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Optically read out gaseous detectors are used in track reconstruction and imaging applications requiring high granularity images. Among resolution-determining factors, the amplification stage plays a crucial role and optimisations of detector geometry are pursued to maximise spatial resolution. To compare MicroPattern Gaseous Detector (MPGD) technologies, focused low-energy X-ray beams at the SOLEIL synchrotron facility were used to record and extract point spread function widths with Micromegas and GEM detectors. Point spread function width of $\approx$108\,\microns for Micromegas and $\approx$127\,\microns for GEM foils were extracted. The scanning of the beam with different intensities, energies and across the detector active region can be used to quantify resolution-limiting factors and improve imaging detectors using MPGD amplification stages., Comment: 22 pages, 17 figures

The PICOSEC Micromegas detector is a precise-timing gaseous detector based on a Cherenkov radiator coupled with a semi-transparent photocathode and a Micromegas amplifying structure, targeting a time resolution of tens of picoseconds for minimum ionising particles. Initial single-pad prototypes have demonstrated a time resolution below 25 ps, prompting ongoing developments to adapt the concept for applications. The achieved performance is being transferred to robust multi-channel detector modules suitable for large-area detection systems requiring excellent timing precision. To enhance the robustness and stability of the PICOSEC Micromegas detector, research on robust carbon-based photocathodes, including Diamond-Like Carbon (DLC) and Boron Carbide (B4C), is pursued. Results from prototypes equipped with DLC and B4C photocathodes exhibited a time resolution of approximately 32 ps and 34.5 ps, respectively. Efforts dedicated to improve detector robustness and stability enhance the feasibility of the PICOSEC Micromegas concept for large experiments, ensuring sustained performance while maintaining excellent timing precision.

The PICOSEC Micromegas (MM) detector is a precise timing gaseous detector based on a MM detector operating in a two-stage amplification mode and a Cherenkov radiator. Prototypes equipped with cesium iodide (CsI) photocathodes have shown promising time resolutions as precise as 24 picoseconds (ps) for Minimum Ionizing Particles. However, due to the high hygroscopicity and susceptibility to ion bombardment of the CsI photocathodes, alternative photocathode materials are needed to improve the robustness of PICOSEC MM. Diamond-like Carbon (DLC) film have been introduced as a novel robust photocathode material, which have shown promising results. A batch of DLC photocathodes with different thicknesses were produced and evaluated using ultraviolet light. The quantum efficiency measurements indicate that the optimized thickness of the DLC photocathode is approximately 3 nm. Furthermore, DLC photocathodes show good resistance to ion bombardment in aging test compared to the CsI photocathode. Finally, a PICOSEC MM prototype equipped with DLC photocathodes was tested in muon beams. A time resolution of around 42 ps with a detection efficiency of 97% for 150 GeV/c muons were obtained. These results indicate the great potential of DLC as a photocathode for the PICOSEC MM detector.

Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable of operating in real-time on high resolution images, attributing to their widespread usage across many fields. We present an end-to-end object detection pipeline designed for real-time rare event searches for the Migdal effect, using high-resolution image data from a state-of-the-art scientific CMOS camera in the MIGDAL experiment. The Migdal effect in nuclear scattering, crucial for sub-GeV dark matter searches, has yet to be experimentally confirmed, making its detection a primary goal of the MIGDAL experiment. Our pipeline employs the YOLOv8 object detection algorithm and is trained on real data to enhance the detection efficiency of nuclear and electronic recoils, particularly those exhibiting overlapping tracks that are indicative of the Migdal effect. When deployed online on the MIGDAL readout PC, we demonstrate our pipeline to process and perform the rare event search on 2D image data faster than the peak 120 frame per second acquisition rate of the CMOS camera. Applying these same steps offline, we demonstrate that we can reduce a sample of 20 million camera frames to around 1000 frames while maintaining nearly all signal that YOLOv8 is able to detect, thereby transforming a rare search into a much more manageable search. Our studies highlight the potential of pipelines similar to ours significantly improving the detection capabilities of experiments requiring rapid and precise object identification in high-throughput data environments.

