arXiv subjects

Daniel J. Mortlock

Publications and source records attributed to Daniel J. Mortlock.

At least 19 recordsLinked to original sources

arXiv2026

pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web

Galaxy sizes are correlated with stellar mass and redshift, as characterised by size scaling relations. The inferred forms of these scaling relations are sensitive to how galaxies are classified -- either by their star formation activity (e.g. specific star-formation rate, sSFR) or by their morphology markers (e.g. bulge-to-total ratio, S\'{e}rsic index). We combine stellar mass and sSFR estimates from pop-cosmos (a generative model trained on COSMOS2020 Spitzer IRAC $\textit{Ch.1} <26$) with size and morphology measurements from COSMOS-Web, obtaining $99,369$ galaxies. By investigating the size-mass and the size-redshift relations, we show that: (i) the sSFR/morphology splits give quantitatively different slopes, intercepts, and intrinsic scatter behaviour; (ii) intrinsic scatter depends on structural morphology but not on sSFR, which constrains the galaxy-halo connection; (iii) the quiescent and bulge-dominated size-mass relations both show double-power law breaks, but at different pivot masses, indicating that quenching and structural transformation occur on different time-scales; (iv) the morphology-dependent trends are only recoverable from space-based imaging. Further, the quiescent pivot mass $M_{\ast} \sim 10^{10.7}~\mathrm{M}_{\odot}$ coincides with the mass scale at which AGN (infrared torus) bolometric luminosity fraction peaks in transitioning galaxies, while the bulge-dominated pivot mass $M_{\ast} \sim 10^{11.1}~\mathrm{M}_{\odot}$ coincides with the halo mass at which AGN-driven baryonic redistribution peaks, tracing the interval over which AGN feedback ramps from quenching onset to structural transformation.

astro-ph.GA↗

arXiv2026

pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches

The physical processes that shape a galaxy's spectrum are strongly degenerate in observations, obscuring which processes act independently. Leveraging the pop-cosmos generative galaxy population model, we investigate how many independent degrees of freedom the rest-frame optical SED contains. We use a $\beta$-variational autoencoder (VAE) to compress a 16-parameter stellar population synthesis (SPS) description into a disentangled latent representation interpreted through mutual information (MI). We find that five independent dimensions suffice, corresponding to stellar mass, recent star formation, dust, and two degrees of freedom in the ionization state of the gas. Stellar metallicity and stellar age are not among these primary drivers; their spectral effects are distributed across the others rather than independently encoded. By tying each dimension to specific spectral features, this decomposition breaks the star-formation--dust--metallicity degeneracies that limit broadband photometry, and recovers the physical conditions of the gas in typical star-forming galaxies more cleanly than the line-ratio diagnostics in standard use.

astro-ph.GA↗

arXiv2026

pop-cosmos: Star formation over 12 Gyr from generative modelling of a deep infrared-selected galaxy catalogue

We study star formation over 12 Gyr using pop-cosmos, a generative model trained on 26-band photometry of 420,000 COSMOS2020 galaxies (IRAC Ch.1 $<26$). The model learns distributions over 16 SPS parameters via score-based diffusion, matching observed colours and magnitudes. We compute the star formation rate density (SFRD) to $z=3.5$ by directly integrating individual galaxy SFRs. The SFRD peaks at $z=1.3\pm0.1$, with peak value $0.08\pm0.01$ M$_{\odot}$ yr$^{-1}$ Mpc$^{-3}$. We classify star-forming (SF) and quiescent (Q) galaxies using specific SFR $<10^{-11}$ yr$^{-1}$, comparing with $NUVrJ$ colour selection. The sSFR criterion yields up to 20% smaller quiescent fractions across $0 0.95$) during the most recent $\sim$300 Myr, then sharp decorrelation with earlier star-forming epochs, marking clear quenching transitions. Massive ($10<\log_{10}(M_*/$M$_{\odot})<11$) galaxies quench on a time-scale of $\sim1$ Gyr, with mass assembly concentrated in their first 3.5 Gyr. Finally, AGN activity (infrared luminosity) peaks as massive ($\sim10^{10.5}$ M$_\odot$) galaxies approach the transition between star-forming and quiescent states, declining sharply once quiescence is established. This provides evidence that AGN feedback operates in a critical regime during the $\sim1$ Gyr quenching transition.

