Filter and export

Publications

What is a Publication?
42 Publications visible to you, out of a total of 42
Redesign of astrophysical codes for exascale computing: the SPACE experience

Abstract

Not specified

Authors: Giuliano Taffoni, Andrea Mignone, Luca Tornatore, Eva Sciacca, Massimiliano Guarrasi, Giovanni Lapenta, Lubomir Riha, Radim Vavrik, Ondrej Vysocky, Kristian Kadlubiak, Petr Strakos, Milan Jaros, Klaus Dolag, Benoit Commercon, Luciano Rezzolla, Khalil Pierre, Georgios Doulis, Sijing Shen, Manolis Marazakis, Daniele Gregori, Elisabetta Boella, Gino Perna, Marisa Zanotti, Erwan Raffin, Kai Polsterer, Sebastian Trujillo Gomez, Guillermo Marin

Date Published: 18th Jul 2024

Publication Type: Journal

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

Abstract

Not specified

Authors: Kai L. Polsterer, Bernd Doser, Andreas Fehlner, Sebastian Trujillo-Gomez

Date Published: 1st Jun 2024

Publication Type: Journal

Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images

Abstract

Not specified

Authors: A.N. Vantyghem, T.J. Galvin, B. Sebastian, C.P. O’Dea, Y.A. Gordon, M. Boyce, L. Rudnick, K. Polsterer, H. Andernach, M. Dionyssiou, P. Venkataraman, R. Norris, S.A. Baum, X.R. Wang, M. Huynh

Date Published: 1st Apr 2024

Publication Type: Journal

Automatic miscalibration diagnosis: Interpreting probability integral transform (PIT) histograms

Abstract

Not specified

Authors: Ondřej Podsztavek, Alexander I. Jordan, Pavel Tvrdík, Kai L. Polsterer

Date Published: 2024

Publication Type: Journal

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

Abstract

Not specified

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

Date Published: 18th Apr 2023

Publication Type: Journal

Applications of AI in Astronomy

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

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

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 different …rves 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

Powered by
 (v.1.16.0)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH