scholarly journals UOCS*. III. UVIT catalogue of open clusters with machine learning based membership using Gaia EDR3 astrometry

2021 ◽  
Author(s):  
Vikrant V Jadhav ◽  
Clara M Pennock ◽  
Annapurni Subramaniam ◽  
Ram Sagar ◽  
Prasanta Kumar Nayak
Keyword(s):  
Machine Learning ◽  
Main Sequence ◽  
Learning Algorithm ◽  
Ultra Violet ◽  
Gaussian Mixture ◽  
Open Clusters ◽  
Red Giants ◽  
Cluster Membership ◽  
Blue Stragglers ◽  
Red Clump

Abstract We present a study of six open clusters (Berkeley 67, King 2, NGC 2420, NGC 2477, NGC 2682 and NGC 6940) using the Ultra Violet Imaging Telescope (UVIT) aboard ASTROSAT and Gaia EDR3. We used combinations of astrometric, photometric and systematic parameters to train and supervise a machine learning algorithm along with a Gaussian mixture model for the determination of cluster membership. This technique is robust, reproducible and versatile in various cluster environments. In this study, the Gaia EDR3 membership catalogues are provided along with classification of the stars as members, candidates and field in the six clusters. We could detect 200–2500 additional members using our method with respect to previous studies, which helped estimate mean space velocities, distances, number of members and core radii. UVIT photometric catalogues, which include blue stragglers, main-sequence and red giants are also provided. From UV–Optical colour-magnitude diagrams, we found that majority of the sources in NGC 2682 and a few in NGC 2420, NGC 2477 and NGC 6940 showed excess UV flux. NGC 2682 images have ten white dwarf detection in far-UV. The far-UV and near-UV images of the massive cluster NGC 2477 have 92 and 576 members respectively, which will be useful to study the UV properties of stars in the extended turn-off and in various evolutionary stages from main-sequence to red clump. Future studies will carry out panchromatic and spectroscopic analysis of noteworthy members detected in this study.

scholarly journals ML-MOC: Machine Learning (kNN and GMM) based Membership determination for Open Clusters

2021 ◽  
Vol 502 (2) ◽  
pp. 2582-2599
Author(s):  
Manan Agarwal ◽  
Khushboo K Rao ◽  
Kaushar Vaidya ◽  
Souradeep Bhattacharya
Keyword(s):  
Machine Learning ◽  
Open Cluster ◽  
A Priori ◽  
Gaussian Mixture ◽  
Open Clusters ◽  
Proper Motions ◽  
Cluster Membership ◽  
Priori Information ◽  
Gaia Dr2 ◽  
Good Agreement

ABSTRACT The existing open-cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present ml-moc, a new machine-learning-based approach to identify likely members of open clusters using the Gaia DR2 data and no a priori information about cluster parameters. We use the k-nearest neighbour (kNN) algorithm and the Gaussian mixture model (GMM) on high-precision proper motions and parallax measurements from the Gaia DR2 data to determine the membership probabilities of individual sources down to G ∼ 20 mag. To validate the developed method, we apply it to 15 open clusters: M67, NGC 2099, NGC 2141, NGC 2243, NGC 2539, NGC 6253, NGC 6405, NGC 6791, NGC 7044, NGC 7142, NGC 752, Blanco 1, Berkeley 18, IC 4651, and Hyades. These clusters differ in terms of their ages, distances, metallicities, and extinctions and cover a wide parameter space in proper motions and parallaxes with respect to the field population. The extracted members produce clean colour–magnitude diagrams and our astrometric parameters of the clusters are in good agreement with the values derived in previous work. The estimated degree of contamination in the extracted members ranges between 2 ${{\ \rm per\ cent}}$ and 12 ${{\ \rm per\ cent}}$. The results show that ml-moc is a reliable approach to segregate open-cluster members from field stars.

scholarly journals Distance estimates to five open clusters based on 2mass data of red clump giants

