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A spectral decomposition method has been adapted to identify and quantify isotopic source terms in high resolution gamma-ray spectroscopy in scenarios with static geometry and shielding. Monte-Carlo simulations were used to build the response matrix of a shielded high purity germanium detector monitoring an effluent stream with a Marinelli configuration. The decomposition technique was applied to a series of calibration spectra taken with the detector using a multi-nuclide standard. These results are compared to decay corrected values from the calibration certificate. For most nuclei in the standard ($^{241}$Am, $^{109}$Cd, $^{137}$Cs, and $^{60}$Co) the deviations from the certificate values were generally no more than $6$% with a few outliers as high as $12$%. For $^{57}$Co the deviations from the standard reached as high as $25$%, driven by the very low statistics of the sources presence in the calibration spectra. Additionally, a full treatment of error propagation for the technique is presented.
Uncertainty quantification in neural networks gained a lot of attention in the past years. The most popular approaches, Bayesian neural networks (BNNs), Monte Carlo dropout, and deep ensembles have one thing in common: they are all based on some kind
We revisit the problem of exact CMB likelihood and power spectrum estimation with the goal of minimizing computational cost through linear compression. This idea was originally proposed for CMB purposes by Tegmark et al. (1997), and here we develop i
To improve the ability of particle identification of the RIBLL2 separator at the HIRFL-CSR complex, a new high-performance detector for measuring fragment starting time and position at the F1 dispersive plane has been constructed and installed, and a
Absorption spectroscopy is widely used in sensing and astronomy to understand molecular compositions on microscopic to cosmological scales. However, typical dispersive spectroscopic techniques require multichannel detection, fundamentally limiting th
We consider the problem of identifying parameters of a particular class of Markov chains, called Bernoulli Autoregressive (BAR) processes. The structure of any BAR model is encoded by a directed graph. Incoming edges to a node in the graph indicate t