Search for the light dark matter with an X-ray spectrometer

Sterile neutrinos with the mass in the keV range are interesting warm dark matter (WDM) candidates. The restrictions on their parameters (mass and mixing angle) obtained by current X-ray missions (XMM-Newton or Chandra) can only be improved by less than an order of magnitude in the near future. Therefore the new strategy of search is needed. We compare the sensitivities of existing and planned X-ray missions for the detection of WDM particles with the mass ~1-20 keV. We show that existing technology allows an improvement in sensitivity by a factor of 100. Namely, two different designs can achieve such an improvement: [A] a spectrometer with the high spectral resolving power of 0.1%, wide (steradian) field of view, with small effective area of about cm^2 (which can be achieved without focusing optics) or [B] the same type of spectrometer with a smaller (degree) field of view but with a much larger effective area of 10^3 cm^2 (achieved with the help of focusing optics). To illustrate the use of the "type A" design we present the bounds on parameters of the sterile neutrino obtained from analysis of the data taken by an X-ray microcalorimeter. In spite of the very short exposure time (100 sec) the derived bound is comparable to the one found from long XMM-Newton observation.Comment: 9pp, revtex

Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network

3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with rich semantic texture information from images at the cost of extra network designs and overhead. In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference. Our key design is to first exploit the potential instructive semantic knowledge within the ground-truth labels by training a semantic-painted teacher model and then guide the pure-lidar network to learn the semantic-painted representation via knowledge passing modules at different granularities: class-wise passing, pixel-wise passing and instance-wise passing. Experimental results show that the proposed SPNet can seamlessly cooperate with most existing 3D detection frameworks with 1~5% AP gain and even achieve new state-of-the-art 3D detection performance on the KITTI test benchmark. Code is available at: https://github.com/jb892/SPNet.Comment: Accepted by ACMMM202