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license: gpl-3.0

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 2 = 0.106%

Noise level 3 = 0.466% Noise level 3 = 0.466%

Underlying fuction 1 Underlying fuction 1

Underlying fuction 2 Underlying fuction 2

Underlying fuction 3 Underlying fuction 3

Underlying fuction 4 Underlying fuction 4

Underlying fuction 5 Underlying fuction 5

Underlying fuction 6 Underlying fuction 6

Underlying fuction 7 Underlying fuction 7

Underlying fuction 8 Underlying fuction 8

Underlying fuction 9 Underlying fuction 9

Underlying fuction 10 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}
}