Simulated Light Curves for Strong Gravitational Lensing

This mock dataset simulates light curves for strong gravitational lensing (optical data). Each file contains pairs of light curves with a time delay of five days (DS-5).

Table: Simulated Large Scale Data Sets

Noise	Gap Size 0	Gap Size 1	Gap Size 2	Gap Size 3	Gap Size 4	Gap Size 5
0%	1	10	10	10	10	10
0.036%	50	50	50	50	50	50
0.106%	50	50	50	50	50	50
0.466%	50	50	50	50	50	50
Sub-Total	151	1510	1510	1510	1510	1510

Total = 7,701 data sets per underlying function.
5 underlying functions yield 38,505 data sets.

File Naming Convention

Files follow this notation:

DS-5-<Function>-GAP-<GapSize>-<Realization>-N-<NoiseLevel>

Explanation of Components:

DS-5: Indicates that the true time delay is five days.
Function (<Function>): Represents the underlying function number (ranges from 1 to 10).
GAP (<GapSize>): Indicates the number of removed points:
- 0: No gap.
- 1-5: Different gap sizes.
Realization (<Realization>): Represents different random gap realizations (ranges from 1 to 10).
N (<NoiseLevel>): Represents the noise level:
- 0: No noise.
- 1-3: Different noise levels.

Example

DS-5-1-GAP-0-1-N-0

DS-5: True time delay of five days.
1: First underlying function.
GAP-0: No gaps.
1: First realization.
N-0: No noise.

Each pair of light curves is divided into five blocks before applying the gap procedure.

Plots

Noise level 2 = 0.106%

Noise level 3 = 0.466%

Underlying fuction 1

Underlying fuction 2

Underlying fuction 3

Underlying fuction 4

Underlying fuction 5

Underlying fuction 6

Underlying fuction 7

Underlying fuction 8

Underlying fuction 9

Underlying fuction 10

If you use this dataset, please cite the following paper:

Cuevas-Tello, J. C., Tiňo, P., Raychaudhury, S., Yao, X., & Harva, M.
Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application.
Pattern Recognition, 43(3), 1165-1179 (2010).
DOI: 10.1016/j.patcog.2009.07.016

BibTeX Citation

@article{CUEVASTELLO20101165,
  title = {Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application},
  author = {Juan C. Cuevas-Tello and Peter Tiňo and Somak Raychaudhury and Xin Yao and Markus Harva},
  journal = {Pattern Recognition},
  volume = {43},
  number = {3},
  pages = {1165-1179},
  year = {2010},
  issn = {0031-3203},
  doi = {10.1016/j.patcog.2009.07.016}
}