The Electrostatic Ion Beam Trap : a mass spectrometer of infinite mass range

We study the ions dynamics inside an Electrostatic Ion Beam Trap (EIBT) and show that the stability of the trapping is ruled by a Hill's equation. This unexpectedly demonstrates that an EIBT, in the reference frame of the ions works very similar to a quadrupole trap. The parallelism between these two kinds of traps is illustrated by comparing experimental and theoretical stability diagrams of the EIBT. The main difference with quadrupole traps is that the stability depends only on the ratio of the acceleration and trapping electrostatic potentials, not on the mass nor the charge of the ions. All kinds of ions can be trapped simultaneously and since parametric resonances are proportional to the square root of the charge/mass ratio the EIBT can be used as a mass spectrometer of infinite mass range

On the Origin of the Dark Gamma-Ray Bursts

The origin of dark bursts - i.e. that have no observed afterglows in X-ray, optical/NIR and radio ranges - is unclear yet. Different possibilities - instrumental biases, very high redshifts, extinction in the host galaxies - are discussed and shown to be important. On the other hand, the dark bursts should not form a new subgroup of long gamma-ray bursts themselves.Comment: published in Nuovo Ciment

Principal-Component Analysis of Gamma-Ray Bursts’ Spectra

The principal-component analysis is a statistical method, which lowers the number of important variables in a data set. The use of this method for the bursts’ spectra and afterglows is discussed in this paper. The analysis indicates that three principal components are enough among the eight ones to describe the variablity of the data. The correlation between the spectral index α and the redshift suggests that the thermal emission component becomes more dominant at larger redshifts

OH far-infrared emission from low- and intermediate-mass protostars surveyed with Herschel-PACS

OH is a key species in the water chemistry of star-forming regions, because its presence is tightly related to the formation and destruction of water. This paper presents OH observations from 23 low- and intermediate-mass young stellar objects obtained with the PACS integral field spectrometer on-board Herschel in the context of the Water In Star-forming Regions with Herschel (WISH) key program. Most low-mass sources have compact OH emission (< 5000 AU scale), whereas the OH lines in most intermediate-mass sources are extended over the whole PACS detector field-of-view (> 20000 AU). The strength of the OH emission is correlated with various source properties such as the bolometric luminosity and the envelope mass, but also with the OI and H2O emission. Rotational diagrams for sources with many OH lines show that the level populations of OH can be approximated by a Boltzmann distribution with an excitation temperature at around 70 K. Radiative transfer models of spherically symmetric envelopes cannot reproduce the OH emission fluxes nor their broad line widths, strongly suggesting an outflow origin. Slab excitation models indicate that the observed excitation temperature can either be reached if the OH molecules are exposed to a strong far-infrared continuum radiation field or if the gas temperature and density are sufficiently high. Using realistic source parameters and radiation fields, it is shown for the case of Ser SMM1 that radiative pumping plays an important role in transitions arising from upper level energies higher than 300 K. The compact emission in the low-mass sources and the required presence of a strong radiation field and/or a high density to excite the OH molecules points towards an origin in shocks in the inner envelope close to the protostar.Comment: Accepted for publication in Astronomy and Astrophysics. Abstract abridge

The negatively charged nitrogen-vacancy centre in diamond: the electronic solution

The negatively charged nitrogen-vacancy centre is a unique defect in diamond that possesses properties highly suited to many applications, including quantum information processing, quantum metrology, and biolabelling. Although the unique properties of the centre have been extensively documented and utilised, a detailed understanding of the physics of the centre has not yet been achieved. Indeed there persists a number of points of contention regarding the electronic structure of the centre, such as the ordering of the dark intermediate singlet states. Without a sound model of the centre's electronic structure, the understanding of the system's unique dynamical properties can not effectively progress. In this work, the molecular model of the defect centre is fully developed to provide a self consistent model of the complete electronic structure of the centre. The application of the model to describe the effects of electric, magnetic and strain interactions, as well as the variation of the centre's fine structure with temperature, provides an invaluable tool to those studying the centre and a means to design future empirical and ab initio studies of this important defect.Comment: 24 pages, 6 figures, 10 table

Strong reinforcement effects in 2D cellulose nanofibril-graphene oxide (CNF-GO) nanocomposites due to GO-induced CNF ordering

Nanocomposites from native cellulose with low 2D nanoplatelet content are of interest as sustainable materials combining functional and structural performance. Cellulose nanofibril-graphene oxide (CNF-GO) nanocomposite films are prepared by a physical mixing-drying method, with a focus on low GO content, the use of very large GO platelets (2-45 μm) and nanostructural characterization using synchrotron X-ray source for WAXS and SAXS. These nanocomposites can be used as transparent coatings, strong films or membranes, as gas barriers or in laminated form. CNF nanofibrils with random in-plane orientation, form a continuous non-porous matrix with GO platelets oriented in-plane. GO reinforcement mechanisms in CNF are investigated, and relationships between nanostructure and suspension rheology, mechanical properties, optical transmittance and oxygen barrier properties are investigated as a function of GO content. A much higher modulus reinforcement efficiency is observed than in previous polymer-GO studies. The absolute values for modulus and ultimate strength are as high as 17 GPa and 250 MPa at a GO content as small as 0.07 vol%. The remarkable reinforcement efficiency is due to improved organization of the CNF matrix; and this GO-induced mechanism is of general interest for nanostructural tailoring of CNF-2D nanoplatelet composites

Hom-quantum groups I: quasi-triangular Hom-bialgebras

We introduce a Hom-type generalization of quantum groups, called quasi-triangular Hom-bialgebras. They are non-associative and non-coassociative analogues of Drinfel'd's quasi-triangular bialgebras, in which the non-(co)associativity is controlled by a twisting map. A family of quasi-triangular Hom-bialgebras can be constructed from any quasi-triangular bialgebra, such as Drinfel'd's quantum enveloping algebras. Each quasi-triangular Hom-bialgebra comes with a solution of the quantum Hom-Yang-Baxter equation, which is a non-associative version of the quantum Yang-Baxter equation. Solutions of the Hom-Yang-Baxter equation can be obtained from modules of suitable quasi-triangular Hom-bialgebras.Comment: 21 page

Self-Supervised Relative Depth Learning for Urban Scene Understanding

As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth. It is natural for a learning agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, faraway mountains don't move much; nearby trees move a lot. This natural relationship between the appearance of objects and their motion is a rich source of information about the world. In this work, we start by training a deep network, using fully automatic supervision, to predict relative scene depth from single images. The relative depth training images are automatically derived from simple videos of cars moving through a scene, using recent motion segmentation techniques, and no human-provided labels. This proxy task of predicting relative depth from a single image induces features in the network that result in large improvements in a set of downstream tasks including semantic segmentation, joint road segmentation and car detection, and monocular (absolute) depth estimation, over a network trained from scratch. The improvement on the semantic segmentation task is greater than those produced by any other automatically supervised methods. Moreover, for monocular depth estimation, our unsupervised pre-training method even outperforms supervised pre-training with ImageNet. In addition, we demonstrate benefits from learning to predict (unsupervised) relative depth in the specific videos associated with various downstream tasks. We adapt to the specific scenes in those tasks in an unsupervised manner to improve performance. In summary, for semantic segmentation, we present state-of-the-art results among methods that do not use supervised pre-training, and we even exceed the performance of supervised ImageNet pre-trained models for monocular depth estimation, achieving results that are comparable with state-of-the-art methods