2012 ◽  
Vol 8 (S289) ◽  
pp. 367-370
Author(s):  
Li Chen ◽  
Xinhua Gao
Keyword(s):  
Main Sequence ◽  
Near Infrared ◽  
Milky Way ◽  
Large Magellanic Cloud ◽  
Open Clusters ◽  
Distance Determination ◽  
Advantages And Disadvantages ◽  
Cluster Distance ◽  
Red Clump ◽  
Distance Indicators

AbstractRed clump (RC) giants are excellent standard candles in the Milky Way and the Large Magellanic Cloud. The near-infrared K-band intrinsic luminosity of RC giants exhibits only a small variance and a weak dependence on chemical composition and age. In addition, RCs are often easily recognizable in the color–magnitude diagrams of open clusters, which renders them extremely useful distance indicators for some intermediate-age or old open clusters. Here we determine the distance moduli of five Galactic open clusters covering a range of metallicities and ages, based on RC giants in the cluster regions using 2mass photometric data. We compare our result with those from main-sequence fitting and also briefly discuss the advantages and disadvantages of RC-based cluster distance determination.

scholarly journals A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring

2019 ◽  
Vol 255 ◽  
pp. 06008 ◽  
Author(s):  
Mohd. Dasuki Yusoff ◽  
Ching Sheng Ooi ◽  
Meng Hee Lim ◽  
Mohd. Salman Leong
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Degradation Process ◽  
Gaussian Mixture ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Model Set ◽  
Set Up ◽  
Original Equipment Manufacturers

Industrial practise typically applies pre-set original equipment manufacturers (OEMs) limits to turbomachinery online condition monitoring. However, aforementioned technique which considers sensor readings within range as normal state often get overlooked in the developments of degradation process. Thus, turbomachinery application in dire need of a responsive monitoring analysis in order to avoid machine breakdown before leading to a more disastrous event. A feasible machine learning algorithm consists of k-means and Gaussian Mixture Model (GMM) is proposed to observe the existence of signal trend or anomaly over machine active period. The aim of the unsupervised k-means is to determine the number of clusters, k according to the total trend detected from the processed dataset. Next, the designated k is input into the supervised GMM algorithm to initialize the number of components. Experiment results showed that the k-means-GMM model set up not only capable of statistically define machine state conditions, but also yield a time-dependent clustering image in reflecting degradation severity, as a mean to achieve predictive maintenance.

scholarly journals Quantifying News Narratives to Predict Movements in Market Risk

2021 ◽  
pp. 265-285
Author(s):  
Thomas Dierckx ◽  
Jesse Davis ◽  
Wim Schoutens
Keyword(s):  
Machine Learning ◽  
Mixture Models ◽  
Learning Algorithm ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Algorithm ◽  
Market Participants ◽  
Financial News ◽  
Volatility Index ◽  
Time Horizons

AbstractThe theory of Narrative Economics suggests that narratives present in media influence market participants and drive economic events. In this chapter, we investigate how financial news narratives relate to movements in the CBOE Volatility Index. To this end, we first introduce an uncharted dataset where news articles are described by a set of financial keywords. We then perform topic modeling to extract news themes, comparing the canonical latent Dirichlet analysis to a technique combining doc2vec and Gaussian mixture models. Finally, using the state-of-the-art XGBoost (Extreme Gradient Boosted Trees) machine learning algorithm, we show that the obtained news features outperform a simple baseline when predicting CBOE Volatility Index movements on different time horizons.

