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The Performance of Photometric Reverberation Mapping at High Redshift and the Reliability of Damped Random Walk Models
Authors
G Gürkan
M Jarvis
+3 more
MJ Jarvis
SC Read
DJB Smith
Publication date
1 January 2019
Publisher
'Oxford University Press (OUP)'
Doi
View
on
arXiv
Abstract
This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©2019 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Accurate methods for reverberation mapping using photometry are highly sought after since they are inherently less resource intensive than spectroscopic techniques. However, the effectiveness of photometric reverberation mapping for estimating black hole masses is sparsely investigated at redshifts higher than
z
≈
0.04
z\approx0.04
z
≈
0.04
. Furthermore, photometric methods frequently assume a Damped Random Walk (DRW) model, which may not be universally applicable. We perform photometric reverberation mapping using the Javelin photometric DRW model for the QSO SDSSJ144645.44+625304.0 at z=0.351 and estimate the H
β
\beta
β
lag of
6
5
−
1
+
6
65^{+6}_{-1}
6
5
−
1
+
6
days and black-hole mass of
10
8.2
2
−
0.15
+
0.13
M
⊙
10^{8.22^{+0.13}_{-0.15}}M_{\odot}
1
0
8.2
2
−
0.15
+
0.13
M
⊙
. An analysis of the reliability of photometric reverberation mapping, conducted using many thousands of simulated CARMA process light-curves, shows that we can recover the input lag to within 6 per cent on average given our target's observed signal-to-noise of > 20 and an average cadence of 14 days (even when DRW is not applicable). Furthermore, we use our suite of simulated light curves to deconvolve aliases and artefacts from our QSO's posterior probability distribution, increasing the signal-to-noise on the lag by a factor of
∼
2.2
\sim2.2
∼
2.2
. We exceed the signal-to-noise of the Sloan Digital Sky Survey Reverberation Mapping Project (SDSS-RM) campaign with a quarter of the observing time per object, resulting in a
∼
200
\sim200
∼
200
per cent increase in SNR efficiency over SDSS-RM.Peer reviewedFinal Accepted Versio
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