1,835,257 research outputs found
Spectral analysis of the Chandra comet survey
No full textAims.We present results of the analysis of cometary X-ray spectra with an extended version of our charge exchange emission model (Bodewits et al. 2006). We have applied this model to the sample of 8 comets thus far observed with the Chandra X-ray observatory and ACIS spectrometer in the 300-1000 eV range. The surveyed comets are C/1999 S4 (LINEAR), C/1999 T1 (McNaught-Hartley), C/2000 WM1 (LINEAR), 153P/2002 (Ikeya-Zhang), 2P/2003 (Encke), C/2001 Q4 (NEAT), 9P/2005 (Tempel 1) and 73P/2006-B (Schwassmann-Wachmann 3) and the observations include a broad variety of comets, solar wind environments and observational conditions. Methods.The interaction model is based on state selective, velocity dependent charge exchange cross sections and is used to explore how cometary X-ray emission depend on cometary, observational and solar wind characteristics. It is further demonstrated that cometary X-ray spectra mainly reflect the state of the local solar wind. The current sample of Chandra observations was fit using the constrains of the charge exchange model, and relative solar wind abundances were derived from the X-ray spectra. Results.Our analysis showed that spectral differences can be ascribed to different solar wind states, as such identifying comets interacting with (I) fast, cold wind, (II), slow, warm wind and (III) disturbed, fast, hot winds associated with interplanetary coronal mass ejections. We furthermore predict the existence of a fourth spectral class, associated with the cool, fast high latitude wind
Multipurpose small area estimation
No full textSample surveys are generally multivariate, in the sense that they measure more than oneresponse variable. In theory, each variable can then be assigned an optimal weight forestimation purposes. However, it is often a distinct practical advantage to have a singleweight that is used with all variables collected in the survey. This paper describes howsuch multipurpose sample weights can be constructed when small area estimates of thesurvey variables are required. The approach is based on the model-based direct (MBD)method of small area estimation described in Chambers and Chandra (2006). Empiricalresults reported in this paper show that MBD estimators for small areas based onmultipurpose weights perform well across a range of variables that are often of interest inbusiness surveys. Furthermore, these results show that the proposed approach is robust tomodel misspecification and also efficient for the variables ill-suited to standard methodsof small area estimation (e.g. variables that contain a significant proportion of zeros).<br/
Chandra-MARX/marxs: v2.0
No full text<h1>2.0 (02-May-2024)</h1>
<p>In version 2.0 we bring in more instrument models for missions in development. Those models are rapidly changing as the instruments are developed and are not tested to the level of stable code, instead they are meant as examples. There is also new X3D plotting capability and new analysis functionality to look at simulation results.</p>
<h2>What's Changed</h2>
<ul>
<li>make L3 support bars for if no facet is hit by @hamogu in https://github.com/Chandra-MARX/marxs/pull/221</li>
<li>Tolerancing by @hamogu in https://github.com/Chandra-MARX/marxs/pull/222</li>
<li>Ape17 by @hamogu in https://github.com/Chandra-MARX/marxs/pull/223</li>
<li>Try to make GH action pass by @hamogu in https://github.com/Chandra-MARX/marxs/pull/224</li>
<li>Several flixes developed for Arcus combined into one PR by @hamogu in https://github.com/Chandra-MARX/marxs/pull/226</li>
<li>Minor fixes by @hamogu in https://github.com/Chandra-MARX/marxs/pull/227</li>
<li>Upgrade to GitHub-native Dependabot by @dependabot-preview in https://github.com/Chandra-MARX/marxs/pull/228</li>
<li>Prevent problems with probability < 0 or > 1 by @hamogu in https://github.com/Chandra-MARX/marxs/pull/229</li>
<li>Followup with files forgotten in #229 by @hamogu in https://github.com/Chandra-MARX/marxs/pull/230</li>
<li>Looks like a type in an untested try/except block by @hamogu in https://github.com/Chandra-MARX/marxs/pull/231</li>
<li>Improve chandra by @hamogu in https://github.com/Chandra-MARX/marxs/pull/232</li>
<li>add clip to prevent numerical problems in arccos by @hamogu in https://github.com/Chandra-MARX/marxs/pull/234</li>
<li>Modify analyser to account for chip gap by @hamogu in https://github.com/Chandra-MARX/marxs/pull/235</li>
<li>Infra_update by @hamogu in https://github.com/Chandra-MARX/marxs/pull/236</li>
<li>InterpolateEfficiencyTable now takes table, not filename as input by @hamogu in https://github.com/Chandra-MARX/marxs/pull/237</li>
<li>Add X3d visualization backend by @hamogu in https://github.com/Chandra-MARX/marxs/pull/238</li>
<li>Update GH actions to run on current nodes by @hamogu in https://github.com/Chandra-MARX/marxs/pull/241</li>
<li>RDT: deprecate system packages by @hamogu in https://github.com/Chandra-MARX/marxs/pull/242</li>
<li>Integrate missions into marxs by @hamogu in https://github.com/Chandra-MARX/marxs/pull/240</li>
