5,831 research outputs found
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 ÎĽm) 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
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