Synteza spaleniowa nanowłókien SiC

Soszyński, Michał

doi:10.31338/uw.9788323516644.pp.44-53

Cited by 76 publications

(166 citation statements)

References 43 publications

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“…A good fraction of these stars have been monitored and classified by the Optical Gravitational Lensing Experiment (OGLE; Udalski et al 1992) survey, which observed mainly in a single passband (I-band) but with a more dense temporal sampling and a longer baseline. Cross-matching with the labeled variable stars in the Galactic Bulge from the OGLE collaboration, 4 specifically the variable-star catalogs from Udalski et al (1994Udalski et al ( , 1995b; Pietrukowicz et al (2015); Soszyński et al (2011aSoszyński et al ( ,b, 2013Soszyński et al ( , 2014Soszyński et al ( , 2015 covering OGLE I, III and IV, we found matches for 1228 (approximately half the sample) of the B1 variables. To avoid misidentification in very crowded field like the Galactic bulge, we use a conservative search radius of 1 for this cross-matching.…”

Section: Time-domain Data Samplesupporting

confidence: 55%

A Classification Algorithm for Time-domain Novelties in Preparation for LSST Alerts. Application to Variable Stars and Transients Detected with DECam in the Galactic Bulge

Soraisam

Saha

Matheson

et al. 2020

ApJ

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With the advent of the Large Synoptic Survey Telescope (LSST), time-domain astronomy will be faced with an unprecedented volume and rate of data. Real-time processing of variables and transients detected by such large-scale surveys is critical to identifying the more unusual events and allocating scarce follow-up resources efficiently. We develop an algorithm to identify these novel events within a given population of variable sources. We determine the distributions of magnitude changes (dm) over time intervals (dt) for a given passband f , p f (dm|dt), and use these distributions to compute the likelihood of a test source being consistent with the population, or an outlier. We demonstrate our algorithm by applying it to the DECam multi-band time-series data of more than 2000 variable stars identified by Saha et al. (2019) in the Galactic Bulge that are largely dominated by long-period variables and pulsating stars. Our algorithm discovers 18 outlier sources in the sample, including a microlensing event, a dwarf nova, and two chromospherically active RS CVn stars, as well as sources in the Blue Horizontal Branch region of the color-magnitude diagram without any known counterparts. We compare the performance of our algorithm for novelty detection with multivariate KDE and Isolation Forest on the simulated PLAsTiCC dataset. We find that our algorithm yields comparable results despite its simplicity. Our method provides an efficient way for flagging the most unusual events in a real-time alert-broker system.

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Section: Time-domain Data Samplesupporting

confidence: 55%

A Classification Algorithm for Time-domain Novelties in Preparation for LSST Alerts. Application to Variable Stars and Transients Detected with DECam in the Galactic Bulge

Soraisam

Saha

Matheson

et al. 2020

ApJ

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“…The majority of these stars 22 have kinematics, abundances and pulsation properties that link them to the Galactic disc. It is important to note that our sample does not contain RR Lyrae stars found independently in the Galactic disc based on a spatial distribution study (using the Vía Láctea survey Minniti et al 2010;Dékány et al 2018) nor in the direction of the Galactic bulge (Soszyński et al 2014(Soszyński et al , 2017a. This may have, to some extent, affected the selection function of our sample and the observed peak in the pericenter distance dis-tribution.…”

Section: Discussionmentioning

confidence: 95%

“…For this comparison we used the V-band photometry from the fourth data release of the Optical Gravitational Lensing Experiment (hereafter OGLE-IV, Soszyński et al 2014Soszyński et al , 2017a for the Galactic bulge RR Lyrae stars. The decision to use the bulge RR Lyrae variables was based on their assumed metallicity (similar to the metallicity of the pericenter peak RR Lyrae stars [Fe/H] = −1.02 dex, Pietrukowicz et al 2015), and high-quality photometry for over 27 000 fundamental mode RR Lyrae stars.…”

Section: Possibility Of Misclassificationmentioning

confidence: 99%

“…In the three panels depicting the pulsation properties, the green triangles represent classical first overtone Cepheids from the Galactic bulge and Large Magellanic Cloud, and squares stand for anomalous Cepheids from the Large Magellanic Cloud (Soszyński et al 2015a). The underlying gray distribution in the panels represents the pulsation parameters from the OGLE-IV V -band photometry (Soszyński et al 2014(Soszyński et al , 2017a for RR Lyrae stars.…”

Section: Possibility Of Misclassificationmentioning

confidence: 99%

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Evidence for Galactic disc RR Lyrae stars in the solar neighbourhood

Prudil

Dékány

Grebel

et al. 2020

Monthly Notices of the Royal Astronomical Society

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We present a kinematical study of 314 RR Lyrae stars in the solar neighbourhood using the publicly available photometric, spectroscopic, and Gaia DR2 astrometric data to explore their distribution in the Milky Way. We report an overdensity of 22 RR Lyrae stars in the solar neighbourhood at a pericenter distance of between 5-9 kpc from the Galactic center. Their orbital parameters and their chemistry indicate that these 22 variables share the kinematics and the [Fe/H] values of the Galactic disc, with an average metallicity and tangential velocity of [Fe/H]=−0.60 dex and v θ = 241 km s −1 , respectively. From the distribution of the Galactocentric spherical velocity components, we find that these 22 disc-like RR Lyrae variables are not consistent with the Gaia Sausage (Gaia-Enceladus), unlike almost half of the local RR Lyrae stars. Chemical information from the literature shows that the majority of the selected pericenter peak RR Lyrae variables are α-poor, a property shared by typically much younger stars in the thin disc. Using the available photometry we rule out a possible misclassification with the known classical and anomalous Cepheids. The similar kinematic, chemical, and pulsation properties of these disc RR Lyrae stars suggest they share a common origin. In contrast, we find the RR Lyrae stars associated with the Gaia-Enceladus based on their kinematics and chemical composition show a considerable metallicity spread in the old population (∼ 1 dex).

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“…To obtain the labels for the light curves, we perform a cross-match between OGLE-III LPVs (Soszyński et al 2009(Soszyński et al , 2011(Soszyński et al , 2013, OGLE-IV eclipsing binaries (Pawlak et al 2016;Soszyński et al 2016b) and RR Lyraes (Soszyński et al 2016a(Soszyński et al , 2014, and the Gaia catalogue of variable stars using the already available cross-match with WISE objects (Marrese et al 2019). In addition to the previous classes described for OGLE-III and Gaia, NC corresponds to noncontact binaries from OGLE-IV.…”

Section: Wide-field Infrared Survey Explorer (Wise)mentioning

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

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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).

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Synteza spaleniowa nanowłókien SiC

Cited by 76 publications

References 43 publications

A Classification Algorithm for Time-domain Novelties in Preparation for LSST Alerts. Application to Variable Stars and Transients Detected with DECam in the Galactic Bulge

A Classification Algorithm for Time-domain Novelties in Preparation for LSST Alerts. Application to Variable Stars and Transients Detected with DECam in the Galactic Bulge

Evidence for Galactic disc RR Lyrae stars in the solar neighbourhood

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

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