Test in a beam of large-area Micromegas chambers for sampling calorimetry

Application of Micromegas for sampling calorimetry puts specific constraints on the design and performance of this gaseous detector. In particular, uniform and linear response, low noise and stability against high ionisation density deposits are prerequisites to achieving good energy resolution. A Micromegas-based hadronic calorimeter was proposed for an application at a future linear collider experiment and three technologically advanced prototypes of 1$\times$1 m$^{2}$ were constructed. Their merits relative to the above-mentioned criteria are discussed on the basis of measurements performed at the CERN SPS test-beam facility

Unsupervised Medical Image Translation with Adversarial Diffusion Models

Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation. To capture a direct correlate of the image distribution, SynDiff leverages a conditional diffusion process that progressively maps noise and source images onto the target image. For fast and accurate image sampling during inference, large diffusion steps are taken with adversarial projections in the reverse diffusion direction. To enable training on unpaired datasets, a cycle-consistent architecture is devised with coupled diffusive and non-diffusive modules that bilaterally translate between two modalities. Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation. Our demonstrations indicate that SynDiff offers quantitatively and qualitatively superior performance against competing baselines.Comment: M. Ozbey and O. Dalmaz contributed equally to this stud

Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction

Recent years have witnessed a surge in deep generative models for accelerated MRI reconstruction. Diffusion priors in particular have gained traction with their superior representational fidelity and diversity. Instead of the target transformation from undersampled to fully-sampled data, common diffusion priors are trained to learn a multi-step transformation from Gaussian noise onto fully-sampled data. During inference, data-fidelity projections are injected in between reverse diffusion steps to reach a compromise solution within the span of both the diffusion prior and the imaging operator. Unfortunately, suboptimal solutions can arise as the normality assumption of the diffusion prior causes divergence between learned and target transformations. To address this limitation, here we introduce the first diffusion bridge for accelerated MRI reconstruction. The proposed Fourier-constrained diffusion bridge (FDB) leverages a generalized process to transform between undersampled and fully-sampled data via random noise addition and random frequency removal as degradation operators. Unlike common diffusion priors that use an asymptotic endpoint based on Gaussian noise, FDB captures a transformation between finite endpoints where the initial endpoint is based on moderate degradation of fully-sampled data. Demonstrations on brain MRI indicate that FDB outperforms state-of-the-art reconstruction methods including conventional diffusion priors

MICROMEGAS chambers for hadronic calorimetry at a future linear collider

Prototypes of MICROMEGAS chambers, using bulk technology and analog readout, with 1x1cm2 readout segmentation have been built and tested. Measurements in Ar/iC4H10 (95/5) and Ar/CO2 (80/20) are reported. The dependency of the prototypes gas gain versus pressure, gas temperature and amplification gap thickness variations has been measured with an 55Fe source and a method for temperature and pressure correction of data is presented. A stack of four chambers has been tested in 200GeV/c and 7GeV/c muon and pion beams respectively. Measurements of response uniformity, detection efficiency and hit multiplicity are reported. A bulk MICROMEGAS prototype with embedded digital readout electronics has been assembled and tested. The chamber layout and first results are presented

Large Area Micromegas Chambers with Embedded Front-end Electronics for Hadron Calorimetry

AbstractMicromegas (Micro-mesh gaseous structure) is an attractive technology for applications in particle physics experiments (TPC, calorimeters, muon systems, etc.). The most important results of an extensive R&D program aiming to develop a new generation of a fine-grained hadron calorimeter with low power consumption digital readout using Micromegas chambers as an active element are presented. In 2010, the first large scale prototype of Micromegas chamber with almost 8000 readout channels has been built and tested with high energy particle beams at CERN. The fundamental results, such as detection effciency, hit multiplicity, gain stability, response uniformity and effect of power pulsing of the detector front-end electronics are reported. Eventually, the development and test of the second generation of the large scale prototype with new readout electronics and some important improvements of its mechanical design is described and the prospective towards the construction of a technological prototype of a 4.5 λ deep digital calorimeter for a future linear collider is also given

Recent results of Micromegas sDHCAL with a new readout chip

Calorimetry at future linear colliders could be based on a particle flow approach where granularity is the key to high jet energy resolution. Among different technologies, Micromegas chambers with 1 cm2 pad segmentation are studied for the active medium of a hadronic calorimeter. A chamber of 1 m2 with 9216 channels read out by a low noise front-end ASIC called MICROROC has recently been constructed and tested. Chamber design, ASIC circuitry and preliminary test beam results are reported

Construction and test of a 1×1 m2 Micromegas chamber for sampling hadron calorimetry at future lepton colliders

Equipe MicromegasSampling calorimeters can be finely segmented and used to detect showers with high spatial resolution. This imaging power can be exploited at future linear collider experiments where the measurement of jet energy by a Particle flow method requires optimal use of tracking and calorimeter information. Gaseous detectors can achieve high granularity and a hadron sampling calorimeter using Micromegas chambers as active elements is considered in this paper. Compared to traditional detectors using wires or resistive plates, Micromegas is free of space charge effects and could therefore show superior calorimetric performance. To test this concept, a prototype of 1×1 m2 equipped with 9216 readout pads of 1×1 cm2 has been built. Its technical and basic operational characteristics are reported

Micromegas for imaging hadronic calorimetry

The recent progress in R&D of the Micromegas detectors for hadronic calorimetry including new engineering-technical solutions, electronics development, and accompanying simulation studies with emphasis on the comparison of the physics performance of the analog and digital readout is described. The developed prototypes are with 2 bit digital readout to exploit the Micromegas proportional mode and thus improve the calorimeter linearity. In addition, measurements of detection efficiency, hit multiplicity, and energy shower profiles obtained during the exposure of small size prototypes to radioactive source quanta, cosmic particles and accelerator beams are reported. Eventually, the status of a large scale chamber (1{\times}1 m2) are also presented with prospective towards the construction of a 1 m3 digital calorimeter consisting of 40 such chambers.Comment: 6 pages, 9 figures, CALOR2010 conferenc