Hough Transform Proposal and Simulations for Particle Track Recognition for LHC Phase-II Upgrade

In the near future, LHC experiments will continue future upgrades by overcoming the technological obsolescence of the detectors and the readout capabilities. Therefore, after the conclusion of a data collection period, CERN will have to face a long shutdown to improve overall performance, by updating the experiments, and implementing more advanced technologies and infrastructures. In particular, the largest LHC experiment, i.e., ATLAS, will upgrade parts of the detector, the trigger, and the data acquisition system. In addition, the ATLAS experiment will complete the implementation of new strategies, algorithms for data handling, and transmission to the final storage apparatus. This paper presents an overview of an upgrade planned for the second half of this decade for the ATLAS experiment. In particular, we show a study of a novel pattern recognition algorithm used in the trigger system, which is a device designed to provide the information needed to select physical events from unnecessary background data. The idea is to use a well known mathematical transform, the Hough transform, as the algorithm for the detection of particle trajectories. The effectiveness of the algorithm has already been validated in the past, regardless of particle physics applications, to recognize generic shapes within images. On the contrary, here, we first propose a software emulation tool, and a subsequent hardware implementation of the Hough transform, for particle physics applications. Until now, the Hough transform has never been implemented on electronics in particle physics experiments, and since a hardware implementation would provide benefits in terms of overall Latency, we complete the studies by comparing the simulated data with a physical system implemented on a Xilinx hardware accelerator (FELIX-II card). In more detail, we have implemented a low-abstraction RTL design of the Hough transform on Xilinx UltraScale+ FPGAs as target devices for filtering applications.

Proposal of a New Criterion for Electrodiagnosis of Meralgia Paresthetica by Evoked Potentials

We examined 19 subjects with meralgia paresthetica (bilateral in three cases), recording bilateral somatosensory-evoked potentials (SSEPs) after stimulation of the tibial posterior nerve (TPN) and cutaneous stimulation in the region of the lateral femoral cutaneous nerve (LFCN). We calculated the difference between TPN SSEPs and LFCN SSEPs cortical potentials, identifying a temporal parameter that we termed D(SEP). We defined D(SEP) normal values in a control group. D(SEP) evaluation showed good sensitivity and specificity (85.7% and 82.4%, respectively; accuracy, 83.3%) in discriminating affected limbs from unaffected. The main advantage of this method is to disengage from the necessity of contralateral comparison of LFCN recordings, joined with a reduction of interindividual variability of LFCN SSEPs amplitude and latency that often causes a lower sensitivity of other methods. As an interesting consideration, D(SEP) evaluation appears to mark out a possible subclinical involvement of LFCN in the asymptomatic side of patients with meralgia paresthetica.