Intra- and inter-fraction relative range verification in heavy-ion therapy using filtered interaction vertex imaging

Heavy-ion therapy, particularly using scanned (active) beam delivery, provides a precise and highly conformal dose distribution, with maximum dose deposition for each pencil beam at its endpoint (Bragg peak), and low entrance and exit dose. To take full advantage of this precision, robust range verification methods are required; these methods ensure that the Bragg peak is positioned correctly in the patient and the dose is delivered as prescribed. Relative range verification allows intra-fraction monitoring of Bragg peak spacing to ensure full coverage with each fraction, as well as inter-fraction monitoring to ensure all fractions are delivered consistently. To validate the proposed filtered Interaction Vertex Imaging method for relative range verification, a 16O beam was used to deliver 12 Bragg peak positions in a 40 mm poly-(methyl methacrylate) phantom. Secondary particles produced in the phantom were monitored using position-sensitive silicon detectors. Events recorded on these detectors, along with a measurement of the treatment beam axis, were used to reconstruct the sites of origin of these secondary particles in the phantom. The distal edge of the depth distribution of these reconstructed points was determined with logistic fits, and the translation in depth required to minimize the χ2 statistic between these fits was used to compute the range shift between any two Bragg peak positions. In all cases, the range shift was determined with sub-millimeter precision, to a standard deviation of the mean of 220(10) μm. This result validates filtered Interaction Vertex Imaging as a reliable relative range verification method, which should be capable of monitoring each energy step in each fraction of a scanned heavy-ion treatment plan.

Muon detection at FCC-ee

Muons provide a clean experimental signature, typically traversing the whole experimental apparatus without decaying. Muon detection systems are therefore usually located at a rather large distance from the primary interaction vertex after all other sub-detectors. As such, experimental apparatuses at FCC-ee will certainly employ very large muon systems, covering areas of a few thousand square meters. For obvious reasons of cost, the most suitable detectors to realise these large muon systems are gas detectors. In particular, in recent years, micro-pattern gas detectors (MPGDs) have undergone very interesting developments, providing several new types of detectors with very good spatial and time resolution, high efficiency, high rate capability and high radiation tolerance. The good position and time resolution makes a MPGD an excellent particle tracker, reconstructing tracks at 4–5 m from the primary interaction vertex with sub-mm precision. Therefore MPGDs, apart from efficiently detecting muons, can precisely track and help identifying also hypothesized long lived particles (LLP) that would decay outside of the central trackers. MPGDs have the distinct advantage of being, at least for some detectors and some parts of them, mass-producible by industry, since they employ materials and manufacturing procedures that are used extensively for printed circuit boards (PCB) production. A particularly innovative MPGD, the $$\mu $$ μ RWELL, is considered as a possible candidate to build the large muon system of the IDEA detector concept for FCC-ee and is described in some more detail. Other technologies that could be considered for the realisation of muon detection systems are also briefly discussed.

Superspace first order formalism, trivial symmetries and electromagnetic interactions of linearized supergravity

We introduce a first order description of linearized non-minimal (n = −1) supergravity in superspace, using the unconstrained prepotential superfield instead of the conventionally constrained super one forms. In this description, after integrating out the connection-like auxiliary superfield of first-order formalism, the superspace action is expressed in terms of a single superfield which combines the prepotential and compensator superfields. We use this description to construct the supersymmetric cubic interaction vertex 3/2 − 3/2 − 1/2 which describes the electromagnetic interaction between two non-minimal supergravity multiplets (superspin Y = 3/2 which contains a spin 2 and a spin 3/2 particles) and a vector multiplet (superspin Y = 1/2 contains a spin 1 and a spin 1/2 particles). Exploring the trivial symmetries emerging between the two Y = 3/2 supermultiplets, we show that this cubic vertex must depend on the vector multiplet superfield strength. This result generalize previous results for non-supersymmetric electromagnetic interactions of spin 2 particles. The constructed cubic interaction generates non-trivial deformations of the gauge transformations.

Neutrino Vertex Reconstruction in South Pole Ice

Reconstruction of potential ultra-high-energy (UHE) neutrino events at the Askaryan Radio Array (ARA) is complicated by the variable index of refraction of South Pole ice, leading to curved radio signal paths from the interaction vertex. Using a spline table framework for fast raytracing approximation, we perform a GPU-accelerated interferometric reconstruction of the event vertex. We also demonstrate how use of both direct and reflected/refracted radio signals can allow reconstruction of the distance to the interaction vertex, an important step towards neutrino energy reconstruction.

Dense Associative Memory Is Robust to Adversarial Inputs

Deep neural networks (DNNs) trained in a supervised way suffer from two known problems. First, the minima of the objective function used in learning correspond to data points (also known as rubbish examples or fooling images) that lack semantic similarity with the training data. Second, a clean input can be changed by a small, and often imperceptible for human vision, perturbation so that the resulting deformed input is misclassified by the network. These findings emphasize the differences between the ways DNNs and humans classify patterns and raise a question of designing learning algorithms that more accurately mimic human perception compared to the existing methods. Our article examines these questions within the framework of dense associative memory (DAM) models. These models are defined by the energy function, with higher-order (higher than quadratic) interactions between the neurons. We show that in the limit when the power of the interaction vertex in the energy function is sufficiently large, these models have the following three properties. First, the minima of the objective function are free from rubbish images, so that each minimum is a semantically meaningful pattern. Second, artificial patterns poised precisely at the decision boundary look ambiguous to human subjects and share aspects of both classes that are separated by that decision boundary. Third, adversarial images constructed by models with small power of the interaction vertex, which are equivalent to DNN with rectified linear units, fail to transfer to and fool the models with higher-order interactions. This opens up the possibility of using higher-order models for detecting and stopping malicious adversarial attacks. The results we present suggest that DAMs with higher-order energy functions are more robust to adversarial and rubbish inputs than DNNs with rectified linear units.