2016
DOI: 10.1093/mnras/stw2951
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Adaptive PSF fitting – a highly performing photometric method and light curves of the GLS H1413+117: time delays and micro-lensing effects

Abstract: We present new photometric observations of H1413+117 acquired during seasons between 2001 and 2008 in order to estimate the time delays between the lensed quasar images and to characterize at best the on-going micro-lensing events. We propose a highly performing photometric method called the adaptive point spread function fitting and have successfully tested this method on a large number of simulated frames. This has enabled us to estimate the photometric error bars affecting our observational results. We anal… Show more

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Cited by 10 publications
(9 citation statements)
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References 46 publications
(75 reference statements)
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“…If the time-resolved survey, in our case CRTS, had enough spatial resolution to derive two independent lightcurves, the detection of a possible multiply imaged quasar would be trivial. If the sampling is sufficient, a simple comparison of the lightcurves using some distance metric would reveal that they are likely the same lightcurve except for minor effects such as microlensing (e.g., Wambsganss & Paczynski 1991;Chae et al 2001;Akhunov et al 2017). Although microlensing can harm inferences of H 0 , it cannot prevent the identification of the two timeseries as realizations of the same underlying time-series, as expected from a gravitational lens.…”
Section: Lightcurve Entropy Selection Principlementioning
confidence: 99%
“…If the time-resolved survey, in our case CRTS, had enough spatial resolution to derive two independent lightcurves, the detection of a possible multiply imaged quasar would be trivial. If the sampling is sufficient, a simple comparison of the lightcurves using some distance metric would reveal that they are likely the same lightcurve except for minor effects such as microlensing (e.g., Wambsganss & Paczynski 1991;Chae et al 2001;Akhunov et al 2017). Although microlensing can harm inferences of H 0 , it cannot prevent the identification of the two timeseries as realizations of the same underlying time-series, as expected from a gravitational lens.…”
Section: Lightcurve Entropy Selection Principlementioning
confidence: 99%
“…A cross-correlation analysis of the RATCam light curves of the four images of QSO B1413+117 yielded three independent delays, which were also used to improve the lens solution for this system and to estimate the previously unknown lens redshift (Goicoechea & Shalyapin 2010). The mass model consisted of an SIE (main lensing galaxy), a singular isothermal sphere (secondary lensing galaxy), and external shear (MacLeod et al 2009), and we derived a lens redshift of z = 1.88 +0.09 −0.11 (1σ interval; see also Akhunov et al 2017). Additionally, from OSIRIS spectroscopy of field objects in the external shear direction, we identified an emission line galaxy at z ∼ 0.57 that is responsible for < 2% of γ ∼ 0.1 (Shalyapin & Goicoechea 2013).…”
Section: Lensing Mass Distributionsmentioning
confidence: 99%
“…The measurements of the time delays between the lensed images provide estimations of possible redshifts of the lensing galaxy, z lens ∼ 1.88−1.95 (e.g. Goicoechea & Shalyapin 2010;Akhunov et al 2017). Here we adopt the redshift of lens z lens = 1.90.…”
Section: Resultsmentioning
confidence: 99%
“…Kayser et al 1990;Goicoechea & Shalyapin 2010;Akhunov et al 2017). The measurements of the time delays with the Sloan Digital-Sky Survey improved the lens model for quasar H1413+1143 and provided estimations of possible redshifts of the lensing galaxy: z lens = 1.88 +0.09 −0.11 (Goicoechea & Shalyapin 2010) and 1.95 +0.06 −0.10 (Akhunov et al 2017). The time-delay measurements imply a possible redshift of the lensing galaxy: z lens ∼ 1.9.…”
Section: Gravitationally Lensing Galaxymentioning
confidence: 89%
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