The distribution of neutral hydrogen (HI) in the post-reionization universe traces the cosmic large-scale structure and therefore serves as a powerful cosmological probe. An efficient way to measure its distribution over wide sky areas and redshift ranges is through single-dish intensity mapping, which exploits the autocorrelation signal of each dish in a telescope array while scanning the same sky patch. Thanks to its broad frequency coverage and technical capabilities, SKA-Mid will enable measurements of the integrated 21 cm emission from HI up to redshift z∼3, making single-dish intensity mapping a key observable for probing dark matter and dark energy. Isolating the faint 21 cm cosmological signal without introducing biases is, however, challenging. The 21 cm signal is several orders of magnitude weaker than the astrophysical foregrounds, and its analysis is further affected by instrumental systematics. Overcoming these difficulties requires detailed modelling together with continuous improvements and innovations in data-analysis techniques. Over the past decade, the international community has developed and tested new methods to address current observational challenges and prepare for forthcoming SKA-Mid observations. This chapter reviews recent advances in map-making and component-separation techniques, with particular emphasis on telescope-specific systematics such as beam response and correlated noise. We focus on results obtained in controlled simulation environments, providing a valuable framework for assessing the strengths and limitations of different approaches. Developing robust algorithms capable of accurately handling instrumental effects and sky-model uncertainties is a crucial step toward fully exploiting the cosmological potential of HI intensity-mapping surveys in the SKA Observatory era.

The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is a "collaboration of collaborations" that is using the Canada-France-Hawai'i Telescope, the Pan-STARRS telescopes, and the Subaru Observatory to obtain $ugriz$ images of a core survey region of 6250 deg$^2$ of the northern sky. The $10\sigma$ point source depth of the data, as measured within a 2-arcsecond diameter aperture, are $[u,g,r,i,z] = [23.7, 24.5, 24.2, 23.8, 23.3]$\ in AB magnitudes. UNIONS is addressing some of the most fundamental questions in astronomy, including the properties of dark matter, the growth of structure in the Universe from the very smallest galaxies to large-scale structure, and the assembly of the Milky Way. It is set to become the major ground-based legacy survey for the northern hemisphere for the next decade and provides an essential northern complement to the static-sky science of the Vera C. Rubin Observatory's Legacy Survey of Space and Time. UNIONS supports the core science mission of the {\it Euclid} space mission by providing the data necessary in the northern hemisphere for the calibration of the wavelength dependence of the {\it Euclid} point-spread function and derivation of photometric redshifts in the North Galactic Cap. This region contains the highest quality sky for {\it Euclid}, with low backgrounds from the zodiacal light, stellar density, extinction, and emission from Galactic cirrus. Here, we describe the UNIONS survey components, science goals, data products, and the current status of the overall program.

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses.

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of antennas to look for the radio emission from extensive air showers that are triggered by the interaction of ultra-high-energy cosmic rays, gamma rays, and neutrinos in the atmosphere or underground. In particular, for ultra-high-energy neutrinos, the future final phase of GRAND aims to be sensitive enough to detect them in spite of their plausibly tiny flux. Three prototype GRAND radio arrays have been in operation since 2023: GRANDProto300, in China, GRAND@Auger, in Argentina, and GRAND@Nançay, in France. Their goals are to field-test the GRAND detection units, understand the radio background to which they are exposed, and develop tools for diagnostic, data gathering, and data analysis. This list of contributions to the 39th International Cosmic Ray Conference (ICRC 2025) presents an overview of GRAND, in its present and future incarnations, and a first look at data collected by GRANDProto300 and GRAND@Auger, including the first cosmic-ray candidates detected by them.

Radio detection is now an established technique for the study of ultra-high-energy (UHE) cosmic rays with energies above similar to 1017 eV. The next-generation of radio experiments aims to extend this technique to the observation of UHE earth-skimming neutrinos, which requires the detection of very inclined extensive air showers (EAS). In this article we present a new reconstruction method for the arrival direction and the energy of EAS. It combines a point-source-like description of the radio wavefront with a phenomenological model: the Angular Distribution Function (ADF). The ADF describes the angular distribution of the radio signal amplitude in the 50-200 MHz frequency range, with a particular focus on the Cherenkov angle, a crucial feature of the radio amplitude pattern. The method is applicable to showers with zenith angles larger than 60 degrees, and in principle up to neutrino-induced showers with up-going trajectories. It is tested here on a simulated data set of EAS induced by cosmic rays. A resolution better than 4 arc-minutes (0.07 degrees) is achieved on arrival direction, as well as an intrinsic resolution of 5% on the electromagnetic energy, and around 15% on the primary energy.

