arXiv subjects

K. L. Polsterer

Publications and source records attributed to K. L. Polsterer.

6 recordsLinked to original sources

arXiv2023

A Gaussian process cross-correlation approach to time delay estimation in active galactic nuclei

We present a probabilistic cross-correlation approach to estimate time delays in the context of reverberation mapping (RM) of Active Galactic Nuclei (AGN). We reformulate the traditional interpolated cross-correlation method as a statistically principled model that delivers a posterior distribution for the delay. The method employs Gaussian processes as a model for observed AGN light curves. We describe the mathematical formalism and demonstrate the new approach using both simulated light curves and available RM observations. The proposed method delivers a posterior distribution for the delay that accounts for observational noise and the non-uniform sampling of the light curves. This feature allow us to fully quantify its uncertainty and propagate it to subsequent calculations of dependent physical quantities, e.g., black hole masses. It delivers out-of-sample predictions, which enables us to subject it to model selection and it can calculate the joint posterior delay for more than two light curves. Because of the numerous advantages of our reformulation and the simplicity of its application, we anticipate that our method will find favour not only in the specialised community of RM, but in all fields where cross-correlation analysis is performed. We provide the algorithms and examples of their application as part of our Julia GPCC package.

astro-ph.IM↗

arXiv2023

Modeling photometric reverberation mapping data for the next generation of big data surveys. Quasar accretion disks sizes with the LSST

Photometric reverberation mapping can detect the radial extent of the accretion disc (AD) in Active Galactic Nuclei by measuring the time delays between light curves observed in different continuum bands. Quantifying the constraints on the efficiency and accuracy of the delay measurements is important for recovering the AD size-luminosity relation, and potentially using quasars as standard candles. We have explored the possibility of determining the AD size of quasars using next-generation Big Data surveys. We focus on the Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, which will observe several thousand quasars with the Deep Drilling Fields and up to 10 million quasars for the main survey in six broadband filter during its 10-year operational lifetime. We have developed extensive simulations that take into account the characteristics of the LSST survey and the intrinsic properties of the quasars. The simulations are used to characterise the light curves from which AD sizes are determined using various algorithms. We find that the time delays can be recovered with an accuracy of 5 and 15% for light curves with a time sampling of 2 and 5 days, respectively. The results depend strongly on the redshift of the source and the relative contribution of the emission lines to the bandpasses. Assuming an optically thick and geometrically thin AD, the recovered time-delay spectrum is consistent with black hole masses derived with 30% uncertainty.

astro-ph.CO↗

arXiv2021

Disentangling the optical AGN and Host-galaxy luminosity with a probabilistic Flux Variation Gradient

We present a novel Probabilistic Flux Variation Gradient (PFVG) approach to to separate the contributions of active galactic nuclei (AGN) and host galaxies in the context of photometric reverberation mapping (PRM) of AGN. We explored the ability of recovering the fractional contribution in a model-independent way using the entire set of light curves obtained through different filters and photometric apertures simultaneously. The method is based on the observed bluer when brighter phenomenon that is attributed to the superimposition of a two-component structure; the red host galaxy, which is constant in time, and the varying blue AGN. We describe the PFVG mathematical formalism and demonstrate its performance using simulated light curves and available PRM observations. The new probabilistic approach is able to recover host-galaxy fluxes to within 1% precision as long as the light curves do not show a significant contribution from time delays. This represents a significant improvement with respect to previous applications of the traditional FVG method to PRM data. The proposed PFVG provides an efficient and accurate way to separate the AGN and host-galaxy luminosities in PRM monitoring data. The method will be especially helpful in the case of large upcoming photometric survey telescopes such as the public optical/near-infrared Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory. Finally, we have made the algorithms freely available as part of our Julia PFVG package.

astro-ph.IM↗

arXiv2019

Optical continuum photometric reverberation mapping of the Seyfert-1 galaxy Mrk509

We present the results of a two year optical continuum photometric reverberation mapping campaign carried out on the nucleus of the Seyfert-1 galaxy Mrk509. Specially designed narrow-band filters were used in order to mitigate the line and pseudo-continuum contamination of the signal from the broad line region, while allowing for high-accuracy flux-calibration over a large field of view. We obtained light curves with a sub-day time sampling and typical flux uncertainties of $1\%$. The high photometric precision allowed us to measure inter-band continuum time delays of up to $\sim 2$ days across the optical range. The time delays are consistent with the relation $τ\propto λ^{4/3}$ predicted for an optically thick and geometrically thin accretion disk model. The size of the disk is, however, a factor of 1.8 larger than predictions based on the standard thin-disk theory. We argue that, for the particular case of Mrk509, a larger black hole mass due to the unknown geometry scaling factor can reconcile the difference between the observations and theory.

astro-ph.GA↗

arXiv2019

Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps

With the advent of large scale surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. The analysis of complex morphological features in the spatial domain is a particularly important task. Here we discuss the challenges of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project and introduce a proper transfer mechanism via quantile random forest regression. By using parallelized rotation and flipping invariant Kohonen-maps, image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio continuum and WISE infrared all sky surveys are first projected down to a two-dimensional embedding in an unsupervised way. This embedding can be seen as a discretised space of shapes with the coordinates reflecting morphological features as expressed by the automatically derived prototypes. We find that these prototypes have reconstructed physically meaningful processes across two channel images at radio and infrared wavelengths in an unsupervised manner. In the second step, images are compared with those prototypes to create a heat-map, which is the morphological fingerprint of each object and the basis for transferring the user generated labels. These heat-maps have reduced the feature space by a factor of 248 and are able to be used as the basis for subsequent ML methods. Using an ensemble of decision trees we achieve upwards of 85.7% and 80.7% accuracy when predicting the number of components and peaks in an image, respectively, using these heat-maps. We also question the currently used discrete classification schema and introduce a continuous scale that better reflects the uncertainty in transition between two classes, caused by sensitivity and resolution limits.

astro-ph.IM↗

arXiv2015

An Explorative Approach for Inspecting Kepler Data

The Kepler survey has provided a wealth of astrophysical knowledge by continuously monitoring over 150,000 stars. The resulting database contains thousands of examples of known variability types and at least as many that cannot be classified yet. In order to reveal the knowledge hidden in the database, we introduce a new visualisation method that allows us to inspect time series exploratively. To that end, we propose dimensionality reduction on the parameters of a model capable of representing time series as fixed-length vector representation. We show that a more refined objective function can be chosen by minimising the prediction error of the data reconstruction instead of the reconstruction of the model parameters. The proposed visualisation exhibits a strong correlation between the variability behaviour of the light curves and their physical properties. As a consequence, temperature and surface gravity can, for some stars, be directly inferred from non- (or quasi-) periodic light curves.

astro-ph.IM↗