arXiv subjects

Richard Dodson

Publications and source records attributed to Richard Dodson.

At least 19 recordsLinked to original sources

arXiv2026

Spatio-kinematical structure of the Galactic Nuclear Stellar Disk revealed in VLBI astrometry of circumstellar masers

SKA-VLBI astrometry will enable us to measure up to thousands of three dimensional motions of OH masers associated with circumstellar envelopes (CSEs) of OH/IR stars in the Nuclear Stellar Disk (NSD) and sites of high mass star formation in the Central Molecular Zone (CMZ) of the Galactic Center (GC). It is expected that the spatio-kinematical distribution of those OH masers should indicate the existence of a ring structure in the NSD, which has formed as a result of outward propagation of star-formation activities in the GC. This is likely visualized clearly by a group of OH/IR stars, some of which should have stellar pulsation periods of >400 days and the corresponding ages of <500 Myr, and some sites of ongoing star formation. These OH/IR stars should host 1612-MHz OH masers, some of which should become targets of huge-sample VLBI astrometry, in moderate accuracy, in SKA-MID Band 2 (~1.6 GHz). The data of maser source proper motions will exhibit a stream motion in the stellar ring structure. Furthermore, the information of accurate distances (error <100 pc) of the maser sources are necessary to directly find the major-axis direction of a possible elliptical ring of stars at ~8 kpc. These distances may be yielded through trigonometric parallaxes measurable in SKA-MID Band 5a (5--7 GHz) and/or photometric parallaxes derived from the pulsation period--luminosity relation of long period variable stars hosting the maser sources.

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arXiv2026

Mapping the Milky Way with Masers

SKA-VLBI is poised to revolutionize our understanding of the Galactic structure through its unprecedented astrometric precision and sensitivity. As a next-generation facility, it will answer long-standing questions about the Galactic structure by mapping its entire spiral structure in detail, spanning from the solar neighborhood, through the Galactic Center, to the far side of the Milky Way. Its access to the Southern sky will allow us to obtain more precise 3D parameters of the Galactic bar, reveal the nature of the 3-kpc Arm, and clarify the dynamical coupling between the bar and the spiral arms. By leveraging high-precision astrometry of numerous celestial objects with SKA-VLBI, the Galactic fundamental parameters such as the Solar motion and the Galactic rotation curve can be constrained more precisely. These advancements will not only elucidate the structure of our Milky Way, but also provide benchmarks for understanding barred spiral galaxies in general. Furthermore, they are important for advancing our knowledge of cosmological structure formation. The capabilities of SKA-VLBI will open a new era of high precision Galactic astrometry.

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arXiv2026

Ultra-Precise Astrometric Search for Exoplanets with SKA-VLBI

The study of exoplanets is a rapidly developing field, driven by the discoveries of Kepler and TESS, among others. The recent detection of Jovian planetary companions of low-mass stars demonstrates that VLBI observations will be an excellent tool for indirect detection of planetary companions through precise radio astrometry of the host star. The anticipated sensitivity of SKA-VLBI and its capability to form multi-beam Tied Array Beams and MultiView analysis will allow us to achieve an order of magnitude increase in astrometric precision, providing much finer details for a wider range of exoplanets and hosts, which will revolutionize the field of exoplanets. Precise micro-arcsecond astrometric observations are crucial for detecting not only Jupiter-like planets, but also lower-mass planets. SKA-VLBI astrometric observations in L and C bands will open the possibility of indirect detection of thousands of planetary companions to radio-bright ultra cool dwarfs, M dwarfs and young stars. When a companion is also detected, the astrometric fit of the data will provide the dynamical masses of the components. In the case of binary systems with planets, fitting the astrometric data will provide the individual masses of stars and planets, as well as the mutual inclination angle of the system, which will show whether the planet is moving in prograde or retrograde orbit around its host star. The search for exoplanets at radio wavelengths will be complementary to other techniques and will allow for the detection of a population of exoplanets that is difficult to reach using other techniques.