This paper presents design guidelines and experimental verification of a single-channel PICOSEC Micromegas (MM) detector with an improved time resolution. The design encompasses the detector board, vessel, auxiliary mechanical parts, and electrical connectivity for high voltage (HV) and signals, focusing on improving stability, reducing noise, and ensuring signal integrity to optimize timing performance. A notable feature is the simple and fast reassembly procedure, facilitating quick replacement of detector internal components that allows for an efficient measurement strategy involving different detector components. The paper also examines the influence of parasitics on the output signal integrity. To validate the design, a prototype assembly and three interchangeable detector boards with varying readout pad diameters were manufactured. The detectors were initially tested in the laboratory environment. Finally, the timing performance of detectors with different pad sizes was verified using a Minimum Ionizing Particle (MIP) beam test. Notably, a record time resolution for a PICOSEC Micromegas detector technology with a CsI photocathode of 12.5$\pm$0.8 ps was achieved with a 10 mm diameter readout pad size detector.

Gas Electron Multipliers (GEMs) are used in many particle physics experiments, employing their 'standard' configuration with amplification holes of 140 um pitch in a hexagonal pattern. However, the collection of the charge cloud from the primary ionisation electrons from the drift region of the detector into the GEM holes affects the position information from the initial interacting particle. In this paper, the results from studies with a triple-GEM detector with an X-Y-strip readout anode are presented. It is demonstrated that GEMs with a finer hole pitch of here 90 um improve the detector's spatial resolution. Within these studies, also the impact of the front-end electronics on the spatial resolution was investigated, which is briefly discussed in the paper.

Digital Behavior Change Interventions (DBCIs) are supporting development of new health behaviors. Evaluating their effectiveness is crucial for their improvement and understanding of success factors. However, comprehensive guidance for developers, particularly in small-scale studies with ethical constraints, is limited. Building on the CAPABLE project, this study aims to define effective engagement with DBCIs for supporting cancer patients in enhancing their quality of life. We identify metrics for measuring engagement, explore the interest of both patients and clinicians in DBCIs, and propose hypotheses for assessing the impact of DBCIs in such contexts. Our findings suggest that clinician prescriptions significantly increase sustained engagement with mobile DBCIs. In addition, while one weekly engagement with a DBCI is sufficient to maintain well-being, transitioning from extrinsic to intrinsic motivation may require a higher level of engagement.

The proposed new Electron-Ion Collider poses a technical and intellectual challenge for the detector design to accommodate the long-term diverse physics goals envisaged by the program. This requires a 4{\pi} detector system capable of reconstructing the energy and momentum of final state particles with high precision. The Electron-Ion Collider also requires identification of particles of different masses over a wide momentum range. A diverse spectrum of Particle Identification detectors has been proposed. Of the four types of detectors for hadron identification, three are based on Ring Imaging Cherenkov Counter technologies, and one is realized by the Time of Flight method. The quest for a novel photocathode, sensitive in the far vacuum ultra violet wavelength range and more robust than cesium iodide, motivated an R&D programme to explore nano-diamond (ND) based photocathodes, started by a collaboration between INFN and CNR Bari and INFN Trieste. Systematic measurements of the photo emission in different Ar-CH4 and Ar-CO2 gas mixtures with various types of ND powders and Hydrogenated ND (H-ND) powders are reported. A first study of the response of THGEMs coated with different photocathode materials is presented. The progress of this R&D programme and the results obtained so far by these exploratory studies are described., Comment: arXiv admin note: substantial text overlap with arXiv:2401.17485, arXiv:2006.02352

Micro-Pattern Gaseous Detectors (MPGDs) with resistive anode planes provide intrinsic discharge robustness while maintaining good spatial and time resolutions. Typically read out with 1D strips or pad structures, here the characterisation results of resistive anode plane MPGDs with 2D strip readout are presented. A uRWELL prototype is investigated in view of its use as a reference tracking detector in a future gaseous beam telescope. A MicroMegas prototype with a fine-pitch mesh (730 line-pairs-per-inch) is investigated, both for comparison and to profit from the better field uniformity and thus the ability to operate the detector more stable at high gains. Furthermore, the measurements are another application of the RD51 VMM3a/SRS electronics.