astro-ph.GA↗

arXiv2026

Spatially resolved star-formation histories of local post-starburst galaxies: Starburst and quenching spatial patterns consistent with recent mergers

Post-starburst (PSB) galaxies, having recently experienced a starburst followed by rapid quenching, are excellent laboratories to probe physical mechanisms that drive starbursts and shutting down of star formation. Integral-field spectroscopy reveals the galaxies' spatially-resolved properties, where observed directional patterns can be linked to the galaxies' past evolution. We measure the resolved star-formation histories (SFHs), stellar metallicity evolution and dust properties of three local PSBs from the MaNGA survey, down to $0.5$" resolution ($\sim0.3\,$kpc) using a hierarchical Bayesian model. Local parameters were constrained simultaneously with parameters describing spatial trends. We found that all three galaxies first experienced an outer, weaker and slower quenching starburst, followed by a central, stronger and faster quenching starburst that peaked $\sim 1\,$Gyr after the first. The central starbursts induced a significantly stronger rise in stellar metallicity compared to the outer starbursts. These results are consistent with the effects of a recent gas-rich (wet) merger, where the first pericentre passage triggered starbursts in the outer regions, while the later coalescence triggers a stronger centralised starburst. We find non-axisymmetric features in the maps of burst mass fraction and dust attenuation in all galaxies, which could be caused by tidal effects during the recent merger. Comparisons with literature binary merger simulations suggests that the galaxies' rapid quenching was driven by gas consumption and the stabilisation against gas gravitational collapse by a growing spheroid, while AGN feedback was not necessarily a primary cause.

astro-ph.GA↗

arXiv2026

pop-cosmos: Forward modeling KiDS-1000 redshift distributions using realistic galaxy populations

The accuracy of the cosmological constraints from Stage~IV galaxy surveys will be limited by how well the galaxy redshift distributions can be inferred. We have addressed this challenging problem for the Kilo-Degree Survey (KiDS) cosmic shear sample by developing a forward-modeling framework with two main ingredients: (1) the \texttt{pop-cosmos} generative model for the evolving galaxy population, calibrated on \textit{Spitzer} IRAC $\textit{Ch.\,1}<26$ galaxies from COSMOS2020; and (2) a data model for noise and selection, machine-learned from the SURFS-based KiDS-Legacy-Like Simulations (SKiLLS). Applying KiDS tomographic binning to our synthetic photometric data, we infer redshift distributions in each of five bins directly from the population and data models, bypassing the need for spectroscopic reweighting. Keeping the data model fixed, we compare results using two different galaxy population models: \texttt{pop-cosmos}; and \texttt{shark}, the semi-analytic galaxy formation model used in SKiLLS. In the first ($0.1<z<0.3$) and last ($0.9<z<1.2$) tomographic bins we find systematic differences in the mean redshifts of $Δz\sim0.05$-$0.1$, comparable to the reported uncertainties from spectroscopic reweighting methods. This work paves the way for accurate redshift distribution calibration for Stage~IV surveys directly through forward modeling, thus providing an independent cross-check on spectroscopic-based calibrations which avoids their selection biases and incompleteness. We will use the \texttt{pop-cosmos} redshift distributions in an upcoming full KiDS cosmology reanalysis.