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arXiv2026

DINGO/GAMA /WAVES: HI-halo mass relation

We investigate the relation between neutral atomic hydrogen (HI) and dark matter halo mass (HIHM) using observations from the Deep Investigation of Neutral Gas Origins (DINGO) pilot survey 100h data, combined with spectroscopic data from the Galaxy and Mass Assembly (GAMA) survey and photometric data from the Wide Area VISTA Extragalactic Survey (WAVES) photometric catalog. We employ a combination of direct detections and spectral stacking to probe the HI content of halos across a wide mass range ($10^{10.5} \lesssim M_\mathrm{h}/M_\odot \lesssim 10^{14.5}$). By incorporating WAVES photometric members on top of the existing GAMA group catalog, we present a novel approach of extending stacking analyses beyond spectroscopic completeness limits, enabling recovery of satellite HI content otherwise missed. We find that the HIHM relation exhibits a double power-law form, with a turnover near $M_\mathrm{h} \sim 10^{11.2} \text{ M}_\odot$. Central galaxies dominate the halo HI budget below $M_\mathrm{h} \sim 6 \times 10^{12} \text{ M}_\odot$, while satellites dominate at higher halo masses. Including photometric members increases the measured HI content in halos above $10^{13} \text{ M}_\odot$ by a factor of 1.5-3, highlighting the importance of gas-rich satellites in the group and cluster regime. Comparison with previous group-stacking studies shows that low-surface brightness galaxies, and intra-group HI structures contribute only a minor fraction to the total HI mass in group and cluster halos, as the summed galaxy HI masses are consistent with the total halo HI content.

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arXiv2026

The Potential Impact of Neuromorphic Computing on Radio Telescope Observatories

Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and next-generation Very Large Array (ngVLA) take shape. Under increasingly demanding power consumption, and increasingly challenging radio environments, science goals may become intractable with conventional von Neumann computing due to related power requirements. Neuromorphic computing offers a compelling alternative, and combined with a desire for data-driven methods, Spiking Neural Networks (SNNs) are a promising real-time power-efficient alternative. Radio Frequency Interference (RFI) detection is an attractive use-case for SNNs where recent exploration holds promise. This work presents a comprehensive analysis of the potential impact of deploying varying neuromorphic approaches across key stages in radio astronomy processing pipelines for several existing and near-term instruments. Our analysis paves a realistic path from near-term FPGA deployment of SNNs in existing instruments, allowing the addition of advanced data-driven RFI detection for no capital cost, to neuromorphic ASICs for future instruments, finding that commercially available solutions could reduce the power budget for key processing elements by up to three orders of magnitude, transforming the operational budget of the observatory. High-data-rate spectrographic processing could be a well-suited target for the neuromorphic computing industry, as we cast radio telescopes as the world's largest in-sensor compute challenge.

astro-ph.IM↗

arXiv2025

Deep Investigation of Neutral Gas Origins (DINGO): Options for robust Deep Spectral Line Imaging in the SKA-Era

The data storage requirements for deep spectral line observations with next-generation radio interferometers like the Australian Square Kilometre Array Pathfinder (ASKAP) and the Square Kilometre Array (SKA) are challenging. The default strategy is to reduce data after each daily observation and stack the resulting images. Although computationally efficient, this approach risks propagating systematic errors (e.g. RFI, continuum and deconvolution residuals) and degrades data quality. Imaging the entire deep dataset jointly, the traditional approach, is prohibitively expensive in storage and compute. We present an alternative \textit{uv}-grid stacking method and compare its outcomes with both the traditional approach, our benchmark, and the default image-stacking method, using 200~h of the Deep Investigation of Neutral Gas Origins (DINGO) pilot and main survey data. Our method pauses the standard imaging pipeline after forming the daily residual visibility grids, which are then stacked and jointly deconvolved to combine many epochs of data. Relative to the traditional method, image-stacking recovers a median of 0.92$_{-0.02}^{+0.05}$ of the reference {\HI} flux across our source sample, and \textit{uv}-grid stacking recovers 0.99$_{-0.04}^{+0.01}$. For the brightest source, both methods show a similar, negligible flux offset of $\sim$3 per~cent from the traditional flux. {\HI} velocity widths ($W_{50}$, $W_{20}$) are recovered to within a few per~cent by both methods, with image-stacking showing somewhat larger deviations and scatter. Image-stacking further introduces non-physical artefacts, such as negative bowls around strong sources, indicating poor deconvolution and loss of physical information. Based on these findings, we intend to apply \textit{uv}-grid stacking to the DINGO survey on ASKAP.

