Development of a front end ASIC for Dark Matter directional detection with MIMAC

A front end ASIC (BiCMOS-SiGe 0.35 \mum) has been developed within the framework of the MIMAC detector project, which aims at directional detection of non-baryonic Dark Matter. This search strategy requires 3D reconstruction of low energy (a few keV) tracks with a gaseous \muTPC. The development of this front end ASIC is a key point of the project, allowing the 3D track reconstruction. Each ASIC monitors 16 strips of pixels with charge preamplifiers and their time over threshold is provided in real time by current discriminators via two serializing LVDS links working at 320 MHz. The charge is summed over the 16 strips and provided via a shaper. These specifications have been chosen in order to build an auto triggered electronics. An acquisition board and the related software were developed in order to validate this methodology on a prototype chamber. The prototype detector presents an anode where 2 x 96 strips of pixels are monitored.Comment: 12 pages, 10 figure

Weak Lensing of Intensity Mapping: the Cosmic Infrared Background

Gravitational lensing deflects the paths of cosmic infrared background (CIB) photons, leaving a measurable imprint on CIB maps. The resulting statistical anisotropy can be used to reconstruct the matter distribution out to the redshifts of CIB sources. To this end, we generalize the CMB lensing quadratic estimator to any weakly non-Gaussian source field, by deriving the optimal lensing weights. We point out the additional noise and bias caused by the non-Gaussianity and the `self-lensing' of the source field. We propose methods to reduce, subtract or model these non-Gaussianities. We show that CIB lensing should be detectable with Planck data, and detectable at high significance for future CMB experiments like CCAT-Prime. The CIB thus constitutes a new source image for lensing studies, providing constraints on the amplitude of structure at intermediate redshifts between galaxies and the CMB. CIB lensing measurements will also give valuable information on the star formation history in the universe, constraining CIB halo models beyond the CIB power spectrum. By laying out a detailed treatment of lens reconstruction from a weakly non-Gaussian source field, this work constitutes a stepping stone towards lens reconstruction from continuum or line intensity mapping data, such as the Lyman-alpha emission, absorption, and the 21cm radiation.Comment: Accepted in Physical Review

Weak lensing reconstruction through cosmic magnification I: a minimal variance map reconstruction

We present a concept study on weak lensing map reconstruction through the cosmic magnification effect in galaxy number density distribution. We propose a minimal variance linear estimator to minimize both the dominant systematical and statistical errors in the map reconstruction. It utilizes the distinctively different flux dependences to separate the cosmic magnification signal from the overwhelming galaxy intrinsic clustering noise. It also minimizes the shot noise error by an optimal weighting scheme on the galaxy number density in each flux bin. Our method is in principle applicable to all galaxy surveys with reasonable redshift information. We demonstrate its applicability against the planned Square Kilometer Array survey, under simplified conditions. Weak lensing maps reconstructed through our method are complementary to that from cosmic shear and CMB and 21cm lensing. They are useful for cross checking over systematical errors in weak lensing reconstruction and for improving cosmological constraints.Comment: 12 pages, 9 figures, published in MNRA

Neural Face Editing with Intrinsic Image Disentangling

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.Comment: CVPR 2017 ora

Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using MR imaging

To develop an automated approach for 3D quantitative assessment and measurement of alpha angles from the femoral head-neck (FHN) junction using bone models derived from magnetic resonance (MR) images of the hip joint

Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation

In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is required for subsurface visualisation to characterise the state of the tissue. However, scanning of large tissue surfaces in the presence of deformation is a challenging task for the surgeon. Recently, robot-assisted local tissue scanning has been investigated for motion stabilisation of imaging probes to facilitate the capturing of good quality images and reduce the surgeon's cognitive load. Nonetheless, these approaches require the tissue surface to be static or deform with periodic motion. To eliminate these assumptions, we propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue deformation. The 3D structure of the surgical scene is recovered and a feature-based method is proposed to estimate the motion of the tissue in real-time. A desired scanning trajectory is manually defined on a reference frame and continuously updated using projective geometry to follow the tissue motion and control the movement of the robotic arm. The advantage of the proposed method is that it does not require the learning of the tissue motion prior to scanning and can deal with free-form deformation. We deployed this framework on the da Vinci surgical robot using the da Vinci Research Kit (dVRK) for Ultrasound tissue scanning. Since the framework does not rely on information from the Ultrasound data, it can be easily extended to other probe-based imaging modalities.Comment: 7 pages, 5 figures, ICRA 202