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Register a new userThe Answer is Blowing in the Wind
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Added by
Yousaf Mahmood Butt
Publication date
2007
fields
Physics
and research's language is
English
Authors
Yousaf Butt
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Measurements of the velocity of interstellar HeI inside of the heliosphere have been conducted over the past forty years. These historical data suggest that the ecliptic longitude of the direction of the interstellar flow has increased at an average rate of about 0.19 degrees per year over time. Possible astronomical explanations for these short-term variations in the interstellar gas entering the heliosphere are presented.
Recent results from multi-wavelength observations of the inner few hundred pc of the Galactic center have added two new characteristics to the ISM in this unique region. One is the cosmic ray ionization rate derived from H$_3^+$ measurements is at least two orders of magnitudes higher than in the disk of the Galaxy. The other is the bipolar thermal X-ray and synchrotron emission from this region, suggesting a relic of past activity. We propose that the high cosmic ray pressure drives a large-scale wind away from the Galactic plane and produces the bipolar emission as well as highly blue-shifted diffuse gas detected in H$_3^+$ absorption studies. We then discuss the interaction of large-scale winds with a number of objects, such as cloudlets and stellar wind bubbles, to explain the unusual characteristics of the ISM in this region including the nonthermal radio filaments. One of the implications of this scenario is the removal of gas driven by outflowing winds may regulate star formation or black hole accretion.
Observations with the Spitzer Space Telescope and the WISE satellite have revealed a prominent arc-like structure at 50 ($simeq0.1$ pc) from the O9.5V/B0.5V system $sigma$ Ori AB. We attribute this dust structure to the interaction of radiation pressure from the star with dust carried along by the IC 434 photo-evaporative flow of ionized gas from the dark cloud L1630. We have developed a quantitative model for the interaction of a dusty ionized flow with nearby (massive) stars where radiation pressure stalls dust, piling it up at an appreciable distance (> 0.1 pc), and force it to flow around the star. The model demonstrates that for the conditions in IC 434, the gas will decouple from the dust and will keep its original flow lines. We argue that this dust structure is the first example of a dust wave created by a massive star moving through the interstellar medium. Dust waves (and bow waves) stratify dust grains according to their radiation pressure opacity, which reflects the size distribution and composition of the grain material. Comparison of our model with observations implies that dust-gas coupling through Coulomb interaction is less important than previously thought, challenging our understanding of grain dynamics in hot, ionized regions of space. We describe the difference between dust (and bow) waves and classical bow shocks. We conclude that dust waves and bow waves should be common around stars showing the weak-wind phenomenon and that these structures are best observed at mid-IR to FIR wavelengths. In particular, dust waves and bow waves are most efficiently formed around weak-wind stars moving through a high density medium. Moreover, they provide a unique opportunity to study the direct interaction between a (massive) star and its immediate surroundings.
Many structured prediction tasks in machine vision have a collection of acceptable answers, instead of one definitive ground truth answer. Segmentation of images, for example, is subject to human labeling bias. Similarly, there are multiple possible pixel values that could plausibly complete occluded image regions. State-of-the art supervised learning methods are typically optimized to make a single test-time prediction for each query, failing to find other modes in the output space. Existing methods that allow for sampling often sacrifice speed or accuracy. We introduce a simple method for training a neural network, which enables diverse structured predictions to be made for each test-time query. For a single input, we learn to predict a range of possible answers. We compare favorably to methods that seek diversity through an ensemble of networks. Such stochastic multiple choice learning faces mode collapse, where one or more ensemble members fail to receive any training signal. Our best performing solution can be deployed for various tasks, and just involves small modifications to the existing single-mode architecture, loss function, and training regime. We demonstrate that our method results in quantitative improvements across three challenging tasks: 2D image completion, 3D volume estimation, and flow prediction.
For a variety of inter-related cultural, organizational, and political reasons, progress in climate science and the actual solution of scientific problems in this field have moved at a much slower rate than would normally be possible. Not all these factors are unique to climate science, but the heavy influence of politics has served to amplify the role of the other factors. Such factors as the change in the scientific paradigm from a dialectic opposition between theory and observation to an emphasis on simulation and observational programs, the inordinate growth of administration in universities and the consequent increase in importance of grant overhead, and the hierarchical nature of formal scientific organizations are cosidered. This paper will deal with the origin of the cultural changes and with specific examples of the operation and interaction of these factors. In particular, we will show how political bodies act to control scientific institutions, how scientists adjust both data and even theory to accommodate politically correct positions, and how opposition to these positions is disposed of.
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