Automatic detection and tracking of emotional states has the potential for helping individuals with various mental health conditions. While previous studies have captured physiological signals using wearable devices in laboratory settings, providing valuable insights into the relationship between physiological responses and mental states, the transfer of these findings to real-life scenarios is still in its nascent stages. Our research aims to bridge the gap between laboratory-based studies and real-life settings by leveraging consumer-grade wearables and self-report measures. We conducted a preliminary study involving 15 healthy participants to assess the efficacy of wearables in capturing user valence in real-world settings. In this paper, we present the initial analysis of the collected data, focusing primarily on the results of valence classification. Our findings demonstrate promising results in distinguishing between high and low positive valence, achieving an F1 score of 0.65. This research opens up avenues for future research in the field of mobile mental health interventions., Comment: Accepted for MobileHCI 2023

The next generation of gaseous photon detectors is requested to overcome the limitations of the available technology, in terms of resolution and robustness. The quest for a novel photocathode, sensitive in the far vacuum ultra violet wavelength range and more robust than present ones, motivated an R&D programme to explore nanodiamond based photoconverters, which represent the most promising alternative to cesium iodine. A procedure for producing the novel photocathodes has been defined and applied on THGEMs samples. Systematic measurements of the photo emission in different Ar/CH4 and Ar/CO2 gas mixtures with various types of nanodiamond powders have been performed. A comparative study of the response of THGEMs before and after coating demonstrated their full compatibility with the novel photocathodes.

We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI). Our proposed system predicts whether a patient will perform the targeted behaviour and uses counterfactual examples with feature control to guide personalisation of BCI. We use simulated patient data with varying levels of receptivity to intervention to arrive at the study design which would enable evaluation of our system., Comment: 8 pages,3 figures, Knowledge Representation for Health Care (HR4HC) 2023

As demonstrated by the ATLAS New Small Wheel community with their MicroMegas (MM) design, resistive electrodes are now used in different detector types within the Micro Pattern Gaseous Detector family to improve their robustness or performance. The extended form of the Ramo-Shockley theorem for conductive media has been applied to a 1 M$\Omega$/$\Box$ 2D resistive strip bulk MM to calculate the signal's spreading over neighbouring channels using an 80 GeV/c muon track. For this geometry, the dynamic weighting potential was obtained numerically using a finite element solver by applying a junction condition and coordinate scaling technique to accurately represent the boundary conditions of a $10\times 10$ cm$^2$ active area. Using test beam measurements, the results of this model will be used to benchmark this microscopic modelling methodology for signal induction in resistive particle detectors., Comment: There are 6 pages and 4 figures. Proceeding for the 7th International Conference on Micro-Pattern Gaseous Detectors, 11-16 December 2022, Rehovot, Israel

The PICOSEC Micromegas precise timing detector is based on a Cherenkov radiator coupled to a semi-transparent photocathode and a Micromegas amplification structure. The first proof of concept single-channel small area prototype was able to achieve time resolution below 25 ps. One of the crucial aspects in the development of the precise timing gaseous detectors applicable in high-energy physics experiments is a modular design that enables large area coverage. The first 19-channel multi-pad prototype with an active area of approximately 10 cm$^2$ suffered from degraded timing resolution due to the non-uniformity of the preamplification gap. A new 100 cm$^2$ detector module with 100 channels based on a rigid hybrid ceramic/FR4 Micromegas board for improved drift gap uniformity was developed. Initial measurements with 80 GeV/c muons showed improvements in timing response over measured pads and a time resolution below 25 ps. More recent measurements with a new thinner drift gap detector module and newly developed RF pulse amplifiers show that the resolution can be enhanced to a level of 17~ps. This work will present the development of the detector from structural simulations, design, and beam test commissioning with a focus on the timing performance of a thinner drift gap detector module in combination with new electronics using an automated timing scan method.