astro-ph.CO↗

arXiv2026

pop-cosmos: Redshifts and physical properties of KiDS-1000 galaxies

Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak lensing surveys with millions of sources. We address this gap by applying the pop-cosmos generative model to perform spectral energy distribution (SED) fitting for 4 million KiDS-1000 galaxies. Calibrated on deep COSMOS2020 photometric data, pop-cosmos specifies a physically-motivated prior over the galaxy population up to $z \simeq 6$ in stellar population synthesis (SPS) parameter space. Using the Speculator SPS emulator with GPU-accelerated MCMC sampling, we perform full posterior inference at 8.2 GPU seconds per galaxy, obtaining joint constraints on galaxy redshifts and physical properties. We validate photometric redshifts against $\sim\!185,\!000$ KiDS galaxies cross-matched to DESI DR1 spectroscopic samples, achieving low bias ($2\times10^{-3}$), scatter ($\sigma_{\mathrm{MAD}}=0.03$), and outlier fraction (3.2%) for the Bright Galaxy Survey, with comparable performance (bias $3\times10^{-2}$, $\sigma_{\mathrm{MAD}}=0.05$, 1.0% outliers) for luminous red galaxies (LRGs). Within the LRG sample, we identify massive, dusty, star-forming contaminants at $z \simeq 0.4$ satisfying standard colour selections for quenched populations. We infer trends in stellar mass, star formation, metallicity, and dust across five tomographic redshift bins consistent with established scaling relations. Using specific star formation rate constraints, we identify $\sim$7% of KiDS-1000 galaxies as quenched, versus 37% implied by conservative colour cuts. This enables the construction of weak lensing samples defined by physical properties while mitigating intrinsic alignment systematics and preserving statistical power. Our analysis validates pop-cosmos out-of-sample, establishing it as a scalable approach for galaxy evolution and cosmological analyses with photometric surveys.

astro-ph.GA↗

arXiv2025

pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population

We present an extension of the pop-cosmos model for the evolving galaxy population up to redshift $z\sim6$. The model is trained on distributions of observed colors and magnitudes, from 26-band photometry of $\sim420,000$ galaxies in the COSMOS2020 catalog with Spitzer IRAC $\textit{Ch. 1}<26$. The generative model includes a flexible distribution over 16 stellar population synthesis (SPS) parameters, and a depth-dependent photometric uncertainty model, both represented using score-based diffusion models. We use the trained model to predict scaling relationships for the galaxy population, such as the stellar mass function, star-forming main sequence, and gas-phase and stellar metallicity vs. mass relations, demonstrating reasonable-to-excellent agreement with previously published results. We explore the connection between mid-infrared emission from active galactic nuclei (AGN) and star-formation rate, finding high AGN activity for galaxies above the star-forming main sequence at $1\lesssim z\lesssim 2$. Using the trained population model as a prior distribution, we perform inference of the redshifts and SPS parameters for 429,669 COSMOS2020 galaxies, including 39,588 with publicly available spectroscopic redshifts. The resulting redshift estimates exhibit minimal bias ($\text{median}[Δ_z]=-8\times10^{-4}$), scatter ($σ_\text{MAD}=0.0132$), and outlier fraction ($6.19\%$) for the full $0<z<6$ spectroscopic compilation. These results establish that pop-cosmos can achieve the accuracy and realism needed to forward-model modern wide--deep surveys for Stage IV cosmology. We publicly release pop-cosmos software, mock galaxy catalogs, and COSMOS2020 redshift and SPS parameter posteriors.

astro-ph.GA↗

arXiv2025

An approach to robust Bayesian regression in astronomy

Model mis-specification (e.g. the presence of outliers) is commonly encountered in astronomical analyses, often requiring the use of ad hoc algorithms which are sensitive to arbitrary thresholds (e.g. sigma-clipping). For any given dataset, the optimal approach will be to develop a bespoke statistical model of the data generation and measurement processes, but these come with a development cost; there is hence utility in having generic modelling approaches that are both principled and robust to model mis-specification. Here we develop and implement a generic Bayesian approach to linear regression, based on Student's t-distributions, that is robust to outliers and mis-specification of the noise model. Our method is validated using simulated datasets with various degrees of model mis-specification; the derived constraints are shown to be systematically less biased than those from a similar model using normal distributions. We demonstrate that, for a dataset without outliers, a worst-case inference using t-distributions would give unbiased results with $\lesssim\!10$ per cent increase in the reported parameter uncertainties. We also compare with existing analyses of real-world datasets, finding qualitatively different results where normal distributions have been used and agreement where more robust methods have been applied. A Python implementation of this model, t-cup, is made available for others to use.

