scholarly journals An ANN Approach to Classification of Galaxy Spectra for the 2DF Galaxy Redshift Survey

1999 ◽  
Vol 183 ◽  
pp. 154-154
Author(s):  
S.R. Folkes ◽  
O. Lahav ◽  
S.J. Maddox
Keyword(s):  
Principal Component ◽  
Automated Classification ◽  
Signal To Noise ◽  
Normal Galaxies ◽  
Redshift Surveys ◽  
Redshift Survey ◽  
Galaxy Redshift ◽  
Spectral Simulations ◽  
Artificial Neural Network Ann

We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey and investigate the technique of Principal Component Analysis when applied to spectra of low S/N. It is found that the projection onto the first 8 Principal Components hold most of the real spectral information, with later projections only adding noise. Using these components as input, we train an Artificial Neural Network (ANN) to classify the noisy simulated spectra into morphological classes. We find that more than 90% of our sample of normal galaxies are correctly classified into one of five morphological classes for simulations at bJ=19.7.

scholarly journals Darth Fader: Analysing galaxy spectra at low signal-to-noise

2014 ◽  
Vol 10 (S306) ◽  
pp. 72-74
Author(s):  
Adrienne Leonard ◽  
Daniel P. Machado ◽  
Filipe B. Abdalla ◽  
Jean-Luc Starck
Keyword(s):  
Spectral Lines ◽  
Signal To Noise ◽  
Automated Method ◽  
Redshift Surveys ◽  
Formation History ◽  
The Galaxy ◽  
History Of ◽  
Galaxy Redshift ◽  
The Universe

AbstractSpectroscopic redshift surveys are an incredibly valuable tool in cosmology, allowing us to trace the distribution of galaxies as a function of distance and, thus, trace the evolution of structure formation in the Universe. However, estimating the redshifts from spectra with low signal-to-noise is difficult, and such data are often either discarded or require human classification of spectral lines to obtain the galaxy redshift. Darth Fader offers an automated method for estimating the redshifts of galaxies in the low signal-to-noise regime. Using a sophisticated, wavelet-based technique, galaxy spectra can be separated into continuum, line and noise components, and the lines can then be cross-correlated with template spectra in order to estimate the redshifts. Cross-matching of the identified lines then allows for a cleaning of the resulting catalogue, effectively removing the vast majority of erroneous redshift estimates and resulting in a highly pure, highly accurate redshift catalogue. Darth Fader allows us to effectively use low signal-to-noise galaxy spectra, and dramatically reduces the number of human hours required to do this, allowing spectroscopic surveys to probe deeper into the formation history of the Universe.

scholarly journals Siamese Architecture-Based 3D DenseNet with Person-Specific Normalization Using Neutral Expression for Spontaneous and Posed Smile Classification

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7184
Author(s):  
Kunyoung Lee ◽  
Eui Chul Lee
Keyword(s):  
Principal Component ◽  
Facial Muscle ◽  
Automated Classification ◽  
Video Classification ◽  
Model Accuracy ◽  
Proposed Model ◽  
3D Cnn ◽  
Reference Input ◽  
Neutral Expression

Clinical studies have demonstrated that spontaneous and posed smiles have spatiotemporal differences in facial muscle movements, such as laterally asymmetric movements, which use different facial muscles. In this study, a model was developed in which video classification of the two types of smile was performed using a 3D convolutional neural network (CNN) applying a Siamese network, and using a neutral expression as reference input. The proposed model makes the following contributions. First, the developed model solves the problem caused by the differences in appearance between individuals, because it learns the spatiotemporal differences between the neutral expression of an individual and spontaneous and posed smiles. Second, using a neutral expression as an anchor improves the model accuracy, when compared to that of the conventional method using genuine and imposter pairs. Third, by using a neutral expression as an anchor image, it is possible to develop a fully automated classification system for spontaneous and posed smiles. In addition, visualizations were designed for the Siamese architecture-based 3D CNN to analyze the accuracy improvement, and to compare the proposed and conventional methods through feature analysis, using principal component analysis (PCA).

scholarly journals Building the red sequence through gas-rich major mergers

2009 ◽  
Vol 5 (S262) ◽  
pp. 225-228
Author(s):  
Vivienne Wild ◽  
C. Jakob Walcher ◽  
Peter H. Johansson
Keyword(s):  
Galaxy Evolution ◽  
Principal Component ◽  
Galaxy Mergers ◽  
Redshift Surveys ◽  
Analysis Technique ◽  
Deep Survey ◽  
Galaxy Redshift ◽  
Minor Mergers ◽  
Gas Accretion ◽  
Red Sequence

AbstractUnderstanding the details of how the red sequence is built is a key question in galaxy evolution. What are the relative roles of gas-rich vs. dry mergers, major vs. minor mergers or galaxy mergers vs. gas accretion? In a recent paper (Wild et al. 2009), we compare hydrodynamic simulations with observations to show how gas-rich major mergers result in galaxies with strong post-starburst spectral features, a population of galaxies easily identified in the real Universe using optical spectra. Using spectra from the VVDS deep survey with <z> = 0.7, and a principal component analysis technique to provide indices with high enough SNR, we find that 40% of the mass flux onto the red-sequence could enter through a strong post-starburst phase, and thus through gas-rich major mergers. The deeper samples provided by next generation galaxy redshift surveys will allow us to observe the primary physical processes responsible for the shut-down in starformation and build-up of the red sequence.

