Astronomical instrumentation

Abstract:

Integral field spectroscopy (IFS) of the central kiloparsecs of active galactic nuclei (AGN) reveals a mixture of spatially coincident emission from star-formation and AGN feedback exciting the interstellar medium. Disentangling these components remains a challenge, as most spectral tracers are affected by both processes, limiting robust interpretation of kinematics and energetics. We present a new framework for decomposing IFS data into distinct components on a spaxel-by-spaxel basis using a multi-tracer, stacked kinematics approach. This method combines kinematic modelling with imposed flux decomposition per spaxel, quantifying the contribution of each component across the field of view. We apply this method to JWST IFS observations of the Seyfert galaxy NGC 5728 from the Galaxy Activity, Torus, and Outflow Survey (GATOS), analysing twelve mid-infrared fine-structure lines (4.49 ≤ λ ≤ 25.89μm, 7.9 ≤ IP ≤126.2 eV). We find that the circumnuclear emission can be decomposed into two dominant components: a star-forming ring and an AGN-driven biconical outflow. Our method separates these structures and recovers their detailed spatial morphology. This framework provides a general and scalable method for physically motivated component separation in IFS data, applicable across many wavelength ranges and targets, enabling reliable interpretation of complex emission line structures (or morphologies) in active galaxies and beyond.

Abstract:

<p> MOSAIC is the Multi-Object Spectrograph currently under development for the ESO Extremely Large Telescope (ELT). The instrument will provide highly multiplexed spectroscopic capabilities over a wide field of view, combining visible and near-infrared observations with both low- and high-resolution modes. The Near-Infrared (NIR) channel is one of the major subsystems of MOSAIC and is designed to support both Multi-Object Spectroscopy (MOS) and multi-Integral Field Unit (mIFU) observations over the wavelength range from 0.95 &mu;m to 1.8 &mu;m. </p> <p> The NIR channel combines complex fiber-fed architecture, cryogenic operation, high multiplexing capability and demanding spectral performance requirements. Light collected at the ELT focal plane is transported through dedicated MOS and mIFU fiber systems towards two identical cryogenic spectrographs located on the Nasmyth platform. Each spectrograph provides low-resolution, LR, (R~4,000) observations across the full NIR J and H bands wavelength range and high-resolution, HR, (R~18,000) observations in a selected H-band spectral window (1.52 – 1.62 &mu;m). </p> <p> This paper presents an overview of the NIR channel architecture, including the fiber systems, spectrographs and detectors. We summarize the main technical challenges associated with the NIR channel architecture: for the fiber links are the high multiplexing, the light injection and the routing of 2604 fibers from the ELT focal plane to the pseudo-slits at the spectrographs inside the cryostat. On the spectrograph side, the challenging elements are the optical design to accommodate both low- and high- resolution modes in a limited envelope and tight mass assignment, an extremely fast camera, the large optical components, the LR/HR mechanism, and the thermal stability of the cryostat and the cold bench. </p> <p> The current development status towards a System PDR in 2027 and an instrument-PDR with ESO in February 2028. </p> <p> The MOSAIC NIR channel is being developed by a team of seven different Research institutes and universities. This paper introduces that team and the description of the work packages developed by each of the involved MOSAIC partners. The major Management challenge is the coordination of the common design of the NIR channel where each partner is funding their own contribution (also in hardware) and the preparation of the subsystem, System and Instrument Assembly, Integration and Tests at the different sites in Europe and the verification at the ELT in Chile. </p>

Abstract:

Abstract Strong gravitational lenses (SGLs) are rare systems whose discovery currently relies primarily on supervised machine learning methods trained on large simulated datasets. We present the first application of Astronomaly:PROTEGE to SGL discovery, demonstrating that a human-in-the-loop active learning framework can efficiently identify lenses in large imaging surveys without the need for simulated training data. We consider a sample of 3.7 million bright galaxies from the Kilo-Degree Survey (KiDS) DR4. Feature representations are extracted using a convolutional neural network pre-trained on the ImageNet dataset and subsequently fine-tuned on KiDS data using the self-supervised Bootstrap Your Own Latent (BYOL) framework. Within the embedding of these representations, the active learning loop of Astronomaly iteratively selects the most informative systems for expert inspection. A total of 3,000 objects are inspected across multiple rounds, yielding 34 high-quality (grade A/B) SGL candidates. On the basis that these systems occupy similar regions in the learned feature space, we expand this sample through nearest-neighbour similarity analysis. Including the active learning discoveries, we identify a total of 140 grade A/B candidates and more than 1,000 additional lower-confidence systems (grade C). Among the A/B candidates, 81 are newly identified, while ∼22% of previously known grade A/B KiDS lenses are recovered. These results demonstrate strong potential for next-generation surveys such as Euclid, Roman, and Rubin’s Legacy Survey of Space and Time. With approximately 60% of the high-quality candidates newly reported, this approach complements supervised methods by reducing reliance on simulations and enabling the discovery of a diverse population of SGLs.