astro-ph.IM↗

arXiv2025

An automated method for finding the most distant quasars

Upcoming surveys such as Euclid, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (Roman) will detect hundreds of high-redshift (z > 7) quasars, but distinguishing them from the billions of other sources in these catalogues represents a significant data analysis challenge. We address this problem by extending existing selection methods by using both i) Bayesian model comparison on measured fluxes and ii) a likelihood-based goodness-of-fit test on images, which are then combined using the F_beta statistic (where beta is a parameter which can be tuned to prioritise completeness). The result is an automated, reproduceable and objective high-redshift quasar selection pipeline. We test this on both simulations and real data from the cross-matched Sloan Digital Sky Survey (SDSS) and UKIRT Infrared Deep Sky Survey (UKIDSS) catalogues. On this cross-matched dataset we achieve an area under the curve (AUC) score of up to 0.81 and an F_3 score of up to 0.79; or, if the completeness is fixed to be 0.9, then we can obtain an efficiency of 0.15. This is sufficient to be applied to the Euclid, LSST and Roman data when available.

astro-ph.IM↗

arXiv2025

Impact of redshift distribution uncertainties on Lyman-break galaxy cosmological parameter inference

A significant number of Lyman-break galaxies (LBGs) with redshifts 3 < z < 5 are expected to be observed by the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). This will enable us to probe the universe at higher redshifts than is currently possible with cosmological galaxy clustering and weak lensing surveys. However, accurate inference of cosmological parameters requires precise knowledge of the redshift distributions of selected galaxies, where the number of faint objects expected from LSST alone will make spectroscopic based methods of determining these distributions extremely challenging. To overcome this difficulty, it may be possible to leverage the information in the large volume of photometric data alone to precisely infer these distributions. This could be facilitated using forward models, where in this paper we use stellar population synthesis (SPS) to estimate uncertainties on LBG redshift distributions for a 10 year LSST (LSSTY10) survey. We characterise some of the modelling uncertainties inherent to SPS by introducing a flexible parameterisation of the galaxy population prior, informed by observations of the galaxy stellar mass function (GSMF) and cosmic star formation density (CSFRD). These uncertainties are subsequently marginalised over and propagated to cosmological constraints in a Fisher forecast. Assuming a known dust attenuation model for LBGs, we forecast constraints on the sigma8 parameter comparable to Planck cosmic microwave background (CMB) constraints.

astro-ph.CO↗

arXiv2024

Data-Space Validation of High-Dimensional Models by Comparing Sample Quantiles

We present a simple method for assessing the predictive performance of high-dimensional models directly in data space when only samples are available. Our approach is to compare the quantiles of observables predicted by a model to those of the observables themselves. In cases where the dimensionality of the observables is large (e.g. multiband galaxy photometry), we advocate that the comparison is made after projection onto a set of principal axes to reduce the dimensionality. We demonstrate our method on a series of two-dimensional examples. We then apply it to results from a state-of-the-art generative model for galaxy photometry (pop-cosmos; arXiv:2402.00935) that generates predictions of colors and magnitudes by forward simulating from a 16-dimensional distribution of physical parameters represented by a score-based diffusion model. We validate the predictive performance of this model directly in a space of nine broadband colors. Although motivated by this specific example, we expect that the techniques we present will be broadly useful for evaluating the performance of flexible, non-parametric population models of this kind, and other settings where two sets of samples are to be compared.

