arXiv subjects

J. A. Ely

Publications and source records attributed to J. A. Ely.

6 recordsLinked to original sources

arXiv2026

Scalable Bayesian data curation for next-generation radio experiments

Next-generation radio telescopes produce data volumes that preclude manual quality assessment, yet data curation remains essential for science. We present a general, fully automatic Bayesian anomaly-detection method for radio science experiments in which data curation is performed inside the inference: a latent anomaly indicator is marginalised in the likelihood rather than converted into an external pre-flag. Implemented in JAX with GPU-accelerated inference, the pipeline assigns probabilistic data-curation scores without prior knowledge and requires no thresholds, manual inspection, or subjective decisions. We demonstrate the method on the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH), applying it to 4655 observations (one year of REACH data). The pipeline assigns scores across time and frequency, enabling identification of the optimal observations to carry forward into scientific inference while reducing the risk that contaminated data bias the result. In doing so, it simultaneously recovers weather-driven systematics, instrument-component drifts, and narrow-band radio-frequency interference, while revealing complex dependencies between data quality and environmental or instrumental state that would be difficult to uncover by manual curation alone. This turns data curation from an external manual bottleneck into autonomous, inference-level infrastructure for the Square Kilometre Array era.

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arXiv2025

Radiometer Calibration using Machine Learning

Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer's primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the ``receiver'' chain. Instrumental effects introduced by the receiver are typically corrected or removed during calibration. However, impedance mismatches between the antenna and receiver can introduce unwanted signal reflections and distortions. Traditional calibration methods, such as Dicke switching, alternate the receiver input between the antenna and a well-characterised reference source to mitigate errors by comparison. Recent advances in Machine Learning (ML) offer promising alternatives. Neural networks, which are trained using known signal sources, provide a powerful means to model and calibrate complex systems where traditional analytical approaches struggle. These methods are especially relevant for detecting the faint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This is one of the main challenges in observational Cosmology today. Here, for the first time, we introduce and test a machine learning-based calibration framework capable of achieving the precision required for radiometric experiments aiming to detect the 21-cm line.

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arXiv2023

Radio antenna design for sky-averaged 21 cm cosmology experiments: the REACH case

Following the reported detection of an absorption profile associated with the 21~cm sky-averaged signal from the Cosmic Dawn by the EDGES experiment in 2018, a number of experiments have been set up to verify this result. This paper discusses the design process used for global 21~cm experiments, focusing specifically on the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH). This experiment will seek to understand and compensate for systematic errors present using detailed modelling and characterization of the instrumentation. There is detailed the quantitative figures of merit and numerical modelling used to assist the design process of the REACH dipole antenna (one of the 2 antenna designs for REACH Phase I). This design process produced a 2.5:1 frequency bandwidth dipole. The aim of this design was to balance spectral smoothness and low impedance reflections with the ability to describe and understand the antenna response to the sky signal to inform the critically important calibration during observation and data analysis.

astro-ph.IM↗

arXiv2022

The REACH radiometer for detecting the 21-cm hydrogen signal from redshift 7.5 to 28

Observations of the 21-cm line from primordial hydrogen promise to be one of the best tools to study the early epochs of the Universe: the Dark Ages, the Cosmic Dawn, and the subsequent Epoch of Reionization. In 2018, the EDGES experiment caught the attention of the cosmology community with a potential detection of an absorption feature in the sky-averaged radio spectrum centred at 78 MHz. The feature is deeper than expected, and, if confirmed, would call for new physics. However, different groups have re-analyzed the EDGES data and questioned the reliability of the signal. The Radio Experiment for the Analysis of Cosmic Hydrogen (REACH) is a sky-averaged 21-cm experiment aiming at improving the current observations by tackling the issues faced by current instruments related to residual systematic signals in the data. The novel experimental approach focuses on detecting and jointly explaining these systematics together with the foregrounds and the cosmological signal using Bayesian statistics. To achieve this, REACH features simultaneous observations with two different antennas, an ultra wideband system (redshift range 7.5 to 28), and a receiver calibrator based on in-field measurements. Simulated observations forecast percent-level constraints on astrophysical parameters, potentially opening up a new window to the infant Universe.

astro-ph.CO↗

arXiv2012

A blind detection of a large, complex, Sunyaev--Zel'dovich structure

We present an interesting Sunyaev-Zel'dovich (SZ) detection in the first of the Arcminute Microkelvin Imager (AMI) 'blind', degree-square fields to have been observed down to our target sensitivity of 100μJy/beam. In follow-up deep pointed observations the SZ effect is detected with a maximum peak decrement greater than 8 \times the thermal noise. No corresponding emission is visible in the ROSAT all-sky X-ray survey and no cluster is evident in the Palomar all-sky optical survey. Compared with existing SZ images of distant clusters, the extent is large (\approx 10') and complex; our analysis favours a model containing two clusters rather than a single cluster. Our Bayesian analysis is currently limited to modelling each cluster with an ellipsoidal or spherical beta-model, which do not do justice to this decrement. Fitting an ellipsoid to the deeper candidate we find the following. (a) Assuming that the Evrard et al. (2002) approximation to Press & Schechter (1974) correctly gives the number density of clusters as a function of mass and redshift, then, in the search area, the formal Bayesian probability ratio of the AMI detection of this cluster is 7.9 \times 10^4:1; alternatively assuming Jenkins et al. (2001) as the true prior, the formal Bayesian probability ratio of detection is 2.1 \times 10^5:1. (b) The cluster mass is MT,200 = 5.5+1.2\times 10^14h-1M\odot. (c) Abandoning a physical model with num- -1.3 70 ber density prior and instead simply modelling the SZ decrement using a phenomenological β-model of temperature decrement as a function of angular distance, we find a central SZ temperature decrement of -295+36 μK - this allows for CMB primary anisotropies, receiver -15 noise and radio sources. We are unsure if the cluster system we observe is a merging system or two separate clusters.

astro-ph.CO↗

arXiv2008

The Arcminute Microkelvin Imager

The Arcminute Microkelvin Imager is a pair of interferometer arrays operating with six frequency channels spanning 13.9-18.2 GHz, with very high sensitivity to angular scales 30''-10'. The telescope is aimed principally at Sunyaev-Zel'dovich imaging of clusters of galaxies. We discuss the design of the telescope and describe and explain its electronic and mechanical systems.

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