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arXiv2025

Neuromorphic Astronomy: An End-to-End SNN Pipeline for RFI Detection Hardware

Imminent radio telescope observatories provide massive data rates making deep learning based processing appealing while simultaneously demanding real-time performance at low-energy; prohibiting the use of many artificial neural network based approaches. We begin tackling the scientifically existential challenge of Radio Frequency Interference (RFI) detection by deploying deep Spiking Neural Networks (SNNs) on resource-constrained neuromorphic hardware. Our approach partitions large, pre-trained networks onto SynSense Xylo hardware using maximal splitting, a novel greedy algorithm. We validate this pipeline with on-chip power measurements, achieving instrument-scaled inference at 100mW. While our full-scale SNN achieves state-of-the-art accuracy among SNN baselines, our experiments reveal a more important insight that a smaller un-partitioned model significantly outperforms larger, split models. This finding highlights that hardware co-design is paramount for optimal performance. Our work thus provides a practical deployment blueprint, a key insight into the challenges of model scaling, and reinforces radio astronomy as a demanding yet ideal domain for advancing applied neuromorphic computing.

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arXiv2025

CHILES X: Molecular and atomic gas at intermediate redshift

We present ALMA CO observations of 14 HI-detected galaxies from the CHILES survey found in a cosmic over-density at z~0.12. This is the largest collection of spatially resolved CO + HI observations beyond the local Universe (z>0.05) to date. While the HI-detected parent sample spans a range of stellar masses, star formation rates (SFR), and environments, we only directly detect CO in the highest stellar mass galaxies, log(M_*/M_Sun)>10.0, with SFRs greater than ~2 M_Sun/yr. The detected CO has the kinematic signature of a rotating disk, consistent with the HI. We stack the CO non-detections and find a mean H_2 mass of log(M_H2/M_Sun) = 8.46 in galaxies with a mean stellar mass of log(M_*/M_Sun) = 9.35. In addition to high stellar masses and SFRs, the systems detected in CO are spatially larger, have redder overall colors, and exhibit broader (stacked) line widths. The CO emission is spatially coincident with both the highest stellar mass surface density and star forming region of the galaxies, as revealed by the 1.4 GHz continuum emission. We interpret the redder colors as the molecular gas being coincident with dusty regions of obscured star formation. The 14 HI detections show a range of morphologies, but the HI reservoir is always more extended than the CO. Finally, we compare with samples in the literature and find mild evidence for evolution in the molecular gas reservoir and H_2-to-HI gas ratio with redshift in HI flux-limited samples. We show that the scatter in the HI, and HI-to-stellar mass ratio is too great to conclusively measure evolution below z=0.2, and is even extremely difficult below z=0.4. Detections from CHILES are likely to be the only individual galaxies detected in HI between 0.1<z<0.23 for the foreseeable future due to the severity of satellite radio frequency interference, and its preferential impact on short baselines which dominate contemporary HI surveys.

astro-ph.GA↗

arXiv2025

Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes

Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources. Spiking Neural Networks (SNNs) offer a promising solution for real-time RFI detection by exploiting the time-varying nature of radio observation and neuron dynamics together. This work explores the inclusion of polarisation information in SNN-based RFI detection, using simulated data from the Hydrogen Epoch of Reionisation Array (HERA) instrument and provides power usage estimates for deploying SNN-based RFI detection on existing neuromorphic hardware. Preliminary results demonstrate state-of-the-art detection accuracy and highlight possible extensive energy-efficiency gains.

