Publications

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38 Publications visible to you, out of a total of 38

Abstract

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Authors: F. Pozo Nunez, N. Gianniotis, K.L. Polsterer

Date Published: 18th Apr 2023

Publication Type: Journal

Abstract (Expand)

We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early 1990s and the resulting Terascale data sets, which required automating of many data processing and analysis tasks, for example the star-galaxy separation, with billions of feature vectors in hundreds of dimensions. The exponential data growth continued, with the rise of synoptic sky surveys and the Time Domain Astronomy, with the resulting Petascale data streams and the need for a real-time processing, classification, and decision making. A broad variety of classification and clustering methods have been applied for these tasks, and this remains a very active area of research. Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications of an ever increasing complexity and sophistication. ML and AI are now a standard part of the astronomical toolkit. As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.

Authors: S. G. Djorgovski, Ashish Mahabal, M. J. Graham, Kai L. Polsterer, Alberto Krone-Martins

Date Published: 1st Apr 2023

Publication Type: InCollection

Abstract (Expand)

Abstract 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 differentrves 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.

Authors: F Pozo Nuñez, C Bruckmann, S Desamutara, B Czerny, S Panda, A P Lobban, G Pietrzyński, K L Polsterer

Date Published: 6th Feb 2023

Publication Type: Journal

Abstract (Expand)

Over the past 10 years HiPS (Hierarchical Progressive Surveys) has evolved from an experiment led by CDS to an ecosystem supported by more than 20 data centers exposing their own HiPS node. This trend has been pushed by advanced and simple clients (Aladin Desktop, Aladin Lite) or portals (ESASky, ESO Science Portal) and thanks to Hipsgen. Today the HiPS ecosystem gathers 900 HiPS datasets published by 20+ HiPS nodes. We describe a selection of different tools and services that benefit from having a large collection of multi-wavelength datasets available in the same format: hips2fits, on-the-fly generation of RGB tiles from pre-existing HiPS, HiPS as a container for 1d and 2d histograms, CatTiler, computation on the HiPS grid, generation of Spectral Energy Distribution from FITS tiles.

Authors: Thomas Boch, Mark Allen, Caroline Bot, Pierre Fernique, Matthieu Baumann, Mihaela Buga, Francois Bonnarel, Daniel Durand, Kai Polsterer

Date Published: 1st Jul 2022

Publication Type: InProceedings

Abstract

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Authors: Jan Plier, Matthias Zisler, Jennifer Furkel, Maximilian Knoll, Alexander Marx, Alena Fischer, Kai Polsterer, Mathias H. Konstandin, Stefania Petra

Date Published: 2022

Publication Type: InProceedings

Abstract

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

Date Published: 29th Oct 2021

Publication Type: Journal

Abstract

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Authors: Rafaël I. J. Mostert, Kenneth J. Duncan, Huub J. A. Röttgering, Kai L. Polsterer, Philip N. Best, Marisa Brienza, Marcus Brüggen, Martin J. Hardcastle, Nika Jurlin, Beatriz Mingo, Raffaella Morganti, Tim Shimwell, Dan Smith, Wendy L. Williams

Date Published: 2021

Publication Type: Journal

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