arXiv subjects

J. Willingham

Publications and source records attributed to J. Willingham.

4 recordsLinked to original sources

arXiv2026

EMU/GAMA: Refining Dust Extinction Corrections for H{\alpha} Luminosity Functions Using Radio-Based Calibration

We present a novel approach to correcting H$\alpha$ luminosity functions for dust extinction by calibrating against radio-based star formation rates (SFRs), using data from the Evolutionary Map of the Universe (EMU) and Galaxy and Mass Assembly (GAMA) surveys. Accurate dust correction is essential for deriving SFRs from rest-frame UV-optical emission lines, particularly as the \textit{James Webb Space Telescope} extends such measurements to galaxies at $z>5$. While a luminosity dependence of dust obscuration has long been recognised, our method exploits the empirical relationship between obscured (H$\alpha$) and unobscured (radio) SFRs to provide a dust correction that can be applied where traditional spectroscopic techniques, e.g. Balmer line based approaches, are unavailable. We apply the SFR based dust correction to 25 published H$\alpha$ luminosity functions spanning $0<z<8$, and derive corresponding star formation rate densities (SFRDs). Adopting the locally calibrated H$\alpha$--radio relation ends up with an overestimate of the cosmic SFRD by more than two orders of magnitude at $z\gtrsim1$. Motivated by the luminosity dependent relation in the local Universe, we introduce a new model where the luminosity dependence of the dust obscuration decreases with increasing redshift. This approach can reproduce observed SFRDs across cosmic time. These results highlight the potential of a radio-based calibration for dust correction, where a luminosity dependent correction would need to decline in strength with increasing redshift. This implies that the dust content or distribution in galaxies at early epochs differs substantially from that in the local Universe.

astro-ph.GA↗

arXiv2026

EMU/GAMA: A statistical perspective on active galactic nuclei diagnostics

While it is well known that galaxies are composites of many emission processes, quantifying the various contributions remains challenging. In this work, we use unsupervised machine learning based clustering algorithms to evaluate the agreement between the clustering tools and astrophysical classifications, and hence quantify the fractional contributions of star formation processes and nuclear black hole activity to the total galaxy energy budget of radio sources. We perform clustering on the multiwavelength (optical, infrared (IR), and radio) active galactic nuclei (AGN) diagnostic spaces, using the data from the G09 and G23 fields from the Galaxy and Mass Assembly (GAMA) survey, Evolutionary Map of the Universe (EMU) survey, and the Wide-field Infrared Survey Explorer (WISE). We find that the statistical clustering recovers $\approx$ 90 % of the star forming galaxies (SFGs) and $\approx$ 80 % of the AGN. We define a new IR-radio AGN diagnostic scheme that identifies radio AGN from IR SFGs and AGN, corresponding to the KMeans cluster with approximately 90 % reliability. We demonstrate the superior power of radio AGN selection in higher dimensions using a three-dimensional space composed of directly observable parameters ($\rm W_1-W_2$ colour, $\rm W_2$ magnitude, and the 1.4 GHz radio flux density). This novel three dimensional diagnostic shows immense potential in radio AGN selection that is close to 90 % reliable and 90 % complete. We also publish a catalogue of radio sources in the EMU survey with associated probabilities for them to be active in the optical regime, through which we emphasise the philosophy of considering a galaxy to be composed of various fractions rather than a binary classification of SFGs and AGN.

astro-ph.GA↗

arXiv2025

EMU and Euclid: Detection of a radio-optical galaxy clustering cross-correlation signal between the Evolutionary Map of the Universe and Euclid

Synergies between large-scale radio-continuum and optical/near-infrared galaxy surveys are a powerful tool for cosmology. Cross-correlating these surveys can constrain the redshift distribution of radio sources, mitigate systematic effects, and place constraints on cosmological models. We perform the first measurement of the clustering cross-spectrum between radio-continuum sources in the Evolutionary Map of the Universe (EMU) survey and galaxies from the ESA Euclid satellite mission's Q1 release. Our goal is to detect and characterise the cross-correlation signal, test its robustness against systematic effects, and compare our measurements with theoretical predictions. We use data from the Australian SKA Pathfinder's EMU Main Survey, which overlaps with the Euclid Deep Field South. We generate two radio-source catalogues using different source finders to create galaxy maps. We measure the harmonic-space cross-correlation signal using a pseudo-spectrum estimator. The measured signal is compared to theoretical predictions based on a {\Lambda}CDM cosmology, using several models for the EMU source redshift distribution and bias. We report detection above 8{\sigma} of the cross-correlation signal consistent across all tested models and data sets. The measured cross-spectra from the two radio catalogues are in excellent agreement, demonstrating that the cross-correlation is robust against the choice of source-finding algorithm. The measured signal also agrees with theoretical models developed from previous cross-correlation studies and simulations. This pathfinder study establishes a statistically significant cross-correlation between EMU and Euclid. The robustness of the signal is a crucial validation of the methodology, paving the way for future large-scale analyses leveraging the full power of this synergy to constrain cosmological parameters and our understanding of galaxy evolution.

astro-ph.CO↗

arXiv2025

EMU/GAMA: A new approach to characterising radio luminosity functions

This study characterises the radio luminosity functions (RLFs) for SFGs and AGN using statistical redshift estimation in the absence of comprehensive spectroscopic data. Sensitive radio surveys over large areas detect many sources with faint optical and infrared counterparts, for which redshifts and spectra are unavailable. This challenges our attempt to understand the population of radio sources. Statistical tools are often used to model parameters (such as redshift) as an alternative to observational data. Using the data from GAMA G23 and EMU early science observations, we explore simple statistical techniques to estimate the redshifts in order to measure the RLFs of the G23 radio sources as a whole and for SFGs and AGN separately. Redshifts and AGN/SFG classifications are assigned statistically for those radio sources without spectroscopic data. The calculated RLFs are compared with existing studies, and the results suggest that the RLFs match remarkably well for low redshift galaxies with an optical counterpart. We use a more realistic high redshift distribution to model the redshifts of (most likely) high redshift radio sources and find that the LFs from our approach match well with measured LFs. We also look at strategies to compare the RLFs of radio sources without an optical counterpart to existing studies.

astro-ph.GA↗