scholarly journals Redshift Surveys With 2DF

1998 ◽  
Vol 11 (1) ◽  
pp. 473-481
Author(s):  
Matthew Colless ◽  
Brian Boyle
Keyword(s):  
20Th Century ◽  
21St Century ◽  
Preliminary Results ◽  
Redshift Surveys ◽  
Order Of Magnitude ◽  
Redshift Survey ◽  
The Subject ◽  
Galaxy Redshift ◽  
Survey Strategy

This IAU Joint Discussion proposes to address the subject of redshift surveys in the 21st century. This paper, however, deals with two major new redshift surveys that those involved sincerely hope will be completed in the 20th century. Nonetheless, these surveys are relevant to the topic of the meeting, as they clearly foreshadow the scope and style of redshift surveys, if not in the coming millennium, at least in the coming decade. The surveys are being carried out with the new Two Degree Field (2dF) facility on the Anglo-Australian Telescope (AAT), a 400-fibre multi-object spectrograph with the capability, as described in Section 2, to increase the size of redshift surveys by an order of magnitude over current best efforts. The main scientific goals, survey strategy and some preliminary results from the 2dF Galaxy Redshift Survey are outlined in Section 3, while Section 4 similarly describes the 2dF QSO Redshift Survey. Further information can be found on the WWW at http://www.aao.gov.au/2df/ for the 2dF facility, at http://msowww.anu.edu.au/~colless/2dF/ for thegalaxy survey and at http://www.aao.gov.au/local/www/rs/qso_surv.html for the QSO survey.

scholarly journals Classification of Agarwood Oil Using an Electronic Nose

Sensors ◽  
2010 ◽  
Vol 10 (5) ◽  
pp. 4675-4685 ◽  
Author(s):  
Wahyu Hidayat ◽  
Ali Yeon Md. Shakaff ◽  
Mohd Noor Ahmad ◽  
Abdul Hamid Adom
Keyword(s):  
Neural Network ◽  
Electronic Nose ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Scatter Plot ◽  
Different Types ◽  
Artificial Neural Network Ann ◽  
Distinct Separation ◽  
Agarwood Oil

Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.

scholarly journals Classification and Quantitative Evaluation of Eddy Current Based on Kernel-PCA and ELM for Defects in Metal Component

Metals ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 155 ◽  
Author(s):  
Weiquan Deng ◽  
Bo Ye ◽  
Jun Bao ◽  
Guoyong Huang ◽  
Jiande Wu
Keyword(s):  
Eddy Current ◽  
Principal Component ◽  
Support Vector ◽  
Linear Least Squares ◽  
Metal Component ◽  
Testing Technology ◽  
Critical Components ◽  
Artificial Neural Network Ann ◽  
Learning Machine

Eddy current testing technology is widely used in the defect detection of metal components and the integrity evaluation of critical components. However, at present, the evaluation and analysis of defect signals are still mostly based on artificial evaluation. Therefore, the evaluation of defects is often subjectively affected by human factors, which may lead to a lack in objectivity, accuracy, and reliability. In this paper, the feature extraction of non-linear signals is carried out. First, using the kernel-based principal component analysis (KPCA) algorithm. Secondly, based on the feature vectors of defects, the classification of an extreme learning machine (ELM) for different defects is studied. Compared with traditional classifiers, such as artificial neural network (ANN) and support vector machine (SVM), the accuracy and rapidity of ELM are more advantageous. Based on the accurate classification of defects, the linear least-squares fitting is used to further quantitatively evaluate the defects. Finally, the experimental results have verified the effectiveness of the proposed method, which involves automatic defect classification and quantitative analysis.

scholarly journals The Sydney University Molonglo Sky Survey (SUMSS) and Optical Redshift Surveys of the Southern Sky

2002 ◽  
Vol 199 ◽  
pp. 11-20
Author(s):  
Elaine M. Sadler ◽  
R.W. Hunstead
Keyword(s):  
Southern Hemisphere ◽  
Noise Level ◽  
Radio Data ◽  
Redshift Surveys ◽  
Radio Imaging ◽  
Sky Survey ◽  
Redshift Survey ◽  
Galaxy Redshift ◽  
New Generation

The Sydney University Molonglo Sky Survey (SUMSS) is a radio imaging survey at 843 MHz of the whole sky south of declination −30°. With a resolution of 43″ × 43″ cosec |δ| and an rms noise level of ∼ 1 mJy/beam, SUMSS has similar sensitivity and resolution to the northern NRAO VLA Sky Survey (NVSS). Here, we present some results from the first two years of SUMSS and also show what can be done by combining radio data from SUMSS and NVSS with the new generation of large optical redshift surveys (including the 2dF Galaxy Redshift Survey and 6dF Galaxy Survey) now becoming available in the southern hemisphere.

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