The MUSE project face to face with reality

Caillier, Patrick; Accardo, M.; Adjali, L.; Anwand, H.; Bacon, Roland; Boudon, Didier; Brotons, Lluı́s; Capoani, L.; Daguisé, Eric; Dupieux, M.; Dupuy, Christophe; François, M.; Glindemann, Andreas; Gojak, D.; Hansali, G.; Hahn, T.; Jarno, Aurélien; Kelz, Andreas; Koehler, C.; Kosmalski, Johan; Laurent, Florence; Floc’h, M. Le; Lizon, J. L.; Loupias, M.; Manescau, A.; Migniau, Jean-Emmanuel; Monstein, C.; Nicklas, H.; Parès, L.; Pécontal-Rousset, Arlette; Piquéras, Laure; Reiss, Roland; Remillieux, A.; Renault, Edgard; Rupprecht, G.; Streicher, O.; Stuik, Remko; Valentin, Hervé; Vernet, J.; Weilbacher, P.; Zins, G.

doi:10.1117/12.925968

Cited by 5 publications

(4 citation statements)

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“…1(a). These signatures appear as emission lines and in this example have all their maximum at the same spectral channel (50).…”

Section: B Loss Of Performances When Gets Largementioning

confidence: 76%

“…Numerical results presented in this section regard the detection of spectral lines in HSI data that will be acquired by the integral-field spectrograph MUSE (Multi Unit Spectroscopic Explorer) [50], [51]. This instrument will deliver data images of 300 300 pixels at approximately 3600 spectral channels in the visible spectrum.…”

Section: Minimax Detection Of Spectral Profilesmentioning

confidence: 99%

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Dimension Reduction for Hypothesis Testing in Worst-Case Scenarios

Suleiman

Mary

Ferrari

2014

IEEE Trans. Signal Process.

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This paper considers a "one among many" detection problem, where one has to discriminate between pure noise and one among alternatives that are known up to an amplitude factor. Two issues linked to high dimensionality arise in this framework. First, the computational complexity associated to the Generalized Likelihood Ratio (GLR) with the constraint of sparsity-one inflates linearly with , which can be an obstacle when multiple data sets have to be tested. Second, standard procedures based on dictionary learning aimed at reducing the dimensionality may suffer from severe power losses for some alternatives, thus suggesting a worst-case scenario strategy. In the case where the learned dictionary has column, we show that the exact solution of the resulting detection problem, which can be formulated as a minimax problem, can be obtained by Quadratic Programming. Because it allows a better sampling of the diversity of the alternatives, the case is expected to improve the detection performances over the case . The worst-case analysis of this case, which is more involved, leads us to propose two "minimax learning algorithms". Numerical results show that these algorithms indeed allow to increase performances over the case and are in fact comparable to the GLR using the full set of alternatives, while being computationally simpler.

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“…1(a). These signatures appear as emission lines and in this example have all their maximum at the same spectral channel (50).…”

Section: B Loss Of Performances When Gets Largementioning

confidence: 76%

Section: Minimax Detection Of Spectral Profilesmentioning

confidence: 99%

Dimension Reduction for Hypothesis Testing in Worst-Case Scenarios

Suleiman

Mary

Ferrari

2014

IEEE Trans. Signal Process.

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“…As one can see in one of our previous papers, "The MUSE Project Face to Face with Reality" [2], "MUSE instrument global performance test" [4] and "MUSE Optical Alignment Procedure" [5] the status in 2012 was quite critical with a major problem related to the Field Splitting Optics which had to be transferred to another contractor and remanufactured from scratch (and without scratches).…”

Section: June 2012 -A Mountain To Climbmentioning

confidence: 98%

“…It will present and discuss over the critical tasks and the strategy implemented to complete them successfully. It completes in more details the more general papers on the MUSE Project [1], [2] and [3].…”

Section: Introductionmentioning

confidence: 98%

MUSE from Europe to the Chilean Sky

Caillier

Accardo²,

Adjali

et al. 2014

SPIE Proceedings

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MUSE (Multi Unit Spectroscopic Explorer) is a second generation instrument, built for ESO (European SouthernObservatory) and dedicated to the VLT (Very Large Telescope). This instrument is an innovative integral field spectrograph (1x1 arcmin 2 Field of View), operating in the visible wavelength range, from 465 nm to 930 nm. The MUSE project is supported by a European consortium of 7 institutes.After the finalisation of its integration and test in Europe validated by its Preliminary Acceptance in Europe, the MUSE instrument has been partially dismounted and shipped to the VLT (Very Large Telescope) in Chile. From October 2013 till February 2014, it has then been reassembled, tested and finally installed on the telescope its final home. From there it will collect its first photons coming from the outer limit of the visible universe.To come to this achievement, many tasks had to be completed and challenges overcome. These last steps in the project life have certainly been ones of the most critical. Critical in terms of risk, of working conditions, of operational constrains, of schedule and finally critical in terms of outcome: The first light and the final performances of the instrument on the sky.

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