Underground Neutrino Detectors for Particle and Astroparticle Science: the Giant Liquid Argon Charge Imaging ExpeRiment (GLACIER)

The current focus of the CERN program is the Large Hadron Collider (LHC), however, CERN is engaged in long baseline neutrino physics with the CNGS project and supports T2K as recognized CERN RE13, and for good reasons: a number of observed phenomena in high-energy physics and cosmology lack their resolution within the Standard Model of particle physics; these puzzles include the origin of neutrino masses, CP-violation in the leptonic sector, and baryon asymmetry of the Universe. They will only partially be addressed at LHC. A positive measurement of $\sin^22\theta_{13}>0.01$ would certainly give a tremendous boost to neutrino physics by opening the possibility to study CP violation in the lepton sector and the determination of the neutrino mass hierarchy with upgraded conventional super-beams. These experiments (so called ``Phase II'') require, in addition to an upgraded beam power, next generation very massive neutrino detectors with excellent energy resolution and high detection efficiency in a wide neutrino energy range, to cover 1st and 2nd oscillation maxima, and excellent particle identification and $\pi^0$ background suppression. Two generations of large water Cherenkov detectors at Kamioka (Kamiokande and Super-Kamiokande) have been extremely successful. And there are good reasons to consider a third generation water Cherenkov detector with an order of magnitude larger mass than Super-Kamiokande for both non-accelerator (proton decay, supernovae, ...) and accelerator-based physics. On the other hand, a very massive underground liquid Argon detector of about 100 kton could represent a credible alternative for the precision measurements of ``Phase II'' and aim at significantly new results in neutrino astroparticle and non-accelerator-based particle physics (e.g. proton decay).Comment: 31 pages, 14 figure

R&D for Future 100 kton Scale Liquid Argon Detectors

Large liquid argon (LAr) detectors, up to 100 kton scale, are presently being considered for proton decay searches and neutrino astrophysics as well as far detectors for the next generation of long baseline neutrino oscillation experiments, aiming at neutrino mass hierarchy determination and CP violation searches in the leptonic sector. These detectors rely on the possibility of maintaining large LAr masses stably at cryogenic conditions with low thermal losses and of achieving long drifts of the ionization charge, so to minimize the number of readout channels per unit volume. Many R&D initiatives are being undertaken throughout the world, following somewhat different concepts for the final detector design, but with many common basic R&D issues.Comment: Contribution to the Workshop 'European Strategy for Future Neutrino Physics', CERN, Oct. 2009, to appear in the Proceeding

Giant Liquid Argon Observatory for Proton Decay, Neutrino Astrophysics and CP-violation in the Lepton Sector (GLACIER)

GLACIER (Giant Liquid Argon Charge Imaging ExpeRiment) is a large underground observatory for proton decay search, neutrino astrophysics and CP-violation studies in the lepton sector. Possible underground sites are studied within the FP7 LAGUNA project (Europe) and along the JPARC neutrino beam in collaboration with KEK (Japan). The concept is scalable to very large masses.Comment: 4 pages, 1 figure, Contribution to the Workshop "European Strategy for Future Neutrino Physics", CERN, Oct. 200

Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos

We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.Comment: To appear in CVPR 201