When performing radio detection of ultra-high energy astroparticles, the planar wavefront model is often used as a first step to evaluate the arrival direction of primary particles. This model estimates the direction by adjusting the wavefront orientation based on the peak timing of the signal traces from individual antennas. However, despite it is simplicity, the usefulness of this approach is limited by the lack of a good assessment of its robustness and the lack of confidence in its performance. To address these limitations, this study presents two analytical methods to solve for the planar wavefront arrival direction. In addition, we provide the corresponding analytical reconstruction uncertainty, offering a more detailed evaluation of the reconstruction's reliability.

The Giant Radio Array for Neutrino Detection (GRAND) is a proposed multi-messenger observatory of ultra-high-energy (UHE) particles of cosmic origin. Its main goal is to find the long-sought origin of UHE cosmic rays by detecting large numbers of them and the secondary particles created by their interaction – gamma rays, and, especially, neutrinos. GRAND will do so using large arrays of radio antennas that look for the radio signals emitted by the air showers initiated by the interactions of the UHE particles in the atmosphere. Since 2023, three small-scale prototype GRAND arrays have been in operation: GRAND@Nançay in France, GRAND@Auger in Argentina, and GRANDProto300 in China. Together, their goal is to validate the detection principle of GRAND under prolonged field conditions, achieving efficient, autonomous radio-detection of air showers. We describe the hardware, software, layout, and operation of the GRAND prototypes and show the first radio spectra measured by them. Despite challenges, the successful operation of the prototypes confirms that the GRAND instrumentation is apt to address the goals of the experiment and lays the groundwork for its ensuing stages.

Solar and stellar externally occulted coronagraphs share similar concepts, but are actually very different because of geometric characteristics. Solar occulters were first developed with a simple geometric model of diffraction perpendicular to the occulter edges. We apply this mere approach to starshades, and introduce a simple shifted circular integral of the occulter which allows to illustrate the influence of the number of petals on the extent of the deep central dark zone. We illustrate the reasons for the presence of an internal coronagraph in the solar case and its absence in the exoplanet case.

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments.

Context. We studied the imaging of exoplanetary systems using starshades, which are externally occulted coronagraphs in space. Aims. We provide a new method for precisely evaluating the stray light due to the star and a rapid calculation of the point spread functions in the presence of vignetting effects from the external occulter. Our study used shaped occulter configurations published in the literature, in particular, the SISTER NI2 and NW2 systems. Methods. The wavefront at the telescope aperture was computed using the classic Fresnel filtering method. The Fourier transform of the occulter was obtained with the highest possible precision using an approach initially developed for radio antennas, known as the polygonal shape factor. Results. We show that the Fresnel diffraction for a finite spatial field operates at very low frequencies only, and that it is sufficient to calculate the Fourier transforms there. Diffraction patterns computed numerically fully agree with theoretical predictions. The central parts of diffractions of petal and apodized occulters are identical over a large central area that increases in size with the number of petals. These diffraction patterns are used to compute the point spread functions. We computed the stray light for a non-point source star; this shows that starshades are not sensitive to star leakage, with a star diameter limit for a given configuration. We also computed signal-to-noise ratios for a perfect experiment limited by photon noise.

Images of distant galaxies are sheared due to gravitational lensing from intervening astrophysical structures. Shear can be estimated by measuring the shape of galaxies, however, such estimations are biased due to various non-cosmological artifacts introduced in the observation process. Traditional methods to mitigate such biases rely on using simulated images to estimate bias and then correcting the real observations with this bias. Such methods rely on the accuracy of the simulations, and are computationally expensive, particularly in the radio domain. As a first step towards a properly calibrated shear field estimator, we present a supervised deep-learning model based on equivariant convolutional neural networks (CNNs) that can recover ellipticities from reconstructed images of isolated radio galaxies. Our fiducial model recovers ellipticities from simulated galaxies with a measurement bias level of 10(-3) or better which is competitive with the ellipticity measurements from the state-of-the-art studies in the radio domain.