Improving the Shilling Attack Detection in Recommender Systems Using an SVM Gaussian Mixture Model

2019 ◽  
Vol 18 (01) ◽  
pp. 1950011 ◽  
Author(s):  
Jasem M. Alostad
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Recommender System ◽  
Detection Rate ◽  
Learning Algorithm ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Machine Learning Algorithm

With recent advances in e-commerce platforms, the information overload has grown due to increasing number of users, rapid generation of data and items in the recommender system. This tends to create serious problems in such recommender systems. The increasing features in recommender systems pose some new challenges due to poor resilience to mitigate against vulnerable attacks. In particular, the recommender systems are more prone to be attacked by shilling attacks, which creates more vulnerability. A recommender system with poor detection of attacks leads to a reduced detection rate. The performance of the recommender system is thus affected with poor detection ability. Hence, in this paper, we improve the resilience against shilling attacks using a modified Support Vector Machine (SVM) and a machine learning algorithm. The Gaussian Mixture Model is used as a machine learning algorithm to increase the detection rate and it further reduces the dimensionality of data in recommender systems. The proposed method is evaluated against several result metrics, such as the recall rate, precision rate and false positive rate between different attacks. The results of the proposed system are evaluated against probabilistic recommender approaches to demonstrate the efficacy of machine learning language in recommender systems.

scholarly journals Comparative Study of Young Open Clusters: Age Determination and Stellar Content

1980 ◽  
Vol 85 ◽  
pp. 245-245
Author(s):  
J. C. Mermilliod
Keyword(s):  
Comparative Study ◽  
Age Groups ◽  
Age Determination ◽  
Open Clusters ◽  
Red Giants ◽  
Be Stars ◽  
Stellar Content ◽  
Blue Stragglers ◽  
The Mean ◽  
Hr Diagram

75 young open clusters have been divided into 14 age groups on the basis of their MV/U-B diagrams. Composite HR diagrams have been constructed and empirical isochronous curves estimated (Figure 1). The left envelope defines a ZAMS. The mean position of the red giants in the HR diagram has been investigated, as well as the occurrence and location of Ap, Am and Be stars and of blue stragglers. Red giants appear mainly in clumps (shaded area).

scholarly journals On the self-consistent physical parameters of LMC intermediate-age clusters

2008 ◽  
Vol 4 (S256) ◽  
pp. 391-396
Author(s):  
Leandro O. Kerber ◽  
Basílio X. Santiago
Keyword(s):  
Stellar Population ◽  
Evolutionary Model ◽  
Open Clusters ◽  
Physical Parameters ◽  
Distance Modulus ◽  
Red Giants ◽  
Stellar Content ◽  
Self Consistent ◽  
Red Clump ◽  
Good Agreement

AbstractThe LMC clusters with similar ages to the Milky Way open clusters are in general more metal-poor and more populous than the latter, being located close enough to allow their stellar content to be well resolved. Therefore, they are unique templates of simple stellar population (SSP), being crucial to calibrate models describing the integral light as well as to test the stellar evolution theory. With this in mind we analyzed HST/WFPC2 (V, B − V) colour-magnitude diagrams (CMDs) of 15 populous LMC clusters with ages between ~0.3 Gyr and ~4 Gyr using different stellar evolutionary models. Following the approach described by Kerber, Santiago & Brocato (2007), we determined accurate and self-consistent physical parameters (age, metallicity, distance modulus and reddening) for each cluster by comparing the observed CMDs with synthetic ones generated using isochrones from the PEL and BaSTI libraries. These determinations were made by means of simultaneous statistical comparison of the main-sequence fiducial line and the red clump position, offering objective and robust criteria to select the best models. We compared these results with the ones obtained by Kerber, Santiago & Brocato (2007) using the Padova isochrones. This revealed that there are significant trends in the physical parameters due to the choice of stellar evolutionary model and treatment of convective core overshooting. In general, models that incorporate overshooting presented more reliable results than those that do not. Furthermore, the Padova models fitted better the data than the PEL and BaSTI models. Comparisons with the results found in the literature demonstrated that our derived metallicities are in good agreement with the ones from the spectroscopy of red giants. We also confirmed that, independent of the adopted stellar evolutionary library, the recovered 3D distribution for these clusters is consistent with a thick disk roughly aligned with the LMC disk as defined by field stars. Finally, we also provide new estimates of distance modulus to the LMC center, that are marginally consistent with the canonical value of 18.50 mag.