cs.NE↗

arXiv2025

Advancing RFI-Detection in Radio Astronomy with Liquid State Machines

Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI treat the task as a semantic segmentation problem on radio telescope spectrograms. Typically, complex heuristic algorithms handle this task of `flagging' in combination with manual labeling (in the most difficult cases). While recent machine-learning approaches have demonstrated high accuracy, they often fail to meet the stringent operational requirements of modern radio observatories. Owing to their inherently time-varying nature, spiking neural networks (SNNs) are a promising alternative method to RFI-detection by utilizing the time-varying nature of the spectrographic source data. In this work, we apply Liquid State Machines (LSMs), a class of spiking neural networks, to RFI-detection. We employ second-order Leaky Integrate-and-Fire (LiF) neurons, marking the first use of this architecture and neuron type for RFI-detection. We test three encoding methods and three increasingly complex readout layers, including a transformer decoder head, providing a hybrid of SNN and ANN techniques. Our methods extend LSMs beyond conventional classification tasks to fine-grained spatio-temporal segmentation. We train LSMs on simulated data derived from the Hyrogen Epoch of Reionization Array (HERA), a known benchmark for RFI-detection. Our model achieves a per-pixel accuracy of 98% and an F1-score of 0.743, demonstrating competitive performance on this highly challenging task. This work expands the sophistication of SNN techniques and architectures applied to RFI-detection, and highlights the effectiveness of LSMs in handling fine-grained, complex, spatio-temporal signal-processing tasks.

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arXiv2025

CHILES VIII: Probing Evolution of Average HI Content in Star Forming Galaxies over the Past 5 Billion Years

Utilizing the COSMOS HI Large Extragalactic Survey (CHILES) dataset, we investigate the evolution of the average atomic neutral hydrogen (HI) properties of galaxies over the continuous redshift range 0.09 $< z <$ 0.47. First, we introduce a simple multi-step, multi-scale imaging and continuum subtraction process that we apply to each observing session. These sessions are then averaged onto a common \textit{uv}-grid and run through a Fourier filtering artifact mitigation technique. We then demonstrate how this process results in science quality data products by comparing to the expected noise and image-cube kurtosis. This work offers the first-look description and scientific analysis after the processing of the entire CHILES database. These data are used to measure the average HI mass in four redshift bins, out to a redshift 0.47, by separately stacking blue cloud (NUV-r= -1 - 3) and red sequence (NUV-r = 3 - 6) galaxies. We find little-to-no change in gas fraction for the total ensemble of blue galaxies and make no detection for red galaxies. Additionally, we split up our sample of blue galaxies into an intermediate stellar mass bin (M$_{*} = 10^{9-10} M_{\odot}$) and a high stellar mass bin (M$_{*} = 10^{10-12.5} M_{\odot}$). We find that in the high mass bin galaxies are becoming increasingly HI poor with decreasing redshift, while the intermediate mass galaxies maintain a constant HI gas mass. We place these results in the context of the star-forming main sequence of galaxies and hypothesize about the different mechanisms responsible for their different evolutionary tracks.

astro-ph.GA↗

arXiv2025

First frequency phase transfer from the 3 mm to the 1 mm band on an Earth-sized baseline

Frequency Phase Transfer (FPT) is a technique designed to increase coherence and sensitivity in radio interferometry by making use of the non-dispersive nature of the troposphere to calibrate high-frequency data using solutions derived at a lower frequency. While the Korean VLBI Network has pioneered the use of simultaneous multi-band systems for routine FPT up to an observing frequency of 130 GHz, this technique remains largely untested in the (sub)millimeter regime. A recent effort has been made to outfit dual-band systems at (sub)millimeter observatories participating in the Event Horizon Telescope (EHT) and to test the feasibility and performance of FPT up to the observing frequencies of the EHT. We present the results of simultaneous dual-frequency observations conducted in January 2024 on an Earth-sized baseline between the IRAM 30-m in Spain and the JCMT and SMA in Hawai`i. We performed simultaneous observations at 86 and 215 GHz on the bright sources J0958+6533 and OJ287, with strong detections obtained at both frequencies. We observe a strong correlation between the interferometric phases at the two frequencies, matching the trend expected for atmospheric fluctuations and demonstrating for the first time the viability of FPT for VLBI at a wavelength of $\sim$1 millimeter. We show that the application of FPT systematically increases the 215 GHz coherence on all averaging timescales. In addition, the use of the co-located JCMT and SMA as a single dual-frequency station demonstrates the feasibility of paired-antenna FPT for VLBI for the first time, with implications for future array capabilities (e.g., ALMA sub-arraying and ngVLA calibration strategies).

