29 research outputs found
An X-ray Survey of Galaxies in Pairs
Results are reported from the first survey of X-ray emission from galaxies in
pairs. The sample consists of fifty-two pairs of galaxies from the Catalog of
Paired Galaxies Karachentsev (1972) whose coordinates overlap ROSAT Position
Sensitive Proportional Counter pointed observations. The mean observed log l_x
for early-type pairs is 41.35 +/-0.21 while the mean log l_x predicted using
the l_x-l_b relationship for isolated early-type galaxies is 42.10 +/-0.19.
With 95% confidence, the galaxies in pairs are underluminous in the X-ray,
compared to isolated galaxies, for the same l_b. A significant fraction of the
mixed pair sample also appear similarly underluminous. A spatial analysis shows
that the X-ray emission from pairs of both types typically has an extent of ~10
- 50 kpc, much smaller than group intergalactic medium and thus likely
originates from the galaxies. CPG 564, the most X-ray luminous early-type pair,
4.7x10^42 ergs/sec, is an exception. The extent of it's X-ray emission, >169
kpc, and HWHM, ~80 kpc, is comparable to that expected from an intergalactic
medium. The sample shows only a weak correlation, ~81% confidence, between l_x
and l_b, presumably due to variations in gas content within the galaxies. No
correlation between l_x and the pair velocity difference, separation, or
far-infrared luminosity is found though the detection rate is low, 22%.Comment: 40 pages, 6 jpg figures, ApJ (in press
Analysis of a hadron beam in five-dimensional phase space
We conduct a detailed measurement and analysis of a hadron beam in
five-dimensional phase space at the Spallation Neutron Source Beam Test
Facility. The measurement's resolution and dynamic range are sufficient to
image sharp, high-dimensional features in low-density regions of phase space.
To facilitate the complex task of feature identification in the
five-dimensional phase space, we develop several analysis and visualization
techniques, including non-planar slicing. We use these techniques to examine
the transverse dependence of longitudinal hollowing and longitudinal dependence
of transverse hollowing in the distribution. This analysis strengthens the
claim that low-dimensional projections do not adequately characterize
high-dimensional phase space distributions in low-energy hadron acceleratorsComment: 13 pages; 15 figures; submitted to Physical Review Accelerators and
Beams (PRAB
Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning
Early fault detection and fault prognosis are crucial to ensure efficient and
safe operations of complex engineering systems such as the Spallation Neutron
Source (SNS) and its power electronics (high voltage converter modulators).
Following an advanced experimental facility setup that mimics SNS operating
conditions, the authors successfully conducted 21 fault prognosis experiments,
where fault precursors are introduced in the system to a degree enough to cause
degradation in the waveform signals, but not enough to reach a real fault. Nine
different machine learning techniques based on ensemble trees, convolutional
neural networks, support vector machines, and hierarchical voting ensembles are
proposed to detect the fault precursors. Although all 9 models have shown a
perfect and identical performance during the training and testing phase, the
performance of most models has decreased in the prognosis phase once they got
exposed to real-world data from the 21 experiments. The hierarchical voting
ensemble, which features multiple layers of diverse models, maintains a
distinguished performance in early detection of the fault precursors with 95%
success rate (20/21 tests), followed by adaboost and extremely randomized trees
with 52% and 48% success rates, respectively. The support vector machine models
were the worst with only 24% success rate (5/21 tests). The study concluded
that a successful implementation of machine learning in the SNS or particle
accelerator power systems would require a major upgrade in the controller and
the data acquisition system to facilitate streaming and handling big data for
the machine learning models. In addition, this study shows that the best
performing models were diverse and based on the ensemble concept to reduce the
bias and hyperparameter sensitivity of individual models.Comment: 25 Pages, 13 Figures, 5 Table
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
We present a multi-module framework based on Conditional Variational
Autoencoder (CVAE) to detect anomalies in the power signals coming from
multiple High Voltage Converter Modulators (HVCMs). We condition the model with
the specific modulator type to capture different representations of the normal
waveforms and to improve the sensitivity of the model to identify a specific
type of fault when we have limited samples for a given module type. We studied
several neural network (NN) architectures for our CVAE model and evaluated the
model performance by looking at their loss landscape for stability and
generalization. Our results for the Spallation Neutron Source (SNS)
experimental data show that the trained model generalizes well to detecting
multiple fault types for several HVCM module types. The results of this study
can be used to improve the HVCM reliability and overall SNS uptim
Don’t make me angry, you wouldn’t like me when I’m angry: volitional choices to act or inhibit are modulated by subliminal perception of emotional faces
Volitional action and self-control—feelings of acting according to one’s own intentions and in being control of one’s own actions—are fundamental aspects of human conscious experience. However, it is unknown whether high-level cognitive control mechanisms are affected by socially salient but nonconscious emotional cues. In this study, we manipulated free choice decisions to act or withhold an action by subliminally presenting emotional faces: In a novel version of the Go/NoGo paradigm, participants made speeded button-press responses to Go targets, withheld responses to NoGo targets, and made spontaneous, free choices to execute or withhold the response for Choice targets. Before each target, we presented emotional faces, backwards masked to render them nonconscious. In Intentional trials, subliminal angry faces made participants more likely to voluntarily withhold the action, whereas fearful and happy faces had no effects. In a second experiment, the faces were made supraliminal, which eliminated the effects of angry faces on volitional choices. A third experiment measured neural correlates of the effects of subliminal angry faces on intentional choice using EEG. After replicating the behavioural results found in Experiment 1, we identified a frontal-midline theta component—associated with cognitive control processes—which is present for volitional decisions, and is modulated by subliminal angry faces. This suggests a mechanism whereby subliminally presented “threat” stimuli affect conscious control processes. In summary, nonconscious perception of angry faces increases choices to inhibit, and subliminal influences on volitional action are deep seated and ecologically embedded
Five-dimensional phase space measurement at the Spallation Neutron Source Beam Test Facility
<p>This data set consists of 285,082 two-dimensional images which collectively describe the five-dimensional phase space distribution of a 2.5 MeV, -25.6 mA H ion beam in the <a href="https://neutrons.ornl.gov/sns">Spallation Neutron Source</a> Beam Test Facility (SNS-BTF). Here, and are the transverse positions, and are the transverse slopes, is the position along the reference trajectory, and is the deviation from the kinetic energy of the synchronous particle.</p><p>The measurement plane is located in the medium energy beam transport (MEBT) section of the SNS-BTF, 1.3 meters after a radio-frequency quadrupole (RFQ). The measurement apparatus consists of three transverse slits (one horizontal, two vertical) and a 90-degree dipole bend followed by a fluorescent screen. The horizontal slit selects ; two vertical slits select and ; is a function of and the vertical position on the screen, is a function of , , and the horizontal position on the screen. Thus, the image on the screen gives the density within a small three-dimensional region in space. The five-dimensional density is obtained by scanning the slits in a nested loop.</p><p>The data set consists of 285,082 images (20 GB). Jupyter notebooks are included to interpolate the data on a regular grid in five-dimensional phase space, as well as to generate interactive figures. See 'README.md' for instructions. (The interpolated five-dimensional image is also included in a separate folder.)</p><p>More information can be found in a corresponding publication: https://doi.org/10.1103/PhysRevAccelBeams.26.064202</p>Support:
Austin Hoover ([email protected])
Kiersten Ruisard ([email protected]