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The Data Processing Pipeline for the MUSE Instrument

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arxiv 2006.08638 v1 pith:2675XSXS submitted 2020-06-15 astro-ph.IM

classification astro-ph.IM
keywords datapipelineprocessingdatacubesdescribefieldintegralmuse
verification ladder T0 review T1 audit T2 compute T3 formal
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Processing of raw data from modern astronomical instruments is nowadays often carried out using dedicated software, so-called "pipelines" which are largely run in automated operation. In this paper we describe the data reduction pipeline of the Multi Unit Spectroscopic Explorer (MUSE) integral field spectrograph operated at ESO's Paranal observatory. This spectrograph is a complex machine: it records data of 1152 separate spatial elements on detectors in its 24 integral field units. Efficiently handling such data requires sophisticated software, a high degree of automation and parallelization. We describe the algorithms of all processing steps that operate on calibrations and science data in detail, and explain how the raw science data gets transformed into calibrated datacubes. We finally check the quality of selected procedures and output data products, and demonstrate that the pipeline provides datacubes ready for scientific analysis.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    astro-ph.GA 2026-07 accept novelty 7.0 of 10

    Lyα haloes around low-luminosity LAEs at z≥6 have exponential scale lengths ~3× smaller than at z~3 under matched intrinsic surface-brightness sensitivity, with stacks of non-detections showing no extended emission at high-z.

  2. Recent Chemo-morphological Coma Evolution of Comet 67P/Churyumov-Gerasimenko

    astro-ph.EP 2025-07 conditional novelty 6.0 of 10

    Simultaneous VLT/MUSE maps of dust, [OI], C2, NH2, and CN in comet 67P's 2021 coma reveal that NH2 and CN are linked to dust fans, with NH2 scale lengths 1.5-1.9 times longer in one region, hinting at extended sources.

  3. Exploring the synergies of $[\mathrm{O\,II}]\lambda 3727$ with MUSE spectroscopy in PHANGS H II regions

    astro-ph.GA 2026-07 unverdicted novelty 5.5 of 10

    Combining [O II] doublet data with MUSE spectra creates a homogeneous H II region catalog and compares strong-line metallicity calibrations, showing low scatter in radial gradients and [S III]/[S II] as a robust ioniz...

  4. Cosmic CORALS: Timing the Universe with high-z star clusters

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    astro-ph.HE 2026-07 conditional novelty 5.0 of 10

    EP250905a is best explained as a mildly off-axis structured-jet afterglow at z=2.714, possibly weakly magnified by a foreground galaxy at z=0.374.

  6. The PyKOALA python library: a multi-instrument package for IFS data reduction

    astro-ph.IM 2025-07 conditional novelty 5.0 of 10

    PyKOALA is a modular, instrument-agnostic Python framework for IFS data reduction, currently applied to KOALA+AAOmega, but lacking quantitative performance validation in this paper.

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