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- Abgrall, N., et al.
(författare)
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The large enriched germanium experiment for neutrinoless double beta decay (LEGEND)
- 2017
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Ingår i: AIP Conference Proceedings. - : Author(s). - 1551-7616 .- 0094-243X. ; 1894
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Konferensbidrag (refereegranskat)abstract
- The observation of neutrinoless double-beta decay (0νββ) would show that lepton number is violated, reveal that neu-trinos are Majorana particles, and provide information on neutrino mass. A discovery-capable experiment covering the inverted ordering region, with effective Majorana neutrino masses of 15 - 50 meV, will require a tonne-scale experiment with excellent energy resolution and extremely low backgrounds, at the level of ∼0.1 count /(FWHM·t·yr) in the region of the signal. The current generation 76Ge experiments GERDA and the Majorana Demonstrator, utilizing high purity Germanium detectors with an intrinsic energy resolution of 0.12%, have achieved the lowest backgrounds by over an order of magnitude in the 0νββ signal region of all 0νββ experiments. Building on this success, the LEGEND collaboration has been formed to pursue a tonne-scale 76Ge experiment. The collaboration aims to develop a phased 0νββ experimental program with discovery potential at a half-life approaching or at 1028 years, using existing resources as appropriate to expedite physics results.
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- Boström, Henrik, et al.
(författare)
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Explaining multivariate time series forecasts : An application to predicting the Swedish GDP?
- 2020
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS.
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Konferensbidrag (refereegranskat)abstract
- Various approaches to explaining predictions of black box models have been proposed, including model-agnostic techniques that measure feature importance (or effect) by presenting modified test instances to the underlying black-box model. These modifications rely on choosing feature values from the complete range of observed values. However, when applying machine learning algorithms to the task of forecasting from multivariate time-series, it is suggested that the temporal aspect should be taken into account when analyzing the feature effect. A modification of individual conditional expectation (ICE) plots is proposed, called ICE-T plots, which displays the prediction change for temporally ordered feature values. Results are presented from a case study on predicting the Swedish gross domestic product (GDP) based on a comprehensive set of indicator and prognostic variables. The effect of calculating feature effect with and without temporal constraints is demonstrated, as well as the impact of transformations and forecast horizons on what features are found to have a large effect, and the use of ICE-T plots as a complement to ICE plots.
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