arXiv subjects

Natalia Stylianou

Publications and source records attributed to Natalia Stylianou.

4 recordsLinked to original sources

arXiv2026

MIGHTEE-HI / LADUMA: Investigating the link between baryons and dynamics with 130 resolved HI-selected galaxies

The baryonic Tully-Fisher relation (bTFR) and the radial acceleration relation (RAR) link the observed dynamics in galaxies to that expected from their baryonic mass distributions. The relations' small intrinsic scatters place strong constraints on galaxy formation models, dark matter properties and theories of modified dynamics, yet detailed measurements beyond the very local Universe remain limited. We use 130 purely HI-selected galaxies with resolved HI kinematics and baryonic mass profiles to measure the bTFR and RAR up to $z\approx0.09$. We measure a tight RAR with an acceleration scale $a_0=(1.50\pm0.05)\times10^{-10},{\rm m,s^{-2}}$ and an intrinsic scatter of $0.096\pm0.006$ dex, consistent with local results. We fit the bTFR in the `inverse' direction, conditioning on $M_{\rm bar}$ to mitigate HI flux-related selection effects, measuring a logarithmic slope of $0.27\pm0.01$ (corresponding to a forward slope of $3.72\pm0.16$), with vertical intrinsic scatter $σ_\perp\approx0.05$ dex. Fitting the general $δ$-family of MOND interpolating functions to the RAR, we infer $δ=4.10^{+1.4}_{-0.68}$, consistent with the value required by Solar System gravitational constraints and a null Wide Binary Test. We find no significant redshift evolution in the RAR acceleration scale for our pure HI-selected sample. However, the bTFR zero-point shows an apparent evolutionary trend that is strongly dependent on the fit direction: the traditional forward fit yields an $8.7σ$ preference for $z$ evolution, while for our fiducial inverse fit, this reduces to $3.4σ$, within $\approx2σ$ of the RAR evolution constraint. This suggests selection effects bias the forward fit; a careful consideration of such effects will be required in future endeavours to robustly measure the redshift evolution of dynamical scaling relations.

astro-ph.GA↗

arXiv2026

A MIGHTEE robust measurement of the star formation rate-radio correlation

Determining the relationship between star-formation rate (SFR) and the radio luminosity ($L_{1.4}$) is critical if we are to trace the star-formation history of the Universe dust-agnostically using current and future radio facilities. However, until now, such work has relied on potentially biased binary classifications of sources to remove contaminating active galactic nuclei (AGN). We present a new, statistically-driven methodology for deriving the SFR -- $L_{1.4}$ relation, removing the need for problematic cuts. We use a Bayesian hierarchical mixture model fit to the radio-detected sources in the deep MIGHTEE COSMOS DR1 catalogue, incorporating the full SFR posterior probability distributions generated by state-of-the-art spectral energy distribution fitting code \texttt{GRAHSP}. This allows us to probabilistically determine a mean SFR -- $L_{1.4}$ relation for the SF dominated galaxies, whilst accounting for changing fractions of SF dominated sources across redshift, radio luminosity and stellar mass ranges. We find that the SFR -- radio luminosity correlation exhibits a significant dependence on redshift, but a stellar mass dependence that is weaker than previous studies. Our resultant SFR-radio correlation is $\log_{10}(\text{SFR}/M_{\odot}\,\text{yr}^{-1}) = 0.790\times(\log _{10}(L_{1.4}/\text{W\,Hz}^{-1})-23) + 1.244 \times(1+z)^{0.122} -0.033 \times (\log_{10}(M_*/M_{\odot})-10)$, with an intrinsic scatter of 0.178 dex. We show that this redshift evolution could be explained by a moderate evolution in the radio spectral index of SF galaxies. We attribute the lack of observed strong dependence on stellar mass, compared to recent studies, to the novel statistical approach that does not rely on cuts to remove AGN.

astro-ph.GA↗

arXiv2026

MIGHTEE: The dark matter haloes, duty cycle and mechanical feedback from radio-AGN up to $z \sim 2.5$

Radio-AGN are observed to be more strongly clustered than non-active galaxies, though it is unclear whether this is simply due to their preference for massive host galaxies, or if they reside in distinct environments beyond this mass dependence. Using data from three fields covered by the MIGHTEE survey, we measure the angular two-point cross-correlation functions with a large, stellar mass-limited population of near-infrared selected galaxies, overcoming limitations of previous single-deep-field studies. By fitting halo occupation distribution models, we infer the galaxy bias parameters, $b$, for radio-AGN in three redshift ranges with median redshifts of $z_{med}=0.76^{+0.17}_{-0.28}$, $1.25^{+0.14}_{-0.17}$ and $1.75^{+0.44}_{-0.18}$, finding $b=1.94^{+0.07}_{-0.07}$, $2.50^{+0.11}_{-0.18}$ and $3.38^{+0.27}_{-0.38}$, respectively. The typical dark matter halo mass decreases with increasing redshift: $\log_{10}(\langle M_{h} \rangle/{M_\odot})=13.44^{+0.08}_{-0.08}$, $13.17^{+0.07}_{-0.06}$ and $13.03^{+0.09}_{-0.10}$, which we attribute to the increased abundance of cold gas required to fuel AGN activity at earlier times. The AGN duty cycle is determined to be $\sim5-9\%$, and we estimate that the total energy radiated by radio-jets over $0<z<2.5$ is $\sim10^{53}$ J per halo, which is sufficient to account for the observed excess heating of gas beyond that of gravitational collapse. Comparing the typical dark matter halo masses to the values obtained for the control sample, we find that the halo masses of radio-AGN are $1.54^{+0.47}_{-0.33}$, $1.11^{+0.25}_{-0.20}$ and $1.82^{+1.04}_{-0.57}$ times greater than those of the stellar mass- and redshift-matched galaxies. This difference could arise because AGN feedback suppresses stellar mass growth while leaving halo mass unchanged, or because radio-AGN preferentially reside in earlier forming haloes which are more strongly clustered.

astro-ph.GA↗

arXiv2022

The sensitivity of GPz estimates of photo-z posterior PDFs to realistically complex training set imperfections

The accurate estimation of photometric redshifts is crucial to many upcoming galaxy surveys, for example the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Almost all Rubin extragalactic and cosmological science requires accurate and precise calculation of photometric redshifts; many diverse approaches to this problem are currently in the process of being developed, validated, and tested. In this work, we use the photometric redshift code GPz to examine two realistically complex training set imperfections scenarios for machine learning based photometric redshift calculation: i) where the spectroscopic training set has a very different distribution in colour-magnitude space to the test set, and ii) where the effect of emission line confusion causes a fraction of the training spectroscopic sample to not have the true redshift. By evaluating the sensitivity of GPz to a range of increasingly severe imperfections, with a range of metrics (both of photo-z point estimates as well as posterior probability distribution functions, PDFs), we quantify the degree to which predictions get worse with higher degrees of degradation. In particular we find that there is a substantial drop-off in photo-z quality when line-confusion goes above ~1%, and sample incompleteness below a redshift of 1.5, for an experimental setup using data from the Buzzard Flock synthetic sky catalogues.

astro-ph.IM↗