Monitoring the Thermal Power of Nuclear Reactors with a Prototype Cubic Meter Antineutrino Detector

In this paper, we estimate how quickly and how precisely a reactor's operational status and thermal power can be monitored over hour to month time scales, using the antineutrino rate as measured by a cubic meter scale detector. Our results are obtained from a detector we have deployed and operated at 25 meter standoff from a reactor core. This prototype can detect a prompt reactor shutdown within five hours, and monitor relative thermal power to three percent within seven days. Monitoring of short-term power changes in this way may be useful in the context of International Atomic Energy Agency's (IAEA) Reactor Safeguards Regime, or other cooperative monitoring regimes.Comment: 10 pages, 9 figure

A comparative study of nonparametric methods for pattern recognition

The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal

A Linear Programming Approach to Sequential Hypothesis Testing

Under some mild Markov assumptions it is shown that the problem of designing optimal sequential tests for two simple hypotheses can be formulated as a linear program. The result is derived by investigating the Lagrangian dual of the sequential testing problem, which is an unconstrained optimal stopping problem, depending on two unknown Lagrangian multipliers. It is shown that the derivative of the optimal cost function with respect to these multipliers coincides with the error probabilities of the corresponding sequential test. This property is used to formulate an optimization problem that is jointly linear in the cost function and the Lagrangian multipliers and an be solved for both with off-the-shelf algorithms. To illustrate the procedure, optimal sequential tests for Gaussian random sequences with different dependency structures are derived, including the Gaussian AR(1) process.Comment: 25 pages, 4 figures, accepted for publication in Sequential Analysi

Keep Ballots Secret: On the Futility of Social Learning in Decision Making by Voting

We show that social learning is not useful in a model of team binary decision making by voting, where each vote carries equal weight. Specifically, we consider Bayesian binary hypothesis testing where agents have any conditionally-independent observation distribution and their local decisions are fused by any L-out-of-N fusion rule. The agents make local decisions sequentially, with each allowed to use its own private signal and all precedent local decisions. Though social learning generally occurs in that precedent local decisions affect an agent's belief, optimal team performance is obtained when all precedent local decisions are ignored. Thus, social learning is futile, and secret ballots are optimal. This contrasts with typical studies of social learning because we include a fusion center rather than concentrating on the performance of the latest-acting agents

Quantum secret sharing based on modulated high-dimensional time-bin entanglement

We propose a new scheme for quantum secret sharing (QSS) that uses a modulated high-dimensional time-bin entanglement. By modulating the relative phase randomly by {0,pi}, a sender with the entanglement source can randomly change the sign of the correlation of the measurement outcomes obtained by two distant recipients. The two recipients must cooperate if they are to obtain the sign of the correlation, which is used as a secret key. We show that our scheme is secure against intercept-and-resend (I-R) and beam splitting attacks by an outside eavesdropper thanks to the non-orthogonality of high-dimensional time-bin entangled states. We also show that a cheating attempt based on an I-R attack by one of the recipients can be detected by changing the dimension of the time bin entanglement randomly and inserting two "vacant" slots between the packets. Then, cheating attempts can be detected by monitoring the count rate in the vacant slots. The proposed scheme has better experimental feasibility than previously proposed entanglement-based QSS schemes.Comment: To appear in Phys. Rev.