Context. The large aperture arrays for the currently under construction SKA Observatory (SKAO) will allow for observations of the universe in the radio spectrum at unprecedented resolution and sensitivity. However, these telescopes will produce data on the scale of exabytes, introducing a slew of hardware and software design challenges. Aims. This paper proposes a multi-step image reconstruction framework that allows for partitioning of visibility data by baseline length. This enables more flexible data distribution and parallelization, aiding in processing radio-astronomical observations within given constraints. Additionally, as each step of the framework only relies on a subset of the total visibilities, one can perform reconstruction progressively, with the initial step performed on the SKAO Science Data Processors and the second on local clusters. Methods. The multi-step reconstruction is separated into two steps. First a low-resolution image is reconstructed with only short-baseline visibilities, and then using this image together with the long-baseline visibilities, the full-resolution image is reconstructed. The proposed method only operates in the minor cycle, and it can be easily integrated into existing imaging pipelines. Results. We show that our proposed method allows for partitioning of visibilities by baseline without introducing significant additional drawbacks, reconstructing images of similar quality within similar numbers of major cycles compared to a single-step all-baselines approach that uses the same reconstruction method as well as compared to multi-scale CLEAN.

In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. Once extracted, a model is fit to the spectra to measure the observables, leading to an understanding of the underlying physics of the emission mechanism. One crucial, and often overlooked, aspect of this model is the background emission, which contains foreground and background astrophysical sources, intervening atmospheric emission, and artefacts related to the instrument such as noise. This paper proposes an algorithmic approach to constructing a background model for SITELLE observations using statistical tools and supervised machine learning algorithms. SITELLE is an imaging Fourier transform spectrometer located at the Canada-France-Hawaii Telescope, which produces a three-dimensional data cube containing the position of the emission (two dimensions) and the spectrum of the emission. SITELLE has a wide field of view (11 arcmin x 11 arcmin), which makes the background emission particularly challenging to model. We apply a segmentation algorithm implemented in photutils to divide the data cube into background and source spaxels. After applying a principal component analysis (PCA) on the background spaxels, we train an artificial neural network to interpolate from the background to the source spaxels in the PCA coefficient space, which allows us to generate a local background model over the entire data cube. We highlight the performance of this methodology by applying it to SITELLE observations obtained of a Star-formation, Ionized Gas and Nebular Abundances Legacy Survey galaxy, NGC 4449, and the Perseus galaxy cluster of galaxies, NGC 1275. We discuss the physical interpretation of the principal components and noise reduction in the resulting PCA-based reconstructions. Additionally, we compare the fit results using our new background modelling approach with standard methods used in the literature and find that our method better captures the emission from H ii regions in NGC 4449 and the faint emission regions in NGC 1275. These methods also demonstrate that the background does change as a function of the position of the data cube. While the approach is applied explicitly to SITELLE data in this study, we argue that it can be readily adapted to any integral field unit style data, enabling the user to obtain more robust measurements on the flux of the emission lines.

The reconstruction of very inclined air showers is a new challenge for next-generation radio experiments such as the AugerPrime radio upgrade, BEACON, and GRAND, which focus on the detection of ultra high energy particles. To tackle this, we study the electromagnetic particle content of very inclined air showers, which has scarcely been studied so far. Using the simulation tools CORSIKA and CoREAS, and analytical modeling, we explore the energy range of the particles that contribute most to the radio emission, quantify their lateral extent, and estimate the atmospheric depth at which the radio emission is strongest. We find that the spatial distribution of the electromagnetic component in very inclined air showers has characteristic features that could lead to clear signatures in the radio signal, and hence impact the reconstruction strategies of next-generation radio-detection experiments.

The design of starshades, i.e. external occulters for stellar coronography, relies on the fast and precise computation of their associated diffraction patterns of incoming plane waves in the telescope aperture plane. We present here a method based on a polygonal approximation of the occulter's shape, that allows fast computation of their diffraction patterns in the Fresnel approximation, without aliasing artefacts. It is competitive with respect to methods based on direct 2D Fourier transforms, or Boundary Diffraction Wave algorithms.

Large-scale cosmic-ray detectors like the Giant Radio Array for Neutrino Detection (GRAND) are pushing the boundaries of our ability to identify air shower events. Existing trigger schemes rely solely on the timing of signals detected by individual antennas, which brings many challenges in distinguishing true air shower signals from background. This work explores novel event-level radio trigger methods specifically designed for GRAND, but also applicable to other systems, such as the Radio Detector (RD) of the Pierre Auger Observatory. In addition to an upgraded plane wave front reconstruction technique, we introduce orthogonal and complementary approaches that analyze the radio-emission footprint, the spatial distribution of signal strength across triggered antennas, to refine event selection. We test our methods on mock data sets constructed with simulated showers and real background noise measured with the GRAND prototype, to assess the performance potential in terms of sensitivity and background rejection in GRAND. Our preliminary results are a first step to identifying the most discriminating radio signal features at event-level, and optimizing the techniques for future implementation on experimental data.