<li>x3d visualization: transparancy/opacity by @hamogu in https://github.com/Chandra-MARX/marxs/pull/243</li>
<li>Improve X3D output by @hamogu in https://github.com/Chandra-MARX/marxs/pull/244</li>
<li>Add functions to general channels in double-tilted Rowland design by @hamogu in https://github.com/Chandra-MARX/marxs/pull/245</li>
<li>Change the defaults double rowland torus to standard xyz axes. by @hamogu in https://github.com/Chandra-MARX/marxs/pull/246</li>
<li>Increase consistency in where the Rowland tori are oriented by @hamogu in https://github.com/Chandra-MARX/marxs/pull/247</li>
<li>Add option for reflectivity and optical axis offset from geometrical center of PerfectLens by @hamogu in https://github.com/Chandra-MARX/marxs/pull/248</li>
<li>X3dplot by @hamogu in https://github.com/Chandra-MARX/marxs/pull/249</li>
<li>Fix design_tilted_torus by @hamogu in https://github.com/Chandra-MARX/marxs/pull/250</li>
<li>Add method to create archive files for publishing of X3D visualization. by @hamogu in https://github.com/Chandra-MARX/marxs/pull/251</li>
<li><code>CCDRedistNormal</code> now inherits from an optical element . by @hamogu in https://github.com/Chandra-MARX/marxs/pull/252</li>
</ul>
<h2>New Contributors</h2>
<ul>
<li>@dependabot-preview made their first contribution in https://github.com/Chandra-MARX/marxs/pull/228</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/Chandra-MARX/marxs/compare/v1.2...v2.0</p>
Small Area Estimation with Skewed Data
No full textIn business surveys, data typically are skewed and the standard approach for small area estimation based on linear mixed models lead to inefficient estimates. In this paper, we discuss small area estimation techniques for skewed data that are linear following a suitable transformation. In this context, implementation of the empirical best linear unbiased prediction (EBLUP) approach under transformation to a linear mixed model is complicated. However, this is not the case with the model-based direct (MBD) approach (Chambers and Chandra, 2006), which is based on weighted linear estimators. We extend the MBD approach to skewed data using sample weights derived via model calibration based on a log transform model with random area effects. Our results show this estimator is both efficient and robust with respect to the distribution of these random effects. An application to real data demonstrates the satisfactory performance of the method
Chandra Images and False Color
No full textThis is the Chandra X-ray Observatory photo album website. It begins with an introduction on the electromagnetic spectrum, focusing on X-rays in particular. It also contains information on false color images. The images in this photo gallery were taken between 1999 and 2004 by the Chandra telescope. Each image includes a description and a link to more information about the object. Additional resources such as podcasts and other multimedia projects are also included. This is a nice comprehensive look at the makeup of light and research into this area
PRECS Participant: Chandra Davies
No full textChandra Davies, a participant in the Phenotypic Plasticity Research Experience for Community College Students, discusses the experience and assigned project
A star-forming galaxy at z= 5.78 in the Chandra Deep Field South
Full text linkWe report the discovery of a luminous z = 5.78 star-forming galaxy in the Chandra Deep Field South. This galaxy was selected as an ‘i-drop’ from the GOODS public survey imaging with the Hubble Space Telescope/Advanced Camera for Surveys (object 3 in the work of Stanway, Bunker & McMahon 2003). The large colour of (i′−z′)AB = 1.6 indicated a spectral break consistent with the Lyman α forest absorption shortward of Lyman α at z≈ 6. The galaxy is very compact (marginally resolved with ACS with a half-light radius of 0.08 arcsec, so rhl 5. Our spectroscopic redshift for this object confirms the validity of the i′-drop technique of Stanway et al. to select star-forming galaxies atz≈ 6
Chandra-TYK2MS-WBdata
No full textChandra et al., TYK2 MS Western blot raw data imagesTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
Aman Chandra
No full textAman Chandra received an MS in Aerospace Engineering at Arizona State University. He is currently a PhD student at the Univeristy of Arizona’s department of Aerospace and Mechanical Engineering. His master’s thesis dissertation is on inflatable communication antennas for small satellites. As a former design engineer at General Electric, he specialized in thermo-mechanical design and packaging of ultrasonic transducers and medical imaging systems. He has vast experience in the structural design of CubeSats, small satellite systems and deployable mechanism design. His current research interests include computational geometric modelling of compliant linkages, deployable origami design and packaging methods and non-linear finite element modelling.https://commons.erau.edu/stm-images/1105/thumbnail.jp
Chandra-TYK2MS-WBdata
No full textChandra et al., TYK2 MS Western blot raw data imagesTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
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