Cast-as-Intended Mechanism with Return Codes Based on PETs

We propose a method providing cast-as-intended verifiability for remote electronic voting. The method is based on plaintext equivalence tests (PETs), used to match the cast ballots against the pre-generated encrypted code tables. Our solution provides an attractive balance of security and functional properties. It is based on well-known cryptographic building blocks and relies on standard cryptographic assumptions, which allows for relatively simple security analysis. Our scheme is designed with a built-in fine-grained distributed trust mechanism based on threshold decryption. It, finally, imposes only very little additional computational burden on the voting platform, which is especially important when voters use devices of restricted computational power such as mobile phones. At the same time, the computational cost on the server side is very reasonable and scales well with the increasing ballot size

First Demonstration of a Pixelated Charge Readout for Single-Phase Liquid Argon Time Projection Chambers

Liquid Argon Time Projection Chambers (LArTPCs) have been selected for the future long-baseline Deep Underground Neutrino Experiment (DUNE). To allow LArTPCs to operate in the high-multiplicity near detector environment of DUNE, a new charge readout technology is required. Traditional charge readout technologies introduce intrinsic ambiguities, combined with a slow detector response, these ambiguities have limited the performance of LArTPCs, until now. Here, we present a novel pixelated charge readout that enables the full 3D tracking capabilities of LArTPCs. We characterise the signal to noise ratio of charge readout chain, to be about 14, and demonstrate track reconstruction on 3D space points produced by the pixel readout. This pixelated charge readout makes LArTPCs a viable option for the DUNE near detector complex.Comment: 13 pages, 9 figure

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Power tests of a string of magnets comprising a full cell of the Superconducting Super Collider

In this paper we describe the operation and testing of a string of magnets comprising a full cell of the Superconducting Super Collider (SSC). The full cell configuration composed of ten dipoles, two quadrupoles, and three spool pieces is the longest SSC magnet string ever tested. Although the tests of the full cell were undertaken after the SSC project was marked for termination, their completion was deemed necessary and useful to future efforts at other accelerator laboratories utilizing Superconducting magnets. The focus of this work is on the electrical and cryogenic performance of the string components and the quench protection system with an emphasis on solving some of the questions concerning electrical performance raised during the previous two experimental runs involving a half cell configuration