity overlapping with HI gas. We also report the discovery of a faint dwarf galaxy ∼130 kpc from Malin 2, possibly its first known ultra-diffuse satellite. A photometric analysis reveals significant azimuthal asymmetries in Malin 2. Compared to other spirals and GLSBs, Malin 2 stands out with its extremely extended disk. The spatial overlap of a disturbed stellar and gas content suggests Malin 2’s giant LSB disk is likely of tidal origin. Our results illustrate the importance of ultra-deep imaging in studying the structure and formation of these galaxies, where upcoming surveys like LSST will be crucial.

2) GRANCAIN, the first scientific Adaptive Optics camera at GTC.

GRANCAIN is the first scientific Adaptive Optics (AO) camera recently installed on the GTC. This instrument, located behind the GTCAO system, which has been in operation on the GTC since June 2023, will provide images at the diffraction limit of the GTC. GRANCAIN (11 mas/pix, 13" Æ FOV) will be able to obtain images with a resolution of ~40 mas in the K band, ~30 mas in the H band, and ~25 mas in the J band. This represents a resolution ten times better than under good seeing conditions and more than 1.5 times better than that of the JWST. In this talk we will review the design, assembly and integration of GRANCAIN in the IAC laboratory, as well as the integration and acceptance tests of the instrument on the GTC telescope. We will also present future prospects for the commissioning and scientific verification observations with GTCAO and GRANCAIN.

En esta charla se expondrá una nueva invención desarrollada en IACTEC-Espacio que permite la corrección precisa de imágenes obtenidas por una cámara en situaciones donde la temperatura no puede controlarse de manera precisa. Este método ha sido validado con éxito en tres misiones espaciales con cámaras SWIR ("Short Wave Infrared") no refrigeradas, mostrando gran precisión de corrección.

Constraining dust optical properties is essential for interpreting remote sensing observations of planetary atmospheres and surfaces, as well as for understanding the radiative impact of aerosols in climate and circulation models. In this talk, I will present recent advances in the retrieval and experimental characterization of dust optical properties across the ultraviolet, visible, and near-infrared wavelengths. Our approach combines laboratory measurements with advanced light-scattering modeling to determine wavelength dependent complex refractive indices and other key parameters such as single-scattering albedos, cross sections, and efficiencies. Three Martian dust analogues were analyzed, each prepared with narrow particle size distributions representative of airborne dust in the Martian atmosphere. Particular attention was given to the effects of particle shape, composition, and size on the derived optical properties. The resulting validated optical property database covers wavelengths from 200 to 2000 nm and provides a physically consistent foundation for radiative transfer modeling. These results offer improved constraints for interpreting spacecraft and ground based observations of planetary materials and contribute to the broader understanding of dust scattering behavior in planetary environments.

HAYDN is one of the ten mission concepts proposed to ESA’s M-class call (M8) after its Step-1 selection. It is designed to revolutionise our understanding of stellar structure and evolution through high-precision, space-based asteroseismology in dense stellar fields, including clusters. By performing continuous photometric monitoring of stars in these dense stellar fields, HAYDN aims to map stellar interiors across a wide range of ages, masses, and chemical environments—providing unprecedented constraints on stellar physics, Galactic evolution, exoplanet’s formation, and the formation history of the Milky Way, for naming some of the HAYDN’s science cases.

Our work focuses on high-accuracy spectral modeling in NLTE, and the determination of chemical abundances for the oldest known stars, providing crucial insights into the early universe and nucleosynthesis processes. Utilizing state-of-the-art spectroscopic techniques, we have analyzed high-resolution observations of the hyper metal-poor star J0815+4729 to derive its precise elemental abundances, including the elements carbon and oxygen. We discuss the implications of our findings on our understanding of the formation and evolution of the oldest stars. In addition, we are constructing a comprehensive NLTE synthetic spectral library that spans from 0.2 to 3 μm in wavelength, which will be made accessible to the public. This spectral library will make a significant impact to large spectroscopy surveys such as APOGEE, DESI, LAMOST, 4MOST, and others.

The rapid progress of quantum technologies—highlighted by recent Nobel Prizes recognizing advances in controlling quantum matter—is reshaping how we investigate the fundamental laws of nature. In this colloquium, I will introduce the concept of quantum machines: platforms such as quantum computers, analog simulators, and engineered networks of photons or ultracold atoms that process information according to the principles of quantum mechanics. These systems are emerging as powerful tools to emulate physical phenomena that were previously inaccessible to theory or experiment. By bridging quantum information science, tensor network methods, and high-energy physics, this work exemplifies how quantum machines are becoming laboratories for exploring exotic phases of matter, the structure of gauge fields, and the processes that shaped the early cosmos. Together, these advances mark the beginning of a new era in which we can engineer, manipulate, and ultimately understand some aspects of the quantum fabric of the universe.

Third lesson of the course "Master Class on Radio Astronomy And Data Visualization" organized by ExGal Twin Project at the IAC