scholarly journals MULTI-WAVELENGTH OBSERVATIONS OF SUPERNOVA 2011ei: TIME-DEPENDENT CLASSIFICATION OF TYPE IIb AND Ib SUPERNOVAE AND IMPLICATIONS FOR THEIR PROGENITORS

2013 ◽  
Vol 767 (1) ◽  
pp. 71 ◽  
Author(s):  
Dan Milisavljevic ◽  
Raffaella Margutti ◽  
Alicia M. Soderberg ◽  
Giuliano Pignata ◽  
Laura Chomiuk ◽  
...  
Keyword(s):  
Time Dependent ◽  
Multi Wavelength

Detection and classification of atmospheric aerosols using multi-wavelength LWIR LIDAR

2009 ◽  
Author(s):  
Russell E. Warren ◽  
Richard G. Vanderbeek ◽  
Jeffrey L. Ahl
Keyword(s):  
Atmospheric Aerosols ◽  
Multi Wavelength

scholarly journals Spectral Energy Distributions of a set of H ii regions in M33 (HerM33es)

2011 ◽  
Vol 7 (S284) ◽  
pp. 122-124
Author(s):  
Monica Relaño ◽  
Simon Verley ◽  
Isabel Pérez ◽  
Carsten Kramer ◽  
Manolis Xilouris ◽  
...  
Keyword(s):  
Spectral Energy Distribution ◽  
Morphological Type ◽  
Spectral Energy ◽  
H Ii Regions ◽  
Energy Distributions ◽  
Stellar Cluster ◽  
Multi Wavelength ◽  
Hα Emission ◽  
Group Galaxy

AbstractWithin the framework of the HerM33es Key Project for Herschel and in combination with multi-wavelength data, we study the Spectral Energy Distribution (SED) of a set of H ii regions in the Local Group Galaxy M33. Using the Hα emission, we perform a classification of a selected H ii region sample in terms of morphology, separating the objects in filled, mixed, shell and clear shell objects. We obtain the SED for each H ii region as well as a representative SED for each class of objects. We also study the emission distribution of each band within the regions. We find different trends in the SEDs for each morphological type that are related to properties of the dust and their associated stellar cluster. The emission distribution of each band within the region is different for each morphological type of object.

scholarly journals Classification of Astrophysics Journal Articles with Machine Learning to Identify Data for NED

2022 ◽  
Vol 134 (1031) ◽  
pp. 014501
Author(s):  
Tracy X. Chen ◽  
Rick Ebert ◽  
Joseph M. Mazzarella ◽  
Cren Frayer ◽  
Scott Terek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Journal Article ◽  
Value Added ◽  
Journal Articles ◽  
Online Service ◽  
Production Pipeline ◽  
Multi Wavelength ◽  
Data Content ◽  
Number Of Publications

Abstract The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service that combines fundamental multi-wavelength information for known objects beyond the Milky Way and provides value-added, derived quantities and tools to search and access the data. The contents and relationships between measurements in the database are continuously augmented and revised to stay current with astrophysics literature and new sky surveys. The conventional process of distilling and extracting data from the literature involves human experts to review the journal articles and determine if an article is of extragalactic nature, and if so, what types of data it contains. This is both labor intensive and unsustainable, especially given the ever-increasing number of publications each year. We present here a machine learning (ML) approach developed and integrated into the NED production pipeline to help automate the classification of journal article topics and their data content for inclusion into NED. We show that this ML application can successfully reproduce the classifications of a human expert to an accuracy of over 90% in a fraction of the time it takes a human, allowing us to focus human expertise on tasks that are more difficult to automate.

scholarly journals Adaptivity as a Property to Achieve Resilience of Load-Carrying Systems

2018 ◽  
Vol 885 ◽  
pp. 77-87 ◽  
Author(s):  
Pia D. Schlemmer ◽  
Hermann Kloberdanz ◽  
Christopher M. Gehb ◽  
Eckhard Kirchner
Keyword(s):  
Product Development ◽  
Time Dependent ◽  
External Disturbances ◽  
Load Carrying ◽  
System Components ◽  
Brake Application ◽  
Temporal Horizon ◽  
Robust Systems ◽  
Load Conditions

Load-carrying systems often suffer from unexpected disruptions which can cause damages or system breakdowns if they were neglected during product development. In this context, unexpected disruptions summarize unpredictable load conditions, external disturbances or failures of system components and can be comprehended as uncertainties caused by nescience. While robust systems can cope with stochastic uncertainties, uncertainties caused by nescience can be controlled only by resilient load-carrying systems. This paper gives an overview of the characteristics of resilience as well as the time-dependent resilient behaviour of subsystems. Based on this, the adaptivity of subsystems is classified and can be distinguished between autonomous and externally induced adaption and the temporal horizon of adaption. The classification of adaptivity is explained using a simple example of a joint brake application.

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