astro-ph.IM↗

arXiv2025

New constraints on the evolution of the MHI-M* scaling relation combining CHILES and MIGHTEE-HI data

The improved sensitivity of interferometric facilities to the 21-cm line of atomic hydrogen (HI) enables studies of its properties in galaxies beyond the local Universe. In this work, we perform a 21 cm line spectral stacking analysis combining the MIGHTEE and CHILES surveys in the COSMOS field to derive a robust HI-stellar mass relation at z=0.36. In particular, by stacking thousands of star-forming galaxies subdivided into stellar mass bins, we optimize the signal-to-noise ratio of targets and derive mean HI masses in the different stellar mass intervals for the investigated galaxy population. We combine spectra from the two surveys, estimate HI masses, and derive the scaling relation log10(MHI) = (0.32 +- 0.04)log10(M*) + (6.65 +- 0.36). Our findings indicate that galaxies at z=0.36 are HI richer than those at z=0, but HI poorer than those at z=1, with a slope consistent across redshift, suggesting that stellar mass does not significantly affect HI exchange mechanisms. We also observe a slower growth rate HI relative to the molecular gas, supporting the idea that the accretion of cold gas is slower than the rate of consumption of molecular gas to form stars. This study contributes to understanding the role of atomic gas in galaxy evolution and sets the stage for future development of the field in the upcoming SKA era.

astro-ph.GA↗

arXiv2025

Serial MultiView: an efficient approach to mitigating atmospheric spatial-structure errors for VLBI astrometry

Atmospheric propagation errors are a main constraint on the accuracy of Very Long Baseline Interferometry (VLBI) astrometry. For relative astrometry, differential techniques can mitigate these errors, but their effectiveness diminishes with decreasing elevation and increasing angular separations between target and calibrator, among others. The MultiView technique addresses atmospheric spatial-structure errors by observing multiple calibrators around the target and interpolating at the target position, thereby reducing atmospheric errors more effectively than phase-referencing with only one calibrator. The first MultiView realisation at 1.6GHz involved cyclically observing all calibrators and the target, fitting a phase plane from calibrator solutions in each cycle, and is a well-established technique. This implementation reduces on-target time and is constricted by the short atmospheric coherence time at high frequencies. We propose a new realisation, serial MultiView, which rotates the phase plane iteratively based on the time series of calibrator residual phases. This new strategy obviates the necessity of observing all calibrators within each cycle, thereby shortening the observing cycle and offering considerable potential at higher frequencies where the temporal structure is the dominant source of errors. Additionally, by incorporating time-domain information in the iterations, phase ambiguities can be accurately and automatically identified. We verify the astrometric accuracy of serial MultiView at 5GHz by comparing it to conventional MultiView, achieving <10uas error in RA direction, and show the calibration overhead can be reduced in both approaches. This approach enables efficient, high-accuracy differential astrometry and artifact-reduced imaging for astrophysical studies, and we provide a user-friendly tool for it.