astro-ph.IM↗

arXiv2024

pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model

We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pre-trained population model (pop-cosmos) that was calibrated using purely photometric data. This model specifies a prior distribution over 16 stellar population synthesis (SPS) parameters using a score-based diffusion model, and includes a data model with detailed treatment of nebular emission. We use a GPU-accelerated affine invariant ensemble sampler to achieve fast posterior sampling under this model for 292,300 individual galaxies in the COSMOS2020 catalog, leveraging a neural network emulator (Speculator) to speed up the SPS calculations. We apply both the pop-cosmos population model and a baseline prior inspired by Prospector-$α$, and compare these results to published COSMOS2020 redshift estimates from the widely-used EAZY and LePhare codes. For the $\sim 12,000$ galaxies with spectroscopic redshifts, we find that pop-cosmos yields redshift estimates that have minimal bias ($\sim10^{-4}$), high accuracy ($σ_\text{MAD}=7\times10^{-3}$), and a low outlier rate ($1.6\%$). We show that the pop-cosmos population model generalizes well to galaxies fainter than its $r<25$ mag training set. The sample we have analyzed is $\gtrsim3\times$ larger than has previously been possible via posterior sampling with a full SPS model, with average throughput of 15 GPU-sec per galaxy under the pop-cosmos prior, and 0.6 GPU-sec per galaxy under the Prospector prior. This paves the way for principled modeling of the huge catalogs expected from upcoming Stage IV galaxy surveys.

astro-ph.CO↗

arXiv2024

Measuring the nuclear equation of state with neutron star-black hole mergers

Gravitational-wave (GW) observations of neutron star-black hole (NSBH) mergers are sensitive to the nuclear equation of state (EOS). We present a new methodology for EOS inference with non-parametric Gaussian process (GP) priors, enabling direct constraints on the pressure at specific densities and the length-scale of correlations on the EOS. Using realistic simulations of NSBH mergers, incorporating both GW and electromagnetic (EM) selection to ensure sample purity, we find that a GW detector network operating at O5-sensitivities will constrain the radius of a $\unit[1.4]{M_{\odot}}$ NS and the maximum NS mass with $1.6\%$ and $13\%$ precision, respectively. With the same sample, the projected constraint on the length-scale of correlations in the EOS is $\geq~\unit[3.2]{MeV~fm^{-3}}$. These results demonstrate strong potential for insights into the nuclear EOS from NSBH systems, provided they are robustly identified.

gr-qc↗

arXiv2022

Bayesian constraints on the astrophysical neutrino source population from IceCube data

We present constraints on an astrophysical population of neutrino sources imposed by recent data from the IceCube neutrino observatory. By using the IceCube point source search method to model the detection of sources, our detection criterion is more sensitive than using the observation of high-energy neutrino multiplets for source identification. We frame the problem as a Bayesian hierarchical model to connect the high-level population parameters to the IceCube data, allowing us to consistently account for all relevant sources of uncertainty in our model assumptions. Our results show that sources with a local density of $n_0 \gtrsim 10^{-7}$ $\rm{Mpc}^{-3}$ and luminosity $L \lesssim 10^{43}$ erg/s are the most likely candidates, but that populations of rare sources with $n_0 \simeq 10^{-9}$ $\rm{Mpc}^{-3}$ and $L \simeq 10^{45}$ erg/s can still be consistent with the IceCube observations. We demonstrate that these conclusions are strongly dependent on the source evolution considered, for which we consider a wide range of models. In doing so, we present realistic, model-independent constraints on the population parameters that reflect our current state of knowledge from astrophysical neutrino observations. We also use our framework to investigate constraints in the case of possible source detections and future instrument upgrades. Our approach is flexible and can be used to model specific source cases and extended to include multi-messenger information.

astro-ph.HE↗

arXiv2021

Prospects for Measuring the Hubble Constant with Neutron-Star-Black-Hole Mergers

Gravitational wave (GW) and electromagnetic (EM) observations of neutron-star-black-hole (NSBH) mergers can provide precise local measurements of the Hubble constant ($H_0$), ideal for resolving the current $H_0$ tension. We perform end-to-end analyses of realistic populations of simulated NSBHs, incorporating both GW and EM selection for the first time. We show that NSBHs could achieve unbiased 1.5-2.4% precision $H_0$ estimates by 2030. The achievable precision is strongly affected by the details of spin precession and tidal disruption, highlighting the need for improved modeling of NSBH mergers.

