arXiv subjects

Hugh McDougall

Publications and source records attributed to Hugh McDougall.

4 recordsLinked to original sources

arXiv2026

AGN Reverberation Mapping with LITMUS: Fundamental Limits on lag Recovery Rates

Reverberation mapping of active galactic nuclei provides one of the most direct probes of the geometry and kinematics of the broad-line region by measuring time delays between continuum and line variability. Modern RM surveys frequently suffer difficulties with lag measurements due to poor signal to noise and aliasing, whereby multimodal lag posterior distributions arise due to seasonal gaps in our data. These challenge the reliability of commonly used fitting tools such as JAVELIN, which can return a high rate of false positives. We implement a new lag measurement package, LITMUS, and introduce a new framework that uses Bayesian evidence to identify false positive lag measurements, as well as examine the question of how many AGN present detectable lags in high redshift industrial scale surveys like OzDES and SDSS. Our analysis differs from previous RM studies in six key respects: (i) our inference is robust to the previously under-diagnosed numerical component of aliasing, (ii) we use a consistent methodology for all sources, (iii) uncertainty in the underlying AGN variability is fully marginalised, (iv) lag significance is assessed via Bayesian model comparison rather than heuristic metrics, (v) false-positive rates are quantified by comparison against random-chance recoveries and (vi) we use marginal likelihoods to distinguish between sources where a lag is not detectable in our data and sources that show no evidence of reverberation. Applied to the OzDES sample, we find that previous RM studies are likely to have overestimated the confidence of recovered lags, and we find a stark contrast between a low reverberation percentage for the MgII line (3-28% depending on assumptions) and much higher percentages in the CIV and especially the H$β$ line, which is consistent with 100%. We also present a re-analysed set of lags from the OzDES sample with better quantified reliabilities

astro-ph.CO↗

arXiv2026

Fortifying gravitational-wave population inference with normalizing flows

As the LIGO-Virgo-KAGRA collaboration's (LVK's) gravitational-wave transient catalog grows, we are learning a wealth of information from the population properties of binary black hole mergers. Events in the catalog are represented with posterior samples describing the astrophysical parameters for each event. Population studies combine these samples to measure the distribution of astrophysical parameters such as black hole masses and spins. However, the posterior-sample representation of each event is only approximate. We construct a mock population with masses drawn from an astrophysically-motivated distribution with sharp features. Using this, we demonstrate that when $\gtrsim 300$ events are combined, even with each event's posterior represented by $1 \times 10^4 {-} 2 \times 10^4$ samples, the numerical error can become large enough that the resulting population inference is unreliable. We consider two solutions. In the short term, we show that nested samples (already produced by LVK analyses) can be used to more accurately describe each event in population studies. But this will only grant a temporary reprieve until the nested-sample representation becomes inadequate. In the longer term, we propose to represent each event with a normalizing flow. In order to represent each event with sufficient accuracy, each normalizing flow can be used to generate an arbitrarily large number of new posterior samples with a significantly reduced computational cost relative to traditional sampling methods. When compared to nested sampling, our normalizing flows produce posterior draws with a median of $\approx 80\%$ fewer likelihood evaluations per sample, while also providing greater opportunity for parallelization. We believe refinement of normalizing flow architectures and training techniques in future works could further reduce this per-sample cost significantly.

astro-ph.HE↗

arXiv2026

Stacked Reverberation Mapping of High Redshift Quasars in DESI. I. Feasibility Analysis

The broad line region of quasars has long been probed by reverberation mapping techniques that measure time lags between continuum and broad emission line variations. Stacked reverberation mapping has been proposed as a less observationally expensive alternative to traditional methods. This ensemble approach also reduces biases from small-number statistics. The Dark Energy Spectroscopic Instrument (DESI) is conducting the most extensive spectroscopic survey of quasars to date. We create mock light curves emulating expected DESI quasar observations at redshifts $1.48<z<5.2$ and luminosities $ 44.68 \leq \log L_{1350} λ/ \mathrm{erg\,s^{-1}} \leq 45.99 $ to test stacked reverberation mapping feasibility using sparse spectroscopic data paired with well-sampled photometric data. The pipeline, using the lag estimation code JAVELIN, successfully recovers the simulated C IV lags within one sigma of the true values using spectroscopic light curves composed of only a few spectral epochs (2-10) with irregular cadences. We investigate how observational factors, including C IV flux error magnitude, number of stacked quasars, and spectral epoch count, affect performance. This work motivates a pathway for future stacked reverberation mapping projects with large scale spectroscopic surveys of quasars having $\geq 2$ spectroscopic observations. Our results suggest an economical alternative for constraining and extending the radius-luminosity relation to higher redshifts and luminosities. Subsequently, this relation can be employed more reliably in single-epoch black hole mass measurements and quasar cosmology in these distant regimes.

astro-ph.GA↗

arXiv2026

LITMUS: Bayesian Lag Recovery in Reverberation Mapping with Fast Differentiable Models

Reverberation mapping is a technique in which the mass of a Seyfert I galaxy's central supermassive black hole is estimated, along with the system's physical scale, from the timescale at which variations in brightness propagate through the galactic nucleus. This mapping allows for a long baseline of time measurements to extract spatial information beyond the angular resolution of our telescopes, and is the main means of constraining supermassive black hole masses at high redshift. The most recent generation of multi-year reverberation mapping campaigns for large numbers of active galactic nuclei (e.g. OzDES) have had to deal with persistent complications of identifying false positives, such as those arising from aliasing due to seasonal gaps in time-series data. We introduce LITMUS (Lag Inference Through the Mixed Use of Samplers), a modern lag recovery tool built on the "damped random walk" model of quasar variability, built in the autodiff framework JAX. LITMUS is purpose built to handle the multimodal aliasing of seasonal observation windows and provides evidence integrals for model comparison, a more quantified alternative to existing methods of lag validation. LITMUS also offers a flexible modular framework for extending modelling of AGN variability, and includes JAX-enabled implementations of other popular lag recovery methods like nested sampling and the interpolated cross correlation function. We test LITMUS on a number of mock light curves modelled after the OzDES sample and find that it recovers their lags with high precision and a successfully identifies spurious lag recoveries, reducing its false positive rate to drastically outperform the state of the art program JAVELIN. LITMUS's high performance is accomplished by an algorithm for mapping the Bayesian posterior density which both constrains the lag and offers a Bayesian framework for model null hypothesis testing.

astro-ph.GA↗