scholarly journals Stellar Cluster Detection Using GMM with Deep Variational Autoencoder

2018 ◽  
Author(s):  
Arnab Karmakar ◽  
Deepak Mishra ◽  
Anandmayee Tej
Keyword(s):  
Cluster Detection ◽  
Stellar Cluster ◽  
Variational Autoencoder

scholarly journals Diffuse γ-ray emission toward the massive star-forming region, W40

2020 ◽  
Vol 639 ◽  
pp. A80
Author(s):  
Xiao-Na Sun ◽  
Rui-Zhi Yang ◽  
Yun-Feng Liang ◽  
Fang-Kun Peng ◽  
Hai-Ming Zhang ◽  
...  
Keyword(s):  
Cosmic Ray ◽  
High Energy ◽  
Young Star ◽  
Gas Content ◽  
Large Area ◽  
Stellar Cluster ◽  
Production Region ◽  
Star Forming ◽  
Photon Index ◽  
Young Star Clusters

We report the detection of high-energy γ-ray signal towards the young star-forming region, W40. Using 10-yr Pass 8 data from the Fermi Large Area Telescope (Fermi-LAT), we extracted an extended γ-ray excess region with a significance of ~18σ. The radiation has a spectrum with a photon index of 2.49 ± 0.01. The spatial correlation with the ionized gas content favors the hadronic origin of the γ-ray emission. The total cosmic-ray (CR) proton energy in the γ-ray production region is estimated to be the order of 1047 erg. However, this could be a small fraction of the total energy released in cosmic rays (CRs) by local accelerators, presumably by massive stars, over the lifetime of the system. If so, W40, together with earlier detections of γ-rays from Cygnus cocoon, Westerlund 1, Westerlund 2, NGC 3603, and 30 Dor C, supports the hypothesis that young star clusters are effective CR factories. The unique aspect of this result is that the γ-ray emission is detected, for the first time, from a stellar cluster itself, rather than from the surrounding “cocoons”.

scholarly journals Wandering of the central black hole in a galactic nucleus and correlation of the black hole mass with the bulge mass

2021 ◽  
Author(s):  
Hajime Inoue
Keyword(s):  
Black Hole ◽  
Loss Rate ◽  
Energy Gain ◽  
Stellar Disk ◽  
Black Hole Mass ◽  
Central Black Hole ◽  
Massive Black Hole ◽  
Stellar Cluster ◽  
Upper Limit ◽  
Simple Parameter

Abstract We investigate a mechanism for a super-massive black hole at the center of a galaxy to wander in the nucleus region. A situation is supposed in which the central black hole tends to move by the gravitational attractions from the nearby molecular clouds in a nuclear bulge but is braked via the dynamical frictions from the ambient stars there. We estimate the approximate kinetic energy of the black hole in an equilibrium between the energy gain rate through the gravitational attractions and the energy loss rate through the dynamical frictions in a nuclear bulge composed of a nuclear stellar disk and a nuclear stellar cluster as observed from our Galaxy. The wandering distance of the black hole in the gravitational potential of the nuclear bulge is evaluated to get as large as several 10 pc, when the black hole mass is relatively small. The distance, however, shrinks as the black hole mass increases, and the equilibrium solution between the energy gain and loss disappears when the black hole mass exceeds an upper limit. As a result, we can expect the following scenario for the evolution of the black hole mass: When the black hole mass is smaller than the upper limit, mass accretion of the interstellar matter in the circumnuclear region, causing the AGN activities, makes the black hole mass larger. However, when the mass gets to the upper limit, the black hole loses the balancing force against the dynamical friction and starts spiraling downward to the gravity center. From simple parameter scaling, the upper mass limit of the black hole is found to be proportional to the bulge mass, and this could explain the observed correlation of the black hole mass with the bulge mass.

scholarly journals Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Osman Mamun ◽  
Madison Wenzlick ◽  
Arun Sathanur ◽  
Jeffrey Hawk ◽  
Ram Devanathan
Keyword(s):  
Pearson Correlation ◽  
Rupture Life ◽  
Model Performance ◽  
Austenitic Stainless Steels ◽  
Generative Model ◽  
Austenitic Steels ◽  
Gradient Boosting ◽  
Variational Autoencoder ◽  
Feature Importance ◽  
Boosting Algorithm

AbstractThe Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.

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