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RISS 인기검색어
- 검색키워드
- 저자 : Aghamousa, Amir (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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TIME DELAY ANALYSIS OF THE LENSED QUASAR SDSS J1001+5027
Aghamousa, Amir,Shafieloo, Arman American Astronomical Society 2017 The Astrophysical journal Vol.834 No.1
<P>We modify. the algorithm we proposed. in Aghamousa & Shafieloo for. the. time delay. estimation of. strongly lensed systems incorporating the. weighted cross-correlation and weighted summation of correlation coefficients. We show the high performance of this algorithm by applying it to. Time Delay Challenge (TDC1) simulated data. We apply then our proposed method to. the light curves of the lensed quasar SDSS J1001+5027. since this system has been well studied by other groups, to compare our results with their findings. In this work we propose a new estimator, the 'mirror' estimator, along with a list of criteria for reliability testing of the estimation. Our mirror estimator results are. -117.1(-3.7)(+7.1) and -117.1(-8.8)(+7.2) using simple Monte Carlo simulations and simulated light curves provided by Rathna Kumar et al., respectively. Although the. TDC1 simulations do not reflect the properties of the. SDSS J1001+5027 light curves, using these simulations results in a. smaller uncertainty, which shows that the. higher quality observations can lead to a. substantially more precise time delay estimation. Our time delay estimation is in agreement with the. findings of the other groups for this strongly lensed system, and the difference in the size of the error bars reflects the importance of appropriate light curve simulations.</P>
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Aghamousa, Amir,Shafieloo, Arman IOP Publishing 2015 The Astrophysical journal Vol.804 No.1
<P>The observable time delays between multiple images of strong lensing systems with time variable sources can provide us with some valuable information for probing the expansion history of the universe. Estimating these time delays can be very challenging due to complexities in the observed data caused by seasonal gaps, various noises, and systematics such as unknown microlensing effects. In this paper, we introduce a novel approach for estimating the time delays for strong lensing systems, implementing various statistical methods of data analysis including the smoothing and cross-correlation methods. The method we introduce in this paper has recently been used in the TDC0 and TDC1 Strong Lens Time Delay Challenges and has shown its power in providing reliable and precise estimates of time delays dealing with data with different complexities.</P>
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STRONG LENS TIME DELAY CHALLENGE. II. RESULTS OF TDC1
Liao, Kai,Treu, Tommaso,Marshall, Phil,Fassnacht, Christopher D.,Rumbaugh, Nick,Dobler, Gregory,Aghamousa, Amir,Bonvin, Vivien,Courbin, Frederic,Hojjati, Alireza,Jackson, Neal,Kashyap, Vinay,Rathna Ku IOP Publishing 2015 The Astrophysical journal Vol.800 No.1
<P>We present the results of the first strong lens time delay challenge. The motivation, experimental design, and entry level challenge are described in a companion paper. This paper presents the main challenge, TDC1, which consisted of analyzing thousands of simulated light curves blindly. The observational properties of the light curves cover the range in quality obtained for current targeted efforts (e.g., COSMOGRAIL) and expected from future synoptic surveys (e.g., LSST), and include simulated systematic errors. Seven teams participated in TDC1, submitting results from 78 different method variants. After describing each method, we compute and analyze basic statisticsmeasuring accuracy (or bias) A, goodness of fit chi(2), precision P, and success rate f. For some methods we identify outliers as an important issue. Other methods show that outliers can be controlled via visual inspection or conservative quality control. Several methods are competitive, i.e., give vertical bar A vertical bar < 0.03, P < 0.03, and chi(2) < 1.5, with some of the methods already reaching sub-percent accuracy. The fraction of light curves yielding a time delay measurement is typically in the range f = 20%-40%. It depends strongly on the quality of the data: COSMOGRAIL-quality cadence and light curve lengths yield significantly higher f than does sparser sampling. Taking the results of TDC1 at face value, we estimate that LSST should provide around 400 robust time-delay measurements, each with P < 0.03 and vertical bar A vertical bar < 0.01, comparable to current lens modeling uncertainties. In terms of observing strategies, we find that A and f depend mostly on season length, while P depends mostly on cadence and campaign duration.</P>
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