scholarly journals Dynamical mass inference of galaxy clusters with neural flows

2020 ◽  
Vol 499 (2) ◽  
pp. 1985-1997
Author(s):  
Doogesh Kodi Ramanah ◽  
Radosław Wojtak ◽  
Zoe Ansari ◽  
Christa Gall ◽  
Jens Hjorth
Keyword(s):  
Neural Network ◽  
Galaxy Clusters ◽  
Probability Distributions ◽  
Mass Range ◽  
High Mass ◽  
Mass Measurements ◽  
Dynamical Mass ◽  
Mass Scaling ◽  
The Neural Network ◽  
Conventional Machine

ABSTRACT We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, that is, the observed line-of-sight velocities and projected distances of galaxies from the cluster centre. Our method employs normalizing flows, a deep neural network capable of learning arbitrary high-dimensional probability distributions, and inherently accounts, to an adequate extent, for the presence of interloper galaxies which are not bounded to a given cluster, the primary contaminant of dynamical mass measurements. We validate and showcase the performance of our neural flow approach to robustly infer the dynamical mass of clusters from a realistic mock cluster catalogue. A key aspect of our novel algorithm is that it yields the probability density function of the mass of a particular cluster, thereby providing a principled way of quantifying uncertainties, in contrast to conventional machine learning (ML) approaches. The neural network mass predictions, when applied to a contaminated catalogue with interlopers, have a mean overall logarithmic residual scatter of 0.028 dex, with a lognormal scatter of 0.126 dex, which goes down to 0.089 dex for clusters in the intermediate- to high-mass range. This is an improvement by nearly a factor of 4 relative to the classical cluster mass scaling relation with the velocity dispersion, and outperforms recently proposed ML approaches. We also apply our neural flow mass estimator to a compilation of galaxy observations of some well-studied clusters with robust dynamical mass estimates, further substantiating the efficacy of our algorithm.

scholarly journals DYNAMICAL MASS MEASUREMENTS OF CONTAMINATED GALAXY CLUSTERS USING MACHINE LEARNING

2016 ◽  
Vol 831 (2) ◽  
pp. 135 ◽  
Author(s):  
M. Ntampaka ◽  
H. Trac ◽  
D. J. Sutherland ◽  
S. Fromenteau ◽  
B. Póczos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Galaxy Clusters ◽  
Mass Measurements ◽  
Dynamical Mass

scholarly journals A MACHINE LEARNING APPROACH FOR DYNAMICAL MASS MEASUREMENTS OF GALAXY CLUSTERS

2015 ◽  
Vol 803 (2) ◽  
pp. 50 ◽  
Author(s):  
M. Ntampaka ◽  
H. Trac ◽  
D. J. Sutherland ◽  
N. Battaglia ◽  
B. Póczos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Galaxy Clusters ◽  
Learning Approach ◽  
Mass Measurements ◽  
Dynamical Mass ◽  
Machine Learning Approach

scholarly journals Contribution of stripped nuclei to the ultracompact dwarf galaxy population in the Virgo cluster

2020 ◽  
Vol 501 (2) ◽  
pp. 1852-1867
Author(s):  
Rebecca J Mayes ◽  
Michael J Drinkwater ◽  
Joel Pfeffer ◽  
Holger Baumgardt ◽  
Chengze Liu ◽  
...  
Keyword(s):  
Galaxy Clusters ◽  
Dwarf Galaxy ◽  
Mass Range ◽  
Virgo Cluster ◽  
High Mass ◽  
Massive Galaxies ◽  
Mass Distributions ◽  
Galaxy Population ◽  
Massive Galaxy ◽  
Individual Galaxies

ABSTRACT We use the hydrodynamical EAGLE simulation to predict the numbers, masses, and radial distributions of tidally stripped galaxy nuclei in massive galaxy clusters, and compare these results to observations of ultracompact dwarf galaxies (UCDs) in the Virgo cluster. We trace the merger trees of galaxies in massive galaxy clusters back in time and determine the numbers and masses of stripped nuclei from galaxies disrupted in mergers. The spatial distribution of stripped nuclei in the simulations is consistent with those of UCDs surrounding massive galaxies in the Virgo cluster. Additionally, the numbers of stripped nuclei are consistent with the numbers of M > 107 M⊙ UCDs around individual galaxies and in the Virgo cluster as a whole. The mass distributions in this mass range are also consistent. We find that the numbers of stripped nuclei surrounding individual galaxies correlate better with the stellar or halo mass of individual galaxies than the total cluster mass. We conclude that most high mass (M > 107 M⊙) UCDs are likely stripped nuclei. It is difficult to draw reliable conclusions about low mass (M < 107 M⊙) UCDs because of observational selection effects. We additionally predict that a few hundred stripped nuclei below a mass of 2 × 106 M⊙ should exist in massive galaxies that will overlap in mass with the globular cluster population. Approximately 1–3 stripped nuclei in the process of forming also exist per massive galaxy.

scholarly journals A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters

2019 ◽  
Vol 887 (1) ◽  
pp. 25 ◽  
Author(s):  
Matthew Ho ◽  
Markus Michael Rau ◽  
Michelle Ntampaka ◽  
Arya Farahi ◽  
Hy Trac ◽  
...  
Keyword(s):  
Deep Learning ◽  
Galaxy Clusters ◽  
Learning Method ◽  
Mass Measurements ◽  
Dynamical Mass

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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