scholarly journals Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models

2018 ◽  
Author(s):  
Daniel Buscombe ◽  
Paul Grams
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Graphical Model ◽  
Learning Algorithm ◽  
Conditional Random Field ◽  
Gaussian Mixture ◽  
Generative Models ◽  
Generative Model ◽  
Machine Learning Algorithms ◽  
Acoustic Backscatter

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative machine learning algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of proximity and allowable backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model that doesn't include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF approach therefore may prove to be a powerful `spatially aware' alternative to other discriminative classifiers.

scholarly journals New light on the Gaia DR2 parallax zero-point: influence of the asteroseismic approach, in and beyond the Kepler field

2019 ◽  
Vol 628 ◽  
pp. A35 ◽  
Author(s):  
S. Khan ◽  
A. Miglio ◽  
B. Mosser ◽  
F. Arenou ◽  
K. Belkacem ◽  
...  
Keyword(s):  
Zero Point ◽  
Open Clusters ◽  
Eclipsing Binaries ◽  
Scaling Relations ◽  
Red Giants ◽  
The Third ◽  
Red Clump ◽  
Red Giant ◽  
Gaia Dr2 ◽  
Efficient Elimination

The importance of studying the Gaia DR2 parallax zero-point by external means was underlined by the articles that accompanied the release, and initiated by several works making use of Cepheids, eclipsing binaries, and asteroseismology. Despite a very efficient elimination of basic-angle variations, a small fluctuation remains and shows up as a small offset in the Gaia DR2 parallaxes. By combining astrometric, asteroseismic, spectroscopic, and photometric constraints, we undertake a new analysis of the Gaia parallax offset for nearly 3000 red-giant branch (RGB) and 2200 red clump (RC) stars observed by Kepler, as well as about 500 and 700 red giants (all either in the RGB or RC phase) selected by the K2 Galactic Archaeology Program in campaigns 3 and 6. Engaging in a thorough comparison of the astrometric and asteroseismic parallaxes, we are able to highlight the influence of the asteroseismic method, and measure parallax offsets in the Kepler field that are compatible with independent estimates from literature and open clusters. Moreover, adding the K2 fields to our investigation allows us to retrieve a clear illustration of the positional dependence of the zero-point, in general agreement with the information provided by quasars. Lastly, we initiate a two-step methodology to make progress in the simultaneous calibration of the asteroseismic scaling relations and of the Gaia DR2 parallax offset, which will greatly benefit from the gain in precision with the third data release of Gaia.

scholarly journals Blue Stragglers as Long-Lived Stars

1981 ◽  
Vol 93 ◽  
pp. 187-189
Author(s):  
J. Craig Wheeler ◽  
Michel Breger
Keyword(s):  
Main Sequence ◽  
Narrow Range ◽  
Core Region ◽  
Open Clusters ◽  
Apparent Mass ◽  
Core Mass ◽  
Hydrogen Burning ◽  
Blue Stragglers ◽  
The Core ◽  
Chandrasekhar Limit

The existence of blue stragglers in old open clusters with apparent mass more than twice the mass of the turnoff argues against simple binary mass transfer as the mechanism of their origin. The excess of blue stragglers to the red of the termination of the core hydrogen burning main sequence suggests that blue stragglers are not evolving normally. Stellar evolution models invoking mixing in an extended core region can account for the distribution of blue stragglers in the H-R diagram. Such models live longer, brightening and evolving further to the red before core hydrogen exhaustion than do normal stars. The distribution of blue stragglers in NGC 7789 is consistent with a range of mixed core mass fraction ~30–90 per cent and a narrow range in mass ~1.7–2.1 M⊙. Such evolution will result in a class of helium rich stars which have lived longer than normal and whose total mass exceeds the Chandrasekhar limit.

Sign in / Sign up

Export Citation Format

Share Document