Classification of Variable Stars Using Thick-Pen Transform Method

Park, M.; Oh, Hee‐Seok; Kim, D.

doi:10.1086/670671

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Within astronomy in particular RF classification is one of the more widely-employed methods of machine-learning, though many alternatives exist. For example, Masci et al (2014) use the RF method for variable-star lightcurve classification, while others have approached this problem via the use of, e.g., support vector machines (Woźniak et al 2004), Kohonen self-organizing maps (Brett et al 2004), Bayesian networks and mixture-models (Mahabal et al 2008), principle component analysis (Deb & Singh 2009), multivariate Bayesian and Gaussian mixture models (Blomme et al 2011), and thick-pen transform methods (Park et al 2013).…”

Section: Machine Learningmentioning

confidence: 99%

Asteroid Light Curves From the Palomar Transient Factory Survey: Rotation Periods and Phase Functions From Sparse Photometry

Waszczak

Chang

Ofek

et al. 2015

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We fit 54,296 sparsely-sampled asteroid lightcurves in the Palomar Transient Factory (PTF) survey to a combined rotation plus phase-function model. Each lightcurve consists of 20 or more observations acquired in a single opposition. Using 805 asteroids in our sample that have reference periods in the literature, we find the reliability of our fitted periods is a complicated function of the period, amplitude, apparent magnitude and other lightcurve attributes. Using the 805-asteroid ground-truth sample, we train an automated classifier to estimate (along with manual inspection) the validity of the remaining ∼53,000 fitted periods. By this method we find 9,033 of our lightcurves (of ∼8,300 unique asteroids) have 'reliable' periods. Subsequent consideration of asteroids with multiple lightcurve fits indicate a 4% contamination in these 'reliable' periods. For 3,902 lightcurves with sufficient phase-angle coverage and either a reliably-fit period or low amplitude, we examine the distribution of several phase-function parameters, none of which are bimodal though all correlate with the bond albedo and with visible-band colors. Comparing the theoretical maximal spin rate of a fluid body with our amplitude versus spin-rate distribution suggests that, if held together only by self-gravity, most asteroids are in general less dense than ∼2 g/cm 3 , while C types have a lower limit of between 1 and 2 g/cm 3 . These results are in agreement with previous density estimates. For 5-20 km diameters, S types rotate faster and have lower amplitudes than C types. If both populations share the same angular momentum, this may indicate the two types' differing ability to deform under rotational stress. Lastly, we compare our absolute magnitudes (and apparent-magnitude residuals) to those of the Minor Planet Center's nominal (G = 0.15, rotation-neglecting) model; our phase-function plus Fourier-series fitting reduces asteroid photometric RMS scatter by a factor ∼3.

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Section: Machine Learningmentioning

confidence: 99%

Asteroid Light Curves From the Palomar Transient Factory Survey: Rotation Periods and Phase Functions From Sparse Photometry

Waszczak

Chang

Ofek

et al. 2015

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“…RF classification has extensive and diverse applications in many fields (e.g., economics, bioinformatics, sociology). Within astronomy in particular RF clas- (Blomme et al 2011), and thick-pen transform methods (Park et al 2013).…”

Section: Machine-learned Classificationmentioning

confidence: 99%

“…Within astronomy in particular RF clas- sification is one of the more widely-employed methods of machine-learning, though many alternatives exist. For example, Masci et al (2014) use the RF method for variable-star lightcurve classification, while others have approached this problem via the use of, e.g., support vector machines (Woźniak et al 2004), Kohonen self-organizing maps (Brett et al 2004;Masters et al 2015), Bayesian networks and mixturemodels (Mahabal et al 2008), principlal component analysis (Deb & Singh 2009), multivariate Bayesian and Gaussian mixture models (Blomme et al 2011), and thick-pen transform methods (Park et al 2013).…”

Section: Completeness and Contaminationmentioning

confidence: 99%

Small Near-Earth Asteroids in the Palomar Transient Factory Survey: A Real-Time Streak-detection System

Waszczak

Prince

Laher

et al. 2017

PASP

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Near-Earth asteroids (NEAs) in the 1-100 meter size range are estimated to be ∼1,000 times more numerous than the ∼15,000 currently-catalogued NEAs, most of which are in the 0.5-10 kilometer size range. Impacts from 10-100 meter size NEAs are not statistically life-threatening but may cause significant regional damage, while 1-10 meter size NEAs with low velocities relative to Earth are compelling targets for space missions. We describe the implementation and initial results of a real-time NEA-discovery system specialized for the detection of small, high angular rate (visually-streaked) NEAs in Palomar Transient Factory (PTF) images. PTF is a 1.2-m aperture, 7.3-deg 2 field-of-view optical survey designed primarily for the discovery of extragalactic transients (e.g., supernovae) in 60-second exposures reaching ∼20.5 visual magnitude. Our real-time NEA discovery pipeline uses a machine-learned classifier to filter a large number of false-positive streak detections, permitting a human scanner to efficiently and remotely identify real asteroid streaks during the night. Upon recognition of a streaked NEA detection (typically within an hour of the discovery exposure), the scanner triggers follow-up with the same telescope and posts the observations to the Minor Planet Center for worldwide confirmation. We describe our ten initial confirmed discoveries, all small NEAs that passed 0.3-15 lunar distances from Earth. Lastly, we derive useful scaling laws for comparing streaked-NEA-detection capabilities of different surveys as a function of their hardware and survey-pattern characteristics. This work most directly informs estimates of the streak-detection capabilities of the Zwicky Transient Facility (ZTF, planned to succeed PTF in 2017), which will apply PTF's current resolution and sensitivity over a 47-deg 2 field-of-view.

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Machine learning techniques to select Be star candidates

Pérez-Ortiz

García-Varela

Quiróz

et al. 2017

A&A

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Context. Optical and infrared variability surveys produce a large number of high quality light curves. Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to train an automatic classification system. Quantities related to the magnitude density of the light curves and their Fourier coefficients have been chosen as features in previous studies. However, some of these features are not robust to the presence of outliers and the calculation of Fourier coefficients is computationally expensive for large data sets. Aims. We propose and evaluate the performance of a new robust set of features using supervised classifiers in order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic pole field. Methods. We calculated the proposed set of features on six types of variable stars and also on a set of Be star candidates reported in the literature. We evaluated the performance of these features using classification trees and random forests along with the K-nearest neighbours, support vector machines, and gradient boosted trees methods. We tuned the classifiers with a 10-fold cross-validation and grid search. We then validated the performance of the best classifier on a set of OGLE-IV light curves and applied this to find new Be star candidates. Results. The random forest classifier outperformed the others. By using the random forest classifier and colours criteria we found 50 Be star candidates in the direction of the Gaia south ecliptic pole field, four of which have infrared colours that are consistent with Herbig Ae/Be stars. Conclusions. Supervised methods are very useful in order to obtain preliminary samples of variable stars extracted from large databases. As usual, the stars classified as Be stars candidates must be checked for the colours and spectroscopic characteristics expected for them.

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Classification of Variable Stars Using Thick-Pen Transform Method

Cited by 4 publications

References 20 publications

Asteroid Light Curves From the Palomar Transient Factory Survey: Rotation Periods and Phase Functions From Sparse Photometry

Asteroid Light Curves From the Palomar Transient Factory Survey: Rotation Periods and Phase Functions From Sparse Photometry

Small Near-Earth Asteroids in the Palomar Transient Factory Survey: A Real-Time Streak-detection System

Machine learning techniques to select Be star candidates

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