10/31/2022 0 Comments Metabase histogram(2020) require programming expertise.Īn open and user-friendly visualization tool would also allow researchers with limited time (and limited technical skills) to find new correlations in published data. Besides, ROOT, Matplotlib, and another popular Python package Plotly. However, the plots that these tools produce are not interactive. Moreover, open-source packages HEPData-explore ( Maguire et al., 2017) and ROOT ( Brun and Rademakers, 1997) and Python package Matplotlib ( Hunter, 2007) are the most popular in the High-Energy Physics community. There are exciting visualization tools that solve these problems, but these are not open source ( Ahlberg, 1996 Heer et al., 2008 Lachev and Price, 2018), require technical expertise, or are generic tools ( Metabase, 2020 Superset, 2020). To actually visualize data and make a comparison between two datasets, expert knowledge on the creation of those sets is often needed. The problem becomes even more apparent in the combination and comparison of the results of two different research datasets. Let us assume that the hypothetical real physical model has 20 parameters M1–M20. An example of such a one-dimensional exclusion contour and a maximum likelihood interval is given in Figure 1.Ī typical exclusion limit curve (left) and a typical maximum likelihood interval (right) as a function of the two parameters M1 and M2. #Metabase histogram full#In order to visualize high-dimensional data, one can use one- or two-dimensional projections (e.g., some parameter variables have been marginalized or set to best-fit values) or slices of the full space, but this comes at the cost of information contained in the visualization itself. In the field of searches beyond the Standard Model, typically 95% confidence level upper limits are provided i.e., model parameter sets are classified between “excluded” and “allowed.” Finally, the best-fit contours of the models are presented in the form of likelihoods or posterior distributions as functions of a model parameter θ, typically in the form of one- to two-dimensional figures in scientific publications. Predictions of models that describe physics are typically compared to the data using often time-consuming simulations of the underlying physical processes for a large number of model parameter sets. The scientific models investigated were also of low complexity. Traditionally, scientific data are provided by the experiments mainly in the form of one-dimensional histograms and data analysis typically required a comparison of the model to the histogram of data. The models are often complex i.e., they depend on various physical parameters and their interpretation may depend on systematic effects described with the help of additional nuisance parameters. In all these scientific efforts, the exploration of data with the help of physical models plays a key role. Some examples include the investigation of the Higgs boson properties, the search and exclusion of new models for physics beyond the Standard Model at the LHC, the investigation of gravitational waves, or the identification of dark matter. Practically, any research done in modern physics nowadays is based on (simulated) data.
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