7,084 research outputs found
Seed populations for large solar particle events of cycle 23
Using high-resolution mass spectrometers on board the Advanced Composition Explorer (ACE), we surveyed the event-averaged ~0.1-60 MeV/nuc heavy ion elemental composition in 64 large solar energetic particle (LSEP) events of cycle 23. Our results show the following: (1) The rare isotope ^3He is greatly enhanced over the corona or the solar wind values in 46% of the events. (2) The Fe/O ratio decreases with increasing energy up to ~10 MeV/nuc in ~92% of the events and up to ~60 MeV/nuc in ~64% of the events. (3) Heavy ion abundances from C-Fe exhibit systematic M/g-dependent enhancements that are remarkably similar to those seen in ^3He-rich SEP events and CME-driven interplanetary (IP) shock events. Taken together, these results confirm the role of shocks in energizing particles up to ~60 MeV/nuc in the majority of large SEP events of cycle 23, but also show that the seed population is not
dominated by ions originating from the ambient corona or the thermal solar wind, as previously
believed. Rather, it appears that the source material for CME-associated large SEP events
originates predominantly from a suprathermal population with a heavy ion enrichment pattern
that is organized according to the ion's mass-per-charge ratio. These new results indicate that
current LSEP models must include the routine production of this dynamic suprathermal seed
population as a critical pre-cursor to the CME shock acceleration process
The role of interplanetary scattering in western hemisphere large solar energetic particle events
Using high-sensitivity instruments on the ACE spacecraft, we have examined the intensities of O and Fe in 14 large solar energetic particle events whose parent activity was in the solar western hemisphere. Sampling the intensities at low (~273 keV nucleon to the -1) and high (~12 MeV nucleon to the -1) energies, we find that at the same kinetic energy per nucleon, the Fe/O ratio decreases with time, as has been reported previously. This behavior is seen in more than 70% of the cases during the rise to maximum intensity and continues in most cases into the decay phase. We find that for most events if we compare the Fe intensity with the O intensity at a higher kinetic energy per nucleon, the two time-intensity profiles are strikingly similar. Examining alternate scenarios that could produce this behavior, we conclude that for events showing this behavior the most likely explanation is that the Fe and O share similar injection profiles near the Sun, and that scattering in the interplanetary medium dominates the profiles observed at 1 AU
Squeezed light for advanced gravitational wave detectors and beyond
Recent experiments have demonstrated that squeezed vacuum states can be injected into gravitational wave detectors to improve their sensitivity at detection frequencies where they are quantum noise limited. Squeezed states could be employed in the next generation of more sensitive advanced detectors currently under construction, such as Advanced LIGO, to further push the limits of the observable gravitational wave Universe. To maximize the benefit from squeezing, environmentally induced disturbances such as back scattering and angular jitter need to be mitigated. We discuss the limitations of current squeezed vacuum sources in relation to the requirements imposed by future gravitational wave detectors, and show a design for squeezed light injection which overcomes these limitations
The effect of RO3201195 and a pyrazolyl ketone P38 MAPK inhibitor library on the proliferation of Werner syndrome cells
No description supplie
Genetic-algorithm-optimized neural networks for gravitational wave classification
Gravitational-wave detection strategies are based on a signal analysis
technique known as matched filtering. Despite the success of matched filtering,
due to its computational cost, there has been recent interest in developing
deep convolutional neural networks (CNNs) for signal detection. Designing these
networks remains a challenge as most procedures adopt a trial and error
strategy to set the hyperparameter values. We propose a new method for
hyperparameter optimization based on genetic algorithms (GAs). We compare six
different GA variants and explore different choices for the GA-optimized
fitness score. We show that the GA can discover high-quality architectures when
the initial hyperparameter seed values are far from a good solution as well as
refining already good networks. For example, when starting from the
architecture proposed by George and Huerta, the network optimized over the
20-dimensional hyperparameter space has 78% fewer trainable parameters while
obtaining an 11% increase in accuracy for our test problem. Using genetic
algorithm optimization to refine an existing network should be especially
useful if the problem context (e.g. statistical properties of the noise, signal
model, etc) changes and one needs to rebuild a network. In all of our
experiments, we find the GA discovers significantly less complicated networks
as compared to the seed network, suggesting it can be used to prune wasteful
network structures. While we have restricted our attention to CNN classifiers,
our GA hyperparameter optimization strategy can be applied within other machine
learning settings.Comment: 25 pages, 8 figures, and 2 tables; Version 2 includes an expanded
discussion of our hyperparameter optimization mode
How Common is Energetic ^3He in the Inner Heliosphere?
Using data from the SIS and ULEIS instruments on the Advanced Composition Explorer (ACE) we have identified
periods during which energetic ^3He is present in near-Earth interplanetary space between November 1997 and May 2002. The data, which cover the energy intervals 0.2–1 MeV/nuc (ULEIS) and 4.5–16.3 MeV/nuc (SIS), show that ^3He is present a significant fraction of the time, as would be required if these suprathermal particles were the major source of the ^3He being accelerated by shocks in the interplanetary medium. Specifically, we find that energetic ^3He is present at least ~ 60% of the time, and perhaps significantly more often
Surface Hydrogen Modeling of Super Soft X-ray Sources: Are They Supernova Ia Progenitors?
Nova explosions occur on the white dwarf (WD) component of a Cataclysmic
Variable stellar system which is accreting matter lost by a companion. A Type
Ia supernova explosion is thought to result when a WD, in a similar binary
configuration, grows in mass to the Chandrasekhar Limit. Here, we present
calculations of accretion of Solar matter, at a variety of mass accretion
rates, onto hot (K), luminous (30L), massive
(1.25M, 1.35M) Carbon-Oxygen WDs. In contrast to our nova
simulations where the WD has a low initial luminosity and a thermonuclear
runaway (TNR) occurs and ejects material, these simulations do not eject
material (or only a small fraction of the accreted material) and the WD grows
in mass. A hydrogen TNR does not occur because hydrogen fuses to helium in the
surface layers, and we call this process Surface Hydrogen Burning (SHB). As the
helium layer grows in mass, it gradually fuses either to carbon and oxygen or
to more massive nuclei depending on the WD mass and mass accretion rate. If
such a WD were to explode in a SN Ia event, therefore, it would show neither
hydrogen nor helium in its spectrum as is observed. Moreover, the luminosities
and effective temperatures of our simulations agree with the observations of
some of the Super Soft X-ray Binary Sources and, therefore, our results
strengthen previous speculation that some of them (CAL 83 and CAL 87 for
example) are probably progenitors of SN Ia explosions. Finally, we have
achieved SHB for values of the mass accretion rate that almost span the
observed values of the Cataclysmic Variables.Comment: Accepted by APJL, 4 pages, 1 figure, LaTex (uses emulateapj.sty
A Historical Perspective Leading Up to and Including the United Nations Conference on Straddling Fish Stocks and Highly Migratory Fish Stocks
The detailed method of optimal regions
The detailed method of optimal regions is an extended form of the method of optimal regions which has been found effective in solving the personnel classification problem when the number of job categories is small. The automatic determination of the successive values of the v i , made possible by the more exact techniques of the detailed method, provide easier solutions for the more complex problems and provide solutions, which, for the most part, can be mechanized. In a sense the detailed method of optimal regions is more than a detailed form of the method of optimal regions. It is essentially a method of transformations by which the original matrix is reduced to a matrix from which the solution is easily obtained.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45700/1/11336_2005_Article_BF02289208.pd
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