The Giant Radio Array for Neutrino Detection (GRAND) aims to detect highly inclined extensive air showers (EAS) with down-going and up-going trajectories. Several working groups in the GRAND collaboration are developing methods to reconstruct the incoming direction, core position, primary energy, and composition of the showers. The reconstruction pipeline – currently under development in the France/IAP working group – relies on a model of spherical wavefront emission for arrival times, which is possible because the radio signals are generated far away from the antenna stations. The amplitude distribution of the signals at the antenna level is described by an Angular Distribution Function that considers various asymmetries in the data, including geomagnetic effects. In this contribution, we present preliminary results from testing our EAS reconstruction procedure using realistic mock observations.

We present an analysis of new VLT/MUSE optical imaging spectroscopic data of 2MASX J23453268–0449256 (J2345–0449), a nearby ( z = 0.0755) massive ( M stellar = 4 × 10 11 M ⊙ ) spiral galaxy. This is a particularly interesting source for a study of active galactic nucleus (AGN) feedback since it hosts two pairs of bright, giant radio jets and a massive, luminous X-ray halo, but it has no massive bulge. The galaxy has a 24 kpc wide ring of molecular gas, and a source-averaged star formation rate that is factors 30 to 70 lower than expected from the Kennicutt-Schmidt law. With MUSE, we have analyzed the stellar continuum and bright optical line emission and have constrained the spatially resolved past and present star formation on scales of approximately 1 kpc. More than 93% of the stellar mass formed ≥10 Gyrs ago including in the disk. Optical emission from the AGN is very faint and contributes 2% of the continuum around the nucleus at most. Most line emission is predominantly excited by shocks and old stellar populations except in 13 young star-forming regions that formed ≤11 Myrs ago, of which only seven are associated with the molecular ring (the others are at larger radii). They avoid a region of high electron densities aligned with the radio source, and form stars at efficiencies that are comparable to those in normal spiral galaxies. We discuss the implications of our findings for the regulation of star formation in galaxies through AGN feedback in the absence of competing mechanisms related to the presence of a massive stellar bulge, such as morphological quenching.

Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study the applicability of supervised machine-learning techniques to systematically classify emission-line regions from the ratios of certain emission lines. Using the Million Mexican Model database, which contains information from grids of photoionization models using cloudy, and from shock models, we develop training and test sets of emission line fluxes for three key diagnostic ratios. The sets are created for three classifications: classic H ii regions, planetary nebulae, and supernova remnants. We train a neural network to classify a region as one of the three classes defined above given three key line ratios that are present both in the SITELLE and MUSE instruments' band-passes: [O III]lambda 5007/H beta, [N II]lambda 6583/H alpha, ([S II]lambda 6717+[SII]lambda 6731)/H alpha. We also tested the impact of the addition of the [O II]lambda 3726, 3729/[O III]lambda 5007 line ratio when available for the classification. A maximum luminosity limit is introduced to improve the classification of the planetary nebulae. Furthermore, the network is applied to SITELLE observations of a prominent field of M33. We discuss where the network succeeds and why it fails in certain cases. Our results provide a framework for the use of machine learning as a tool for the classification of extragalactic emission regions. Further work is needed to build more comprehensive training sets and adapt the method to additional observational constraints.

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of autonomous radio-detection units to look for the radio emission from extensive air showers that are triggered by the interaction of ultra-high-energy cosmic rays, gamma rays, and neutrinos in the atmosphere or underground. In particular, for ultra-high-energy neutrinos, the future final phase of GRAND aims to be sensitive enough to discover them in spite of their plausibly tiny flux. Presently, three prototype GRAND radio arrays are in operation: GRANDProto300, in China, GRAND@Auger, in Argentina, and GRAND@Nancay, in France. Their goals are to field-test the design of the radio-detection units, understand the radio background to which they are exposed, and develop tools for diagnostic, data gathering, and data analysis. This list of contributions to the 38th International Cosmic Ray Conference (ICRC 2023) presents an overview of GRAND, in its present and future incarnations, and a look at the first data collected by GRANDProto13, the first phase of GRANDProto300.