Signatures of black holes at the LHC

Signatures of black hole events at CERN's Large Hadron Collider are discussed. Event simulations are carried out with the Fortran Monte Carlo generator CATFISH. Inelasticity effects, exact field emissivities, color and charge conservation, corrections to semiclassical black hole evaporation, gravitational energy loss at formation and possibility of a black hole remnant are included in the analysis.Comment: 13 pages, 7 figure

Brane Decay of a (4+n)-Dimensional Rotating Black Hole. III: spin-1/2 particles

In this work, we have continued the study of the Hawking radiation on the brane from a higher-dimensional rotating black hole by investigating the emission of fermionic modes. A comprehensive analysis is performed that leads to the particle, power and angular momentum emission rates, and sheds light on their dependence on fundamental parameters of the theory, such as the spacetime dimension and angular momentum of the black hole. In addition, the angular distribution of the emitted modes, in terms of the number of particles and energy, is thoroughly studied. Our results are valid for arbitrary values of the energy of the emitted particles, dimension of spacetime and angular momentum of the black hole, and complement previous results on the emission of brane-localised scalars and gauge bosons.Comment: Latex file, JHEP style, 34 pages, 16 figures Energy range in plots increased, minor changes, version published in JHE

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype