New Method for Analyzing Event Unprecent Accuracy and Spe in Studying Supermassive Black Holes
Scientists have made a breakthrough in the study of black holes by using machine learning
Horizon Telescope Data to analyze data from the Event Horizon whatsapp number list Telescope (EHT). The discovery allows them to determine key characteristics of supermassive black holes,
such as their rotation spe and the ratio of ion and electron temperatures,
Horizon Telescope Data without the intermiate step of imaging.
Traditionally, the analysis was done using images
Which introduc errors. The new method works directly with the data obtain by the interferometer – instead of looking at a how to stop bec attacks? photograph of an object,
scientists analyze its “fingerprint” in the form of a signal, bypassing the “development” stage of the photograph.
A gallery of example images us in this study.
The images are plott on a logarithmic scale with three orders of dynamic range to better visualize low-surface-brightness details. Source: Franc O, Pavlos Protopapas, Dominic W. Pesce, Angelo Ricarte, Sheperd S.
Doeleman, Cecilia Garraffo, Lindy Blackburn,
Mauricio Santillana The principle of operation is bas on training neural networks on data obtain by modeling the behavior of matter philippines numbers around black holes. These models, creat using complex calculations (general relativistic magnetohydrodynamics),
take into account various parameters, such as the rotation spe of the black hole and the temperature of its neighborhood.
Neural networks are train to recognize the relationship between
Horizon Telescope Data these parameters and the signals record by the EHT.
The train network is then appli
To real EHT data to obtain estimates of the black hole parameters. The advantages of the new approach are obvious: the accuracy and efficiency of the analysis are increas, since errors arising during image processing are exclud.
The prospects are associat with the possibility of continuous monitoring of black holes, which will allow tracking changes in their parameters over time and obtaining a more complete picture. The study us data from EHT observations of the black hole M87* in 2017.