As modern astronomy confronts unprecedented data volumes, automated pipelines and machine-learning techniques have become essential for processing and analysis. As these workflows grow more complex, astronomers also require input and inspection tools that can keep pace. To address challenges in navigating multidimensional datasets for quality control and scientific interpretation, we present the immersive Data Visualisation Interactive Explorer (iDaVIE), a virtual reality (VR) software suite developed in collaboration with the astronomy community. iDaVIE enables users to import and render large 3D data cubes within a VR environment, offering real-time tools for selection, cropping, catalogue overlays, and exporting results back into existing pipelines. Built on the Unity engine and SteamVR, the system uses custom plug-ins for efficient data parsing, downsampling, and statistical calculations. The software has already been integrated into workflows such as verifying HI data cubes from MeerKAT, ASKAP, and APERTIF, refining detection masks, and identifying new sources. Its intuitive interface aims to reduce the cognitive load associated with higher-dimensional data, allowing researchers to focus more directly on scientific goals. As an open-source, scalable, and adaptable platform, iDaVIE supports continued development and integration with other tools. Version 1.0 marks a significant milestone, with planned enhancements including subcube loading, advanced rendering modes, video-generation scripts, and collaborative capabilities. By pairing immersive visualisation with robust interaction tools, iDaVIE seeks to transform how researchers engage with complex datasets and enhance productivity in the era of big data.

A compact neutron spectrometer is described, which consists of four square prisms of plastic scintillator capable of pulse shape discrimination, eight silicon photomultipliers and a high density polyethylene moderator. The device is capable, in principle, of spectroscopic measurements up to around 60 MeV, although extendable to higher energies, and is also able to discern the direction of neutron radiation impinging on the detector. Results from simulations using Geant4, together with measurements with neutrons from an 241Am-9Be radioisotopic source, are used to characterise the detector in both spectroscopic and direction-sensitive modes. Field applications of the detector are discussed.

The IDIA Visualisation Laboratory based at the University of Cape Town is exploring the use of virtual reality technology to visualise and analyse astronomical data. The iDaVIE software suite currently under development reads from both volumetric data cubes and sparse multi-dimensional catalogs, rendering them in a room-scale immersive environment that allows the user to intuitively view, navigate around and interact with features in three dimensions. This paper will highlight how the software imports from common astronomy data formats and processes the information for loading into the Unity game engine. It will also describe what tools are currently available to the user and the various performance optimisations made for seamless use. Applications by astronomers will be reviewed in addition to the features we plan to include in future releases.

We present MeerKAT neutral hydrogen (H I) observations of the Fornax A group, which is likely falling into the Fornax cluster for the first time. Our H I image is sensitive to 1.4 × 1019 atoms cm−2 over 44.1 km s−1, where we detect H I in 10 galaxies and a total of (1.12 ± 0.02) × 109 M⊙ of H I in the intra-group medium (IGM). We search for signs of pre-processing in the 12 group galaxies with confirmed optical redshifts that reside within the sensitivity limit of our H I image. There are 9 galaxies that show evidence of pre-processing and we classify each galaxy into their respective pre-processing category, according to their H I morphology and gas (atomic and molecular) scaling relations. Galaxies that have not yet experienced pre-processing have extended H I discs and a high H I content with a H2-to-H I ratio that is an order of magnitude lower than the median for their stellar mass. Galaxies that are currently being pre-processed display H I tails, truncated H I discs with typical gas fractions, and H2-to-H I ratios. Galaxies in the advanced stages of pre-processing are the most H I deficient. If there is any H I, they have lost their outer H I disc and efficiently converted their H I to H2, resulting in H2-to-H I ratios that are an order of magnitude higher than the median for their stellar mass. The central, massive galaxy in our group (NGC 1316) underwent a 10:1 merger ∼2 Gyr ago and ejected 6.6−11.2 × 108 M⊙ of H I, which we detect as clouds and streams in the IGM, some of which form coherent structures up to ∼220 kpc in length. We also detect giant (∼100 kpc) ionised hydrogen (Hα) filaments in the IGM, likely from cool gas being removed (and subsequently ionised) from an in-falling satellite. The Hα filaments are situated within the hot halo of NGC 1316 and there are localised regions that contain H I. We speculate that the Hα and multiphase gas is supported by magnetic pressure (possibly assisted by the NGC 1316 AGN), such that the hot gas can condense and form H I that survives in the hot halo for cosmological timescales.