The PICOSEC Micromegas (MM) detector is a precise timing gaseous detector consisting of a Cherenkov radiator combined with a photocathode and a MM amplifying structure. A 100-channel non-resistive PICOSEC MM prototype with 10x10 cm^2 active area equipped with a Cesium Iodide (CsI) photocathode demonstrated a time resolution below 18 ps. The objective of this work is to improve the PICOSEC MM detector robustness aspects; i.e. integration of resistive MM and carbon-based photocathodes; while maintaining good time resolution. The PICOSEC MM prototypes have been tested in laboratory conditions and successfully characterised with 150 GeV/c muon beams at the CERN SPS H4 beam line. The excellent timing performance below 20 ps for an individual pad obtained with the 10x10 cm^2 area resistive PICOSEC MM of 20 MOhm/sq showed no significant time resolution degradation as a result of adding a resistive layer. A single-pad prototype equipped with a 12 nm thick Boron Carbide (B4C) photocathode presented a time resolution below 35 ps; opening up new possibilities for detectors with robust photocathodes. The results made the concept more suitable for the experiments in need of robust detectors with good time resolution.

Removing ambiguities within a single stage becomes crucial when one can not use multiple detectors behind each other to resolve them which naturally is the case for neutral radiation. An example would be RICH detectors. Commonly pixilated readout is choosen for this purpose. However, this causes a remarkable increase in quantity of channels and does not scale up well. Therefore, the XYU-GEM was proposed as a three coordinate strip-readout which is combined with a triple GEM detector. The readout complements a common XY readout with an additional projection which is tilted by 45{\deg}. The overdetermination due to three projections can be used to resovle ambiguities. Following the detector design will be explained, first measurements discussed to understand the response of the detector and a way to change the charge sharing without changing the manufacturing parameters of the readout.

The RD51 collaboration maintains a common infrastructure at CERN for its R & D activities, including two beam telescopes for test beam campaigns. Recently, one of the beam telescopes has been equipped and commissioned with new multi-channel and charge-sensitive front-end electronics based on the ATLAS/BNL VMM3a front-end ASIC and the RD51 Scalable Readout System (SRS). This allows to read out the detectors at high rates (up to the MHz regime) with electronics time resolutions of the order of 1 ns and the ability to handle different detector types and sizes, due to a larger dynamic range compared to the previous front-end electronics based on the APV25 ASIC. Having studied and improved the beam telescope's performance over the course of three test beam campaigns, the results are presented in this paper.

The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals. The data from 11 participants is utilized to train a machine learning model to detect CL. Subsequently, we create two versions of surveys: a gamified and a traditional one. We estimate the CL experienced by other participants (13) while completing surveys. We find that CL detector performance can be enhanced via pre-training on stress detection tasks. For 10 out of 13 participants, a personalized CL detector can achieve an F1 score above 0.7. We find no difference between the gamified and non-gamified surveys in terms of CL but participants prefer the gamified version., Comment: Accepted for AMIA 2023

Timing information in current and future accelerator facilities is important for resolving objects (particle tracks, showers, etc.) in extreme large particles multiplicities on the detection systems. The PICOSEC Micromegas detector has demonstrated the ability to time 150\,GeV muons with a sub-25\,ps precision. Driven by detailed simulation studies and a phenomenological model which describes stochastically the dynamics of the signal formation, new PICOSEC designs were developed that significantly improve the timing performance of the detector. PICOSEC prototypes with reduced drift gap size ($\sim$\SI{119}{\micro\metre}) achieved a resolution of 45\,ps in timing single photons in laser beam tests (in comparison to 76\,ps of the standard PICOSEC detector). Towards large area detectors, multi-pad PICOSEC prototypes with segmented anodes has been developed and studied. Extensive tests in particle beams revealed that the multi-pad PICOSEC technology provides also very precise timing, even when the induced signal is shared among several neighbouring pads. Furthermore, new signal processing algorithms have been developed, which can be applied during data acquisition and provide real time, precise timing., Comment: 5 pages, 3 figures, 12th International Conference on Position Sensitive Detectors

