Dylan Hsu

This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches. A prioritized, compact set of benchmark models is proposed, accompanied by studies of the parameter space of these models and a repository of generator implementations. This report also addresses how to apply the Effective Field Theory formalism for collider searches and present the results of such interpretations.

Patients with brain metastases (BMs) are surviving longer and returning for multiple courses of stereotactic radiosurgery. BMs are monitored after radiation with follow-up magnetic resonance (MR) imaging every 2-3 months. This study investigated whether it is possible to automatically track BMs on longitudinal imaging and quantify the tumor response after radiotherapy.The METRO process (MEtastasis Tracking with Repeated Observations was developed to automatically process patient data and track BMs. A longitudinal intrapatient registration method for T1 MR post-Gd was conceived and validated on 20 patients. Detections and volumetric measurements of BMs were obtained from a deep learning model. BM tracking was validated on 32 separate patients by comparing results with manual measurements of BM response and radiologists' assessments of new BMs. Linear regression and residual analysis were used to assess accuracy in determining tumor response and size change.A total of 123 irradiated BMs and 38 new BMs were successfully tracked. 66 irradiated BMs were visible on follow-up imaging 3-9 months after radiotherapy. Comparing their longest diameter changes measured manually vs. METRO, the Pearson correlation coefficient was 0.88 (p < 0.001); the mean residual error was -8 ± 17%. The mean registration error was 1.5 ± 0.2 mm.Automatic, longitudinal tracking of BMs using deep learning methods is feasible. In particular, the software system METRO fulfills a need to automatically track and quantify volumetric changes of BMs prior to, and in response to, radiation therapy.

The performance of prototype active-edge VTT sensors bump-bonded to the Timepix ASIC is presented. Non-irradiated sensors of thicknesses 100–200 μm and pixel-to-edge distances of 50 μm and 100 μm were probed with a beam of charged hadrons with sub-pixel precision using the Timepix telescope assembled at the SPS at CERN. The sensors are shown to be highly efficient up to a few micrometers from the physical edge of the sensor. The distortion of the electric field lines at the edge of the sensors is studied by reconstructing the streamlines of the electric field using two-pixel clusters. These results are supported by TCAD simulations. The reconstructed streamlines are used to study the field distortion as a function of the bias voltage and to apply corrections to the cluster positions at the edge.

Silicon micropattern devices are crucial components of detector systems designed to study decays of exotic subatomic particles containing beauty and charm quarks. Among the technologies under consideration for use in future particle physics experiments are edgeless silicon pixel detectors. In these devices a state-of-the-art fabrication process is used to create sensors with a nearly full active area, as compared to conventional sensors which have a “guard ring” which is a dead region at the sensor periphery. Prototypes used for the study described in this paper were designed and fabricated by VTT Technical Research Centre of Finland. In a test beam study, we find that these devices perform in accordance with expectations and fulfill the technical needs of their intended implementation. This active edge technology is indeed efficient in maximizing the useful area of the sensor. More broadly, these devices meet the needs of a detector for particle physics, and may also find a role in medical imaging or X-ray spectroscopy.