astro-ph.CO↗

arXiv2020

Galaxy mass profiles from strong lensing III: The two-dimensional broken power-law model

When modelling strong gravitational lenses, i.e., where there are multiple images of the same source, the most widely used parameterisation for the mass profile in the lens galaxy is the singular power-law model $ρ(r)\propto r^{-γ}$. This model may be insufficiently flexible for very accurate work, for example measuring the Hubble constant based on time delays between multiple images. Here we derive the lensing properties - deflection angle, shear, and magnification - of a more adaptable model where the projected mass surface density is parameterised as a continuous two-dimensional broken power-law (2DBPL). This elliptical 2DBPL model is characterised by power-law slopes $t_1$, $t_2$ either side of the break radius $θ_\mathrm{B}$. The key to the 2DBPL model is the derivation of the lensing properties of the truncated power law (TPL) model, where the surface density is a power law out to the truncation radius $θ_\mathrm{T}$ and zero beyond. This TPL model is also useful by itself. We create mock observations of lensing by a TPL profile where the images form outside the truncation radius, so there is no mass in the annulus covered by the images. We then show that the slope of the profile interior to the images may be accurately recovered for lenses of moderate ellipticity. This demonstrates that the widely-held notion that lensing measures the slope of the mass profile in the annulus of the images, and is insensitive to the mass distribution at radii interior to the images, is incorrect.

astro-ph.GA↗

arXiv2020

Galaxy mass profiles from strong lensing II: The elliptical power-law model

We present a systematic analysis of the constraints $σ_γ$ on the mass profile slope $γ$ obtainable when fitting a singular power-law ellipsoid model to a typical strong lensing observation of an extended source. These results extend our previous analysis of circular systems, Paper I. We draw our results from 676 mock observations covering a range of image configurations, each created with a fixed signal to noise ratio $S=100$ in the images. We analyse the results using a combination of theory and a simplified model which identifies the contribution to the constraints of the individual fluxes and positions in each of the lensed images. The main results are: 1. For any lens ellipticity, the constraints $σ_γ$ for two image systems are well described by the results of Paper I, transformed to elliptical coordinates; 2. We derive an analytical expression for $σ_γ$ for systems with the source aligned with the axis of the lens; 3. For both two-image systems and aligned systems, $σ_γ$ is limited by the flux uncertainties; 4. The constraints for off-axis four-image systems are a factor of two to eight better, depending on source size, than for two-image systems, and improve with increasing lens ellipticity. We show that the constraints on $γ$ in these systems derive from the complementary positional information of the images alone, without using flux. The complementarity improves as the offset of the source from the axis increases, such that the best constraints $σ_γ<0.01$, for $S=100$, occur when the source approaches the caustic.

astro-ph.GA↗

arXiv2019

Unbiased Hubble constant estimation from binary neutron star mergers

Gravitational wave (GW) observations of binary neutron star (BNS) mergers can be used to measure luminosity distances and hence, when coupled with estimates for the mergers' host redshifts, infer the Hubble constant, $H_0$. These observations are, however, affected by GW measurement noise, uncertainties in host redshifts and peculiar velocities, and are potentially biased by selection effects and the mis-specification of the cosmological model or the BNS population. The estimation of $H_0$ from samples of BNS mergers with optical counterparts is tested here by using a phenomenological model for the GW strains that captures both the data-driven event selection and the distance-inclination degeneracy, while being simple enough to facilitate large numbers of simulations. A rigorous Bayesian approach to analyzing the data from such simulated BNS merger samples is shown to yield results that are unbiased, have the appropriate uncertainties, and are robust to model mis-specification. Applying such methods to a sample of $N \simeq 50$ BNS merger events, as LIGO+Virgo could produce in the next $\sim 5$ years, should yield robust and accurate Hubble constant estimates that are precise to a level of $\sim 2$ km s$^{-1}$ Mpc$^{-1}$, sufficient to reliably resolve the current tension between local and cosmological measurements of $H_0$.

astro-ph.CO↗