Testing Observational Techniques with 3D MHD Jets in Clusters

Observations of X-ray cavities formed by powerful jets from AGN in galaxy cluster cores are commonly used to estimate the mechanical luminosity of these sources. We test the reliability of observationally measuring this power with synthetic X-ray observations of 3-D MHD simulations of jets in a galaxy cluster environment. We address the role that factors such as jet intermittency and orientation of the jets on the sky have on the reliability of observational measurements of cavity enthalpy and age. An estimate of the errors in these quantities can be made by directly comparing ``observationally'' derived values with values from the simulations. In our tests, cavity enthalpy, age and mechanical luminosity derived from observations are within a factor of two of the simulation values.Comment: 4 pages, 3 figures; to appear in proceedings of The Monster's Fiery Breath: Feedback in Galaxies, Groups, and Clusters (AIP conference series

X-Atlas: An Online Archive of Chandra's Stellar High Energy Transmission Gratings Observations

The high-resolution X-ray spectroscopy made possible by the 1999 deployment of the Chandra X-ray Observatory has revolutionized our understanding of stellar X-ray emission. Many puzzles remain, though, particularly regarding the mechanisms of X-ray emission from OB stars. Although numerous individual stars have been observed in high-resolution, realizing the full scientific potential of these observations will necessitate studying the high-resolution Chandra dataset as a whole. To facilitate the rapid comparison and characterization of stellar spectra, we have compiled a uniformly processed database of all stars observed with the Chandra High Energy Transmission Grating (HETG). This database, known as X-Atlas, is accessible through a web interface with searching, data retrieval, and interactive plotting capabilities. For each target, X-Atlas also features predictions of the low-resolution ACIS spectra convolved from the HETG data for comparison with stellar sources in archival ACIS images. Preliminary analyses of the hardness ratios, quantiles, and spectral fits derived from the predicted ACIS spectra reveal systematic differences between the high-mass and low-mass stars in the atlas and offer evidence for at least two distinct classes of high-mass stars. A high degree of X-ray variability is also seen in both high and low-mass stars, including Capella, long thought to exhibit minimal variability. X-Atlas contains over 130 observations of approximately 25 high-mass stars and 40 low-mass stars and will be updated as additional stellar HETG observations become public. The atlas has recently expanded to non-stellar point sources, and Low Energy Transmission Grating (LETG) observations are currently being added as well

CosmoFlow: Using deep learning to learn the universe at scale

Deep learning is a promising tool to determine the physical model that describes our universe. To handle the considerable computational cost of this problem, we present CosmoFlow: a highly scalable deep learning application built on top of the TensorFlow framework. CosmoFlow uses efficient implementations of 3D convolution and pooling primitives, together with improvements in threading for many element-wise operations, to improve training performance on Intel® Xeon Phi™ processors. We also utilize the Cray PE Machine Learning Plugin for efficient scaling to multiple nodes. We demonstrate fully synchronous data-parallel training on 8192 nodes of Cori with 77% parallel efficiency, achieving 3.5 Pflop/s sustained performance. To our knowledge, this is the first large-scale science application of the TensorFlow framework at supercomputer scale with fully-synchronous training. These enhancements enable us to process large 3D dark matter distribution and predict the cosmological parameters ΩsubM/sub, σsub8/sub and nsubs/sub with unprecedented accuracy