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arXiv2024

Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy

Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, as a time-series segmentation task suited for SNN execution. Automated systems capable of real-time operation with minimal energy consumption are increasingly important in modern radio telescopes. We explore several spectrogram encoding methods and network parameters, applying first and second-order leaky integrate and fire SNNs to tackle RFI detection. We introduce a divisive normalisation-inspired pre-processing step, improving detection performance across multiple encodings strategies. Our approach achieves competitive performance on a synthetic dataset and compelling initial results on real data from the Low-Frequency Array (LOFAR). We position SNNs as a viable path towards real-time RFI detection, with many possibilities for follow-up studies. These findings highlight the potential for SNNs performing complex time-series tasks, paving the way towards efficient, real-time processing in radio astronomy and other data-intensive fields.

cs.NE↗

arXiv2024

Optimising the Processing and Storage of Visibilities using lossy compression

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, through increased stations in the array and frequency span. Two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and I/O. An example of this is the data deluge expected from the SKA Telescopes of more than 60PB per day, all to be stored on the buffer filesystem. Compressing the data is an obvious solution. We used MGARD, an error-controlled compressor, and applied it to simulated and real visibility data, in noise-free and noise-dominated regimes. As the data has an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. Measuring the degradation of images reconstructed using the lossy compressed data, we explore the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images. We found relative error bounds of as much as $10\%$, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a $0.1\%$ error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds, and has a host of potentially powerful additional features.

astro-ph.IM↗

arXiv2024

Deep Investigation of Neutral Gas Origins (DINGO): Options for the Processing and Storage of Radio Astronomy Data for robust Deep Spectral Line Imaging in the SKA-Era using uv-Grids

The next generation of radio astronomy telescopes are challenging existing data analysis paradigms, as they have an order of magnitude more antennas and larger bandwidth. Foremost amongst these are deep spectral line surveys, because these have the largest number of epochs and spectral channels per dataset. For example, the Deep Investigation of Neutral Gas Origins (DINGO) project on the Australian Square Kilometre Array Pathfinder (ASKAP) aims to observe over 3,200 hours spread over hundreds of observing sessions, covering two tiles, two footprints and two frequency settings. The two primary problems encountered when processing this data are the need for storage and that processing is primarily I/O limited. To address these issues, we have implemented a deep imaging pipeline based on the storage of an intermediate data product in the software ASKAPsoft, that of the uv-gridded data, and have demonstrated lossy and lossless compression of this data on ASKAP, using MGARD and ADIOS2 libraries. We find data compression ratios from a factor of 7 (lossless) up to 20 (using lossy compression with an absolute error bound of $10^{-4}$), and processing is significantly faster for lossless compression. We discuss the effectiveness of lossy MGARD compression and its adherence to the designated error bounds, the trade-off between these error bounds and the corresponding compression ratios, as well as the potential consequences of these I/O and storage improvements on the science quality of the data products. As lossless compression allows us to achieve the DINGO goals within the storage limitations for the project, this will be the option adopted.

astro-ph.IM↗

arXiv2024

Supervised Radio Frequency Interference Detection with SNNs

Radio Frequency Interference (RFI) poses a significant challenge in radio astronomy, arising from terrestrial and celestial sources, disrupting observations conducted by radio telescopes. Addressing RFI involves intricate heuristic algorithms, manual examination, and, increasingly, machine learning methods. Given the dynamic and temporal nature of radio astronomy observations, Spiking Neural Networks (SNNs) emerge as a promising approach. In this study, we cast RFI detection as a supervised multi-variate time-series segmentation problem. Notably, our investigation explores the encoding of radio astronomy visibility data for SNN inference, considering six encoding schemes: rate, latency, delta-modulation, and three variations of the step-forward algorithm. We train a small twolayer fully connected SNN on simulated data derived from the Hydrogen Epoch of Reionization Array (HERA) telescope and perform extensive hyper-parameter optimization. Results reveal that latency encoding exhibits superior performance, achieving a per-pixel accuracy of 98.8% and an f1-score of 0.761. Remarkably, these metrics approach those of contemporary RFI detection algorithms, notwithstanding the simplicity and compactness of our proposed network architecture. This study underscores the potential of RFI detection as a benchmark problem for SNN researchers, emphasizing the efficacy of SNNs in addressing complex time-series segmentation tasks in radio astronomy.

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