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Cade Rodgers

Publications and source records attributed to Cade Rodgers.

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Determining universal spectra from probability distributions

The probability distribution of a two-particle correlation function computed over background auxiliary field configurations, used to generate the interactions, has been shown to inform about the spectra of universal $n$-body clusters [1]. Here, we utilize two approaches, a numerical lattice computation and an analytic expansion in the limit of large numbers of identical species, in an attempt to refine the initial predictions. Exploratory calculations in these directions are presented, and future investigations laid out.

nucl-th

On the analysis of signal peaks in pulse-height spectra

The estimation of the signal location and intensity of a peak in a pulse height spectrum is important for x-ray and $γ$-ray spectroscopy, charged-particle spectrometry, liquid chromatography, and many other subfields. However, both the "centroid" and "signal intensity" of a peak in a pulse-height spectrum are ill-defined quantities and different methods of analysis will yield different numerical results. Here, we apply three methods of analysis. Method A is based on simple count summation and is likely the technique most frequently applied in practice. The analysis is straightforward and fast, and does not involve any statistical modeling. We find that it provides reliable results only for high signal-to-noise data, but has severe limitations in all other cases. Method B employs a Bayesian model to extract signal counts and centroid from the measured total and background counts. The resulting values are derived from the respective posteriors and, therefore, have a rigorous statistical meaning. The method makes no assumptions about the peak shape. It yields reliable and relatively small centroid uncertainties. However, it provides relatively large signal count uncertainties. Method C makes a strong assumption regarding the peak shape by fitting a Gaussian function to the data. The fit is based again on a Bayesian model. Although Method C requires careful consideration of the Gaussian width (usually given by the detector resolution) used in the fitting, it provides reliable values and relatively small uncertainties both for the signal counts and the centroid.

physics.data-an