Published in

Oxford University Press, Monthly Notices of the Royal Astronomical Society, 4(515), p. 5665-5672, 2022

DOI: 10.1093/mnras/stac2034

Links

Tools

Export citation

Search in Google Scholar

Time-delay estimation in unresolved lensed quasars

Journal article published in 2022 by L. Biggio, A. Domi ORCID, S. Tosi, G. Vernardos ORCID, D. Ricci ORCID, L. Paganin, G. Bracco
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

ABSTRACTTime-delay cosmography can be used to infer the Hubble parameter H0 by measuring the relative time delays between multiple images of gravitationally lensed quasars. A few of such systems have already been used to measure H0: Their time delays were determined from the light curves of the multiple images obtained by regular, years long, monitoring campaigns. Such campaigns can hardly be performed by any telescope: many facilities are often oversubscribed with a large amount of observational requests to fulfill. While the ideal systems for time-delay measurements are lensed quasars whose images are well resolved by the instruments, several lensed quasars have a small angular separation between the multiple images, and would appear as a single, unresolved, image to a large number of telescopes featuring poor angular resolutions or located in not privileged geographical sites. Methods allowing to infer the time delay also from unresolved light curves would boost the potential of such telescopes and greatly increase the available statistics for H0 measurements. This work presents a study of unresolved lensed quasar systems to estimate the time delay using a deep learning-based approach that exploits the capabilities of one-dimensional convolutional neural networks. Experiments on state-of-the-art simulations of unresolved light curves show the potential of the proposed method and pave the way for future applications in time-delay cosmography.