A machine learned classifier for RR Lyrae in the VVV survey

Elorrieta, Felipe; Eyheramendy, S.; Jordán, Andrés; Dékány, I.; Catelan, M.; Angeloni, R.; Alonso-García, J.; Contreras-Ramos, Rodrigo; Gran, F.; Hajdu, G.; Espinoza, N.; Saito, R. K.; Minniti, D.

doi:10.1051/0004-6361/201628700

Cited by 45 publications

(62 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quoted circumstantial evidence indicates that we still lack a homogenous and detailed analysis of the Bailey diagram as a function of the metal content. In this context it is worth mentioning that we are neglecting the metallicity estimates based either on photometric indices such as the inversion of the PL relation (Braga et al 2016;Martínez-Vázquez et al 2016;Bono et al 2019) or the Fourier decomposition of the light curve (Jurcsik & Kovacs 1996;Nemec et al 2013;Elorrieta et al 2016;Hajdu et al 2018). Data plotted in Fig.…”

Section: The Fine Structure Of the Bailey Diagrammentioning

confidence: 99%

On the Use of Field RR Lyrae as Galactic Probes. I. The Oosterhoff Dichotomy Based on Fundamental Variables*

Fabrizio

Bono

Braga

et al. 2019

ApJ

View full text Add to dashboard Cite

We collected a large data set of field RR Lyrae stars (RRLs) by using catalogues already available in the literature and Gaia DR2. We estimated the iron abundances for a sub-sample of 2,382 fundamental RRLs (∆S method: Ca II K, Hβ, Hγ and Hδ lines) for which are publicly available medium-resolution SDSS-SEGUE spectra. We also included similar estimates available in the literature ending up with the largest and most homogeneous spectroscopic data set ever collected for RRLs (2,903). The metallicity scale was validated by using iron abundances based on high resolution spectra for a fundamental field RRL (V Ind), for which we collected X-shooter spectra covering the entire pulsation cycle. The peak ([Fe/H]=-1.59±0.01) and the standard deviation (σ=0.43 dex) of the metallicity distribution agree quite well with similar estimates available in the literature. The current measurements disclose a well defined metal-rich tail approaching Solar iron abundance. The spectroscopic sample plotted in the Bailey diagram (period vs luminosity amplitude) shows a steady variation when moving from the metal-poor ([Fe/H]=-3.0/-2.5) to the metal-rich ([Fe/H]=-0.5/0.0) regime. The smooth transition in the peak of the period distribution as a function of the metallicity strongly indicates that the longstanding problem of the Oosterhoff dichotomy among Galactic globulars is the consequence of the lack of metal-intermediate clusters hosting RRLs. We also found that the luminosity amplitude, in contrast Corresponding author: Michele Fabrizio michele.fabrizio@ssdc.asi.it * Partially based on observations collected under ESO programmes 297.D-5047 (PI. G. Bono) and 083.B-0281 (PI. D. Romano). arXiv:1908.02064v2 [astro-ph.SR] 7 Aug 2019 M. Fabrizio et al.with period, does not show a solid correlation with metallicity. This suggests that period-amplitudemetallicity relations should be cautiously treated.

show abstract

Section: The Fine Structure Of the Bailey Diagrammentioning

confidence: 99%

On the Use of Field RR Lyrae as Galactic Probes. I. The Oosterhoff Dichotomy Based on Fundamental Variables*

Fabrizio

Bono

Braga

et al. 2019

ApJ

View full text Add to dashboard Cite

show abstract

“…For example, Dubath et al (2011) applied Random Forest classification to Hipparcos data, while Richards et al (2012) applied the same general technique to techniques to automatically classify variable stars from the ASAS survey. In contrast, Palaversa et al (2013) used support vector machine (Cortes & Vapnik 1995) based machine learning to classify periodic variables from the LINEAR survey, and Elorrieta et al (2016) have found that the AdaBoost technique (Freund & Schapire 1997) provides encouraging results in the case of the NIR light curves obtained by the Vista Variables in the Vía Láctea survey (Minniti et al 2010).…”

Section: Automated Pre-classificationmentioning

confidence: 99%

The Catalina Surveys Southern periodic variable star catalogue

Drake

Djorgovski

Catelan

et al. 2017

Monthly Notices of the Royal Astronomical Society

166

133

View full text Add to dashboard Cite

Here, we present the results from our analysis of 6 yr of optical photometry taken by the Siding Spring Survey (SSS). This completes a search for periodic variable stars within the 30 000 deg 2 of the sky covered by the Catalina Surveys. The current analysis covers 81 million sources with declinations between −20 • and −75 • with median magnitudes in the range 11 < V < 19.5. We find approximately 34 000 new periodic variable stars in addition to the ∼9000 RR Lyrae that we previously discovered in SSS data. This brings the total number of periodic variables identified in Catalina data to ∼110 000. The new SSS periodic variable stars mainly consist of eclipsing binaries, RR Lyrae, LPVs, RS CVn stars, δ Scutis, and Anomalous Cepheids. By cross-matching these variable stars with those from prior surveys, we find that ∼90 per cent of the sources are new discoveries and recover ∼95 per cent of the known periodic variables in the survey region. For the known sources, we find excellent agreement between our catalogue and prior values of luminosity, period, and amplitude. However, we find many variable stars that had previously been misclassified. Examining the distribution of RR Lyrae, we find a population associated with the Large Magellanic Cloud (LMC) that extends more than 20 • from its centre confirming recent evidence for the existence of a very extended stellar halo in the LMC. By combining SSS photometry with Dark Energy Survey data, we identify additional LMC halo RR Lyrae, thus confirming the significance of the population.

show abstract

“…The same year Richards et al (2011) obtained the same conclusions using 53 features. Recent works still apply the same techniques to find specific classes of variability (Elorrieta et al 2016;Gran et al 2016). Pichara et al (2012), working with the EROS database (Beaulieu et al 1995), included autoregressive features which prove valuable for QSO classification.…”

Section: Introductionmentioning

confidence: 99%

Scalable end-to-end recurrent neural network for variable star classification

Becker

Pichara

Catelan

et al. 2020

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as input for many algorithms. Some features are computationally expensive, cannot be updated quickly and hence for large datasets such as the LSST cannot be applied. Previous work has been done to develop alternative unsupervised feature extraction algorithms for light curves, but the cost of doing so still remains high. In this work, we propose an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification. We study a series of deep learning architectures based on Recurrent Neural Networks and test them in automated classification scenarios. Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets. We transform each light curve into an input matrix representation whose elements are the differences in time and magnitude, and the outputs are classification probabilities. We test our method in three surveys: OGLE-III, Gaia and WISE. We obtain accuracies of about 95% in the main classes and 75% in the majority of subclasses. We compare our results with the Random Forest classifier and obtain competitive accuracies while being faster and scalable. The analysis shows that the computational complexity of our approach grows up linearly with the light curve size, while the traditional approach cost grows as N log (N).

show abstract

A machine learned classifier for RR Lyrae in the VVV survey

Cited by 45 publications

References 29 publications

On the Use of Field RR Lyrae as Galactic Probes. I. The Oosterhoff Dichotomy Based on Fundamental Variables*

On the Use of Field RR Lyrae as Galactic Probes. I. The Oosterhoff Dichotomy Based on Fundamental Variables*

The Catalina Surveys Southern periodic variable star catalogue

Scalable end-to-end recurrent neural network for variable star classification

Contact Info

Product

Resources

About