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to regulate blood exchange among twins. The procedure is particularly challenging due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation. Computer-assisted intervention (CAI) can provide surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision challenge, we released the first largescale multicentre TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-centre fetoscopic data, we provide a benchmark for future research in this field., Comment: Accepted at MedIA (Medical Image Analysis)

Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos. Approach. We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated. Main results. We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model. Significance. We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries., Comment: Published at Physics in Medicine & Biology

Designing theory-driven digital health interventions is a challenging task that needs support. We created a guide for the incomers in the field on how to design digital health interventions with case studies from the Cancer Better Life Experience (CAPABLE) European project. The guide explains how behaviour change theories can inform customisation and personalisation of the intervention. The proposed SATO (ideaS expAnded wiTh bciO) design workflow is based on the IDEAS (Integrate, Design, Assess, and Share) framework and is aligned with the Behaviour Change Intervention Ontology (BCIO). We provide a checklist of the activities that should be performed during intervention planning as well as app design templates which bundle together relevant behaviour change techniques. In the process of creating this guide, we found the necessity to extend the BCIO to support the scenarios of multiple clinical goals in the same application. The extension utilizes existing classes and properties where possible., Comment: 31 pages, 6 figures

Federated learning enables building a shared model from multicentre data while storing the training data locally for privacy. In this paper, we present an evaluation (called CXR-FL) of deep learning-based models for chest X-ray image analysis using the federated learning method. We examine the impact of federated learning parameters on the performance of central models. Additionally, we show that classification models perform worse if trained on a region of interest reduced to segmentation of the lung compared to the full image. However, focusing training of the classification model on the lung area may result in improved pathology interpretability during inference. We also find that federated learning helps maintain model generalizability. The pre-trained weights and code are publicly available at (https://github.com/SanoScience/CXR-FL)., Comment: Accepted at International Conference on Computational Science (ICCS) 2022, London

This paper will review the origins, development, and examples of new versions of Micro-Pattern Gas Detectors. The goal for MPGD development was the creation of detectors that could cost-effectively cover large areas while offering excellent position and timing resolution, and the ability to operate at high incident particle rates. The early MPGD developments culminated in the formation of the RD51 collaboration which has become the critical organization for the promotion of MPGDs and all aspects of their production, characterization, simulation, and uses in an expanding array of experimental configurations. For the Snowmass 2021 study, a number of Letters of Interest were received that illustrate ongoing developments and expansion of the use of MPGDs. In this paper, we highlight high precision timing, high rate application, trigger capability expansion of the SRS readout system, and a structure designed for low ion backflow., Comment: Contribution to Snowmass 2021

Recoil imaging entails the detection of spatially resolved ionization tracks generated by particle interactions. This is a highly sought-after capability in many classes of detector, with broad applications across particle and astroparticle physics. However, at low energies, where ionization signatures are small in size, recoil imaging only seems to be a practical goal for micro-pattern gas detectors. This white paper outlines the physics case for recoil imaging, and puts forward a decadal plan to advance towards the directional detection of low-energy recoils with sensitivity and resolution close to fundamental performance limits. The science case covered includes: the discovery of dark matter into the neutrino fog, directional detection of sub-MeV solar neutrinos, the precision study of coherent-elastic neutrino-nucleus scattering, the detection of solar axions, the measurement of the Migdal effect, X-ray polarimetry, and several other applied physics goals. We also outline the R&D programs necessary to test concepts that are crucial to advance detector performance towards their fundamental limit: single primary electron sensitivity with full 3D spatial resolution at the $\sim$100 micron-scale. These advancements include: the use of negative ion drift, electron counting with high-definition electronic readout, time projection chambers with optical readout, and the possibility for nuclear recoil tracking in high-density gases such as argon. We also discuss the readout and electronics systems needed to scale-up such detectors to the ton-scale and beyond., Comment: 77 pages, 20 figures. Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021)