activity
20102024
most citedBlack Hole Mass Estimates Based on CIV are Consistent with Those Based on the Balmer Lines

171 citations · 828 across the 23 of their papers we have counts for

collaborators

26 papers

astro-ph.IM2024

Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations

Kai L. Polsterer, Bernd Doser, Andreas Fehlner +1

Simulations are the best approximation to experimental laboratories in astrophysics and cosmology. However, the complexity, richness, and large size of their outputs severely limit…

astro-ph.IM2023★ 7 cited

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

F. Pozo Nuñez, N. Gianniotis, K. L. Polsterer

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 tradi…

astro-ph.CO2023★ 22 cited

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

F. Pozo Nuñez, C. Bruckmann, S. Desamutara +5

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 di…

cs.LG2022★ 5 cited

Convolutional autoencoders for spatially-informed ensemble post-processing

Sebastian Lerch, Kai L. Polsterer

Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks…

astro-ph.IM2021★ 7 cited

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

N. Gianniotis, F. Pozo Nuñez, K. L. Polsterer

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 photom…

astro-ph.IM2021

From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work

Kai Lars Polsterer, Antonio D'Isanto, Sebastian Lerch

The amount, size, and complexity of astronomical data-sets and databases are growing rapidly in the last decades, due to new technologies and dedicated survey telescopes. Besides d…