100 research outputs found
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 11 m 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 1Ă—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 1Ă—1 m2 equipped with 9216 readout pads of 1Ă—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
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