Galaxy cluster X-ray cavities are inflated by relativistic jets that are ejected into the intracluster medium by active galactic nuclei (AGN). AGN jets prevent predicted cooling flow establishment at the cluster centre, and while this process is not well understood in existing studies, simulations have shown that the heating mechanism will depend on the type of gas that fills the cavities. Thermal and non-thermal distributions of electrons will produce different cavity Sunyaev Zel’dovich (SZ) effect signals, quantified by the ‘suppression factor’ f. This paper explores potential enhancements to prior constraints on the cavity gas type by simulating suppression factor observations with the Square Kilometre Array (SKA). Cluster cavities across different redshifts are observed to predict the optimum way of measuring f in future observations. We find that the SKA can constrain the suppression factor in the cavities of cluster MS 0735.6+7421 (MS0735) in as little as 4 h, with a smallest observable value of $f \approx 0.42$. Additionally, while the SKA may distinguish between possible thermal or non-thermal suppression factor values within the cavities of MS0735 if it observes for more than 8 h, determining the gas type of other clusters will likely require observations at multiple frequencies. The effect of cavity line of sight (LOS) position is also studied, and degeneracies between LOS position and the measured value of f are found. Finally, we find that for small cavities (radius < 80 kpc) at high redshift ($z \approx 1.5$), the proposed high frequencies of the SKA (23.75–37.5 GHz) will be optimal, and that including MeerKAT antennas will improve all observations of this type.

Sea-ice floating in the Arctic ocean is a constantly moving, growing and melting layer. The seasonal cycle of sea-ice volume has an average amplitude of $10\,000\,\mathrm{km}^3$ or 9 trillion tonnes of sea ice. The role of dynamic redistribution of sea ice is observable during winter growth by the incorporation of satellite remote sensing of ice thickness, concentration and drift. Recent advances in the processing of CryoSat-2 radar altimetry data have allowed for the retrieval of summer sea-ice thickness. This allows for a full year of a purely remote sensing-derived ice volume budget analysis.

Here, we present the closed volume budget of Arctic sea ice over the period October 2010–May 2022 revealing the key contributions to summer melt and minimum sea-ice volume and extent. We show the importance of ice drift to the inter-annual variability in Arctic sea-ice volume and the regional distribution of sea ice. The estimates of specific areas of sea-ice growth and melt provide key information on sea-ice over-production, the excess volume of ice growth compared to melt. The statistical accuracy of each key region of the Arctic is presented, revealing the current accuracy of knowledge of Arctic sea-ice volume from observational sources.

This paper will focus on the satellite threat to observational astronomy. As a member of the Satellite Constellation Working Group: Observatories Subgroup a recommendation was drafted for the Dark and Quiet Skies 2 Report, which shall be discussed. I shall also discuss SatHub activities and the education of observers about satellite constellations for regular citizens, amateur astronomers and professional astronomers and the outreach activities planned.

This paper presents an overview of the attitudes and awareness within the amateur astronomical community regarding modern satellite megaconstellation projects. A series of interviews and polls to assess the perspectives of this large community was conducted, aiming to uncover their concerns and issues. Additionally, the potential to involve respondents in community-driven projects focused on satellite data acquisition and processing was investigated. The objective is to enhance data quality for astronomical imagery protection and improve existing tools for scientific research, including satellite tracking, trajectory prediction, and the detection of space debris and orbital objects.

Radio interferometers can potentially detect the sky-averaged signal from the Cosmic Dawn (CD) and the Epoch of Reionisation (EoR) by studying the Moon as a thermal block to the foreground sky. The first step is to mitigate the Earth-based radio frequency interference (RFI) reflections (Earthshine) from the Moon, which significantly contaminate the FM band $\approx 88-110$ MHz, crucial to CD-EoR science. We analysed Murchison Widefield Array (MWA) phase I data from 72 to 180 MHz at 40 kHz resolution to understand the nature of Earthshine over three observing nights. We took two approaches to correct the Earthshine component from the Moon. In the first method, we mitigated the Earthshine using the flux density of the two components from the data, while in the second method, we used simulated flux density based on an FM catalogue to mitigate the Earthshine. Using these methods, we were able to recover the expected Galactic foreground temperature of the patch of sky obscured by the Moon. We performed a joint analysis of the Galactic foregrounds and the Moon’s intrinsic temperature $(T_{\mathrm{Moon}})$ while assuming that the Moon has a constant thermal temperature throughout three epochs. We found $T_{\mathrm{Moon}}$ to be at $184.4\pm{2.6}\,\mathrm{K}$ and $173.8\pm{2.5}\,\mathrm{K}$ using the first and the second methods, respectively, and the best-fit values of the Galactic spectral index $(\alpha)$ to be within the 5% uncertainty level when compared with the global sky models. Compared with our previous work, these results improved constraints on the Galactic spectral index and the Moon’s intrinsic temperature. We also simulated the Earthshine at MWA between November and December 2023 to find suitable observing times less affected by the Earthshine. Such observing windows act as Earthshine avoidance and can be used to perform future global CD-EoR experiments using the Moon with the MWA.

Many problems in astronomy and physics lend themselves to solutions from machine learning methods for the detection and classification of astronomical signals, and model inference from those signals. The historic presentation of machine learning methods as ‘black boxes’ has generated push back from some in the the physics/astronomy communities regarding how useful they are to truly uncover the physical laws that govern our world. Skepticism about the applicability of new computational methods in scientific inference is not new; we highlight connections between the machine learning contexts and previous computational paradigm shifts in astronomy. Moreover, several advances in methodologies challenge the assumption that machine learning ‘gives us answers that we can use but do not understand’ to standing physics questions. We summarize some astronomical machine learning data challenges used in astronomy and how we can use challenges on different scales to test different parts/use cases of our analysis methods.

This paper derives statistical models for predicting wintertime subseasonal temperature over the western US. The statistical models are trained on two separate datasets, namely observations and dynamical model simulations, and are based on least absolute shrinkage and selection operator (lasso). Surprisingly, statistical models trained on dynamical model simulations can predict observations better than observation-trained models. One reason for this is that simulations involve orders of magnitude more data than observational datasets.

Upcoming large-scale surveys like LSST are expected to uncover approximately 105 strong gravitational lenses within massive datasets. Traditional manual techniques are too time-consuming and impractical for such volumes of data. Consequently, machine learning methods have emerged as an alternative. In our prior work (Thuruthipilly et al. 2022), we introduced a self-attention-based machine learning model (transformers) for detecting strong gravitational lenses in simulated data from the Bologna Lens Challenge. These models offer advantages over simpler convolutional neural networks (CNNs) and competitive performance compared to state-of-the-art CNN models. We applied this model to the datasets from Bologna Lens Challenge 1 and 2 and simulated data on Euclid.

The angular power spectrum is a natural tool to analyse the observed galaxy number count fluctuations. In a standard analysis, the angular galaxy distribution is sliced into concentric redshift bins and all correlations of its harmonic coefficients between bin pairs are considered—a procedure referred to as ‘tomography’. However, the unparalleled quality of data from oncoming spectroscopic galaxy surveys for cosmology will render this method computationally unfeasible, given the increasing number of bins. Here, we put to test against synthetic data a novel method proposed in a previous study to save computational time. According to this method, the whole galaxy redshift distribution is subdivided into thick bins, neglecting the cross-bin correlations among them; each of the thick bin is, however, further subdivided into thinner bins, considering in this case all the cross-bin correlations. We create a simulated data set that we then analyse in a Bayesian framework. We confirm that the newly proposed method saves computational time and gives results that surpass those of the standard approach.