Scientists across all disciplines increasingly rely on machine learning algorithms to analyse and sort datasets of ever increasing volume and complexity. Although trends and outliers are easily extracted, careful and close inspection will still be necessary to explore and disentangle detailed behavior, as well as identify systematics and false positives. We must therefore incorporate new technologies to facilitate scientific analysis and exploration. Astrophysical data is inherently multi-parameter, with the spatial-kinematic dimensions at the core of observations and simulations. The arrival of mainstream virtual-reality (VR) headsets and increased GPU power, as well as the availability of versatile development tools for video games, has enabled scientists to deploy such technology to effectively interrogate and interact with complex data. In this paper we present development and results from custom-built interactive VR tools, called the iDaVIE suite, that are informed and driven by research on galaxy evolution, cosmic large-scale structure, galaxy-galaxy interactions, and gas/kinematics of nearby galaxies in survey and targeted observations. In the new era of Big Data ushered in by major facilities such as the SKA and LSST that render past analysis and refinement methods highly constrained, we believe that a paradigm shift to new software, technology and methods that exploit the power of visual perception, will play an increasingly important role in bridging the gap between statistical metrics and new discovery. We have released a beta version of the iDaVIE software system that is free and open to the community.

We present the beta release of iDaVIE-v, a new Virtual Reality software for data cube exploration. The beta release of iDaVIE-v (immersive Data Visualisation Interactive Explorer for volumetric rendering) is planned for release in early 2021. iDaVIE-v has been developed through the Unity game engine using the SteamVR plugin and is compatible with all commercial headsets. It allows the visualization, exploration and interaction of data for scientific analysis. Originally developed to serve the Hi Radio Astronomy community for Hi source identification, the software has now completed the alpha testing phase and is already showing capabilities that will serve the broader astronomical community and more. iDaVIE-v has been developed at the IDIA Visualisation Lab (IVL) based at the University of Cape Town in collaboration with the Italian National Institute for Astrophysics (INAF) in Catania.

In this paper, we describe an HDF5 schema created to support the efficient visualization of the large image cubes that will be produced by SKA Phase 1 and precursor radio telescopes. We demonstrate how the "HDF5-IDIA"schema's features can improve the performance of visualization software, using both low-level metrics and real-world tests of the schema's implementation in CARTA, an image viewer that is being developed to replace the existing CyberSKA and CASA viewers. (C) 2020 Elsevier B.V. All rights reserved.

CASA, the Common Astronomy Software Applications package, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio telescopes. In these proceedings of the 28th Astronomical Data Analysis Software & Systems (ADASS) conference, we give an overview of several new features in the CASA imaging task TCLEAN. This includes improved automated masking for image deconvolution, as well as parallel imaging options to increase imaging speeds. In addition, we highlight two upcoming developments. The first is the anticipated arrival of a first-look version of the Cube Analysis and Rendering Tool for Astronomy (CARTA), which is expected to eventually replace the CASA VIEWER. The other is a change in the way the different CASA components (e.g., tools and tasks) can be integrated within the Python environment, allowing much greater flexibility for users starting with CASA 6. We also summarize a list of CASA links to guide the user community to the latest CASA information and documentation.

Progress towards the realization of a new compact neutron spectrometer is described. The detector is based on EJ299-33 plastic scintillator coupled to silicon photomultipliers, and a digital implementation of pulse shape discrimination is used to separate events associated with neutrons from those associated with gamma rays. The spectrometer will be suitable over the neutron energy range 1–100 MeV, illustrated in this work with measurements made using an AmBe radioisotopic source and quasi-monoenergetic neutron beams produced using a cyclotron.

Three different digital implementations of pulse shape discrimination for pulses from an EJ301 liquid scintillator detector are presented, and illustrated with neutrons and gamma-rays produced by an Am–Be radioisotopic source, a D–T generator and beams produced by cyclotron-accelerated protons of energies 42, 62 and 100MeV on a Li target. A critical comparison between the three methods is provided.

A neutron spectrometer based on a single EJ299-33 plastic scintillator (5 cm diameter × 5 cm) is described. A digital implementation of pulse shape discrimination is used to separate events associated with neutrons from those associated with gamma-rays. Measurements made using a ns-pulsed neutron beam are used to produce response functions of the detector for neutrons over the energy range 10 - 100 MeV. These lineshapes are used to test the capability of the spectrometer to produce neutron energy spectra, via the unfolding of pulse height spectra, for use in neutron fields having any type of time structure. The results are compared with those from a similar spectrometer constructed from EJ301 liquid scintillator.

The pulse shape discrimination capbability of the newly available EJ299-33 plastic scintillator is compared with that of the widely-used EJ301 liquid scintillator, using a digital implementation of the charge comparison algorithm. Typical pulse shapes for neutrons and gamma-rays produced by an Am-Be radioisotropic source are fitted to an analytical model, in order to determine the time constants and relative weightings of the fast, medium and slow decay components of scintillation. These pulse shapes are compared to those generated by a Monte-Carlo simulation, utilising the Geant4 toolkit.