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{"references": ["Beyond the Required LISA Free-Fall Performance: New LISA Pathfinder Results down to 20 \u03bcHz, M. Armano et al., Phys. Rev. Lett. 120, 061101 (2018), Supplemental Material, https://link.aps.org/doi/10.1103/PhysRevLett.120.061101.", "Exponential shapelets: basis functions for data analysis of isolated features, J. Berge et al., Mon. Not. Roy. Astron. Soc. 486 (2019) 1, 544-559, arXiv:1903.05837.", "Detection and characterization of instrumental transients in LISA Pathfinder and their projection to LISA, Q. Baghi et al., pre-print, arXiv:2112.07490."]}
LISA Glitch is a Python package that generates glitch files compatible with LISA Instrument and LISANode. A glitch files contain one or more signals, which are injected in the instrumental simulation at various injections points (see below). User manual and documentation are available here.
LISA, Injection, Artifact, Guidance, Glitch
LISA, Injection, Artifact, Guidance, Glitch
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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