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Register a new userMaximum Likelihood Spectrum Decomposition for Isotope Identification and Quantification
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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.
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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 of mixture model. While the BNNs build infinite mixture models and are derived via variational inference, the latter two build finite mixtures trained with the maximum likelihood method. In this work we investigate the effect of training an infinite mixture distribution with the maximum likelihood method instead of variational inference. We find that the proposed objective leads to stochastic networks with an increased predictive variance, which improves uncertainty based identification of miss-classification and robustness against adversarial attacks in comparison to a standard BNN with equivalent network structure. The new model also displays higher entropy on out-of-distribution data.
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 it into a fully working computational framework for large-scale polarization analysis, adopting WMAP as a worked example. We compare five different linear bases (pixel space, harmonic space, noise covariance eigenvectors, signal-to-noise covariance eigenvectors and signal-plus-noise covariance eigenvectors) in terms of compression efficiency, and find that the computationally most efficient basis is the signal-to-noise eigenvector basis, which is closely related to the Karhunen-Loeve and Principal Component transforms, in agreement with previous suggestions. For this basis, the information in 6836 unmasked WMAP sky map pixels can be compressed into a smaller set of 3102 modes, with a maximum error increase of any single multipole of 3.8% at $ellle32$, and a maximum shift in the mean values of a joint distribution of an amplitude--tilt model of 0.006$sigma$. This compression reduces the computational cost of a single likelihood evaluation by a factor of 5, from 38 to 7.5 CPU seconds, and it also results in a more robust likelihood by implicitly regularizing nearly degenerate modes. Finally, we use the same compression framework to formulate a numerically stable and computationally efficient variation of the Quadratic Maximum Likelihood implementation that requires less than 3 GB of memory and 2 CPU minutes per iteration for $ell le 32$, rendering low-$ell$ QML CMB power spectrum analysis fully tractable on a standard laptop.
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 method for achieving precise Br{ho} determination has been developed using the experimentally derived ion-optical transfer matrix elements from the measured position and ToF information. Using the high-performance detectors and the precise Br{ho} determination method, the fragments produced by the fragmentation of 78Kr at 300 MeV/nucleon were identified clearly at the RIBLL2-ETF under full momentum acceptance. The atomic number Z resolution of {sigma}Z~0.19 and the mass-to-charge ratio A/Q resolution of {sigma}A/Q~5.8e-3 were obtained for the 75As33+ fragment. This great improvement will increase the collection efficiency of exotic nuclei, extend the range of nuclei of interest from the A<40 mass region up to the A~80 mass region, and promote the development of radioactive nuclear beam experiments at the RIBLL2 separator.
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 the ability to detect extremely weak signals when compared to direct photometric methods. We report the realization of direct spectral molecular detection using a silicon nanophotonic waveguide resonator, obviating dispersive spectral acquisition. We use a thermally tunable silicon ring resonator with a transmission spectrum matched and cross-correlated to the quasi-periodic vibronic absorption lines of hydrogen cyanide. We show that the correlation peak amplitude is proportional to the number of overlapping ring resonances and gas lines, and that molecular specificity is obtained from the phase of the correlation signal in a single detection channel. Our results demonstrate on-chip correlation spectroscopy that is less restricted by the signal-to-noise penalty of other spectroscopic approaches, enabling the detection of faint spectral signatures.
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 that the state of the node at a particular time instant is influenced by the states of the corresponding parental nodes in the previous time instant. The associated edge weights determine the corresponding level of influence from each parental node. In the simplest setup, the Bernoulli parameter of a particular nodes state variable is a convex combination of the parental node states in the previous time instant and an additional Bernoulli noise random variable. This paper focuses on the problem of edge weight identification using Maximum Likelihood (ML) estimation and proves that the ML estimator is strongly consistent for two variants of the BAR model. We additionally derive closed-form estimators for the aforementioned two variants and prove their strong consistency.
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