BigData in astrophysics
Talking about observable Astrophysics creates huge amounts of data in imaging if the biggest telescopes worldwide are used. People are decreasingly able to analyze and interpret these data. Especially in the area of high precise image creation for the detection of satellites, space debris or asteroids an automated analysis is essential. Therefore, science is always looking for new and better possibilities to speed up the analysis of such image data whereas the failure ratio at least remains stable or gets improved.
Real-time data analysis efficiency
We, as sciencentric AG, are taking care of such interests by developing algorithms on base of neuronal networks to analyze the data much faster but more optimized. This can be accomplished in real-time or by analyzing the existing data later. In dependency to the data and the used algorithms the analysis can be parallelized, which in turn improves the performance again.
Efficient object detection through categorization and manual interpretation
Finally, all partially or fully generated results lead to a white and black-listing to categorize and to specify the objects being detected. Objects with an ambiguous state have to be analyzed manually. Doing that, further detections of these particular and manually marked objects can be optimized by reusing these data as learning-input within the neuronal network.
Analysis, interpretation, and optimization
sciencentric helps with analysis, interpretation and optimization of scientific based image data originated in Astronomy and Astrophysics sector. We are professionals in scenario specific adaption of algorithms to reduce the physically created image data, to either control or canalize and finally assign them to further subprocesses based on the business case used.
We are open for further questions, suggestions and collaborations anytime. Please contact us under [email protected]
