BRITE Constellation: data processing and photometry

Popowicz, Adam; Pigulski, A.; Bernacki, Krzysztof; Kuschnig, R.; Pablo, H.; Ramiaramanantsoa, Tahina; Zocłońska, E.; Baade, D.; Handler, G.; Moffat, A. F. J.; Wade, G. A.; Neiner, C.; Ruciński, S. M.; Weiß, W. W.; Koudelka, Otto; Orleański, P.; Schwarzenberg‐Czerny, A.; Zwintz, K.

doi:10.1051/0004-6361/201730806

Cited by 61 publications

(80 citation statements)

References 24 publications

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“…One may be interested in identifying stars more variable than the Sun (McQuillan, Aigrain & Roberts 2012) or the ones showing periodic variability (Debosscher et al 2009(Debosscher et al , 2011) -these problems require a different set of light curve features than the ones considered here. The variability detection approach presented here will likely not be useful for space astroseismology missions like MOST (Walker et al 2003), BRITE (Weiss et al 2014, Pablo et al 2016, Popowicz et al 2017) and the upcoming transit photometry mission CHEOPS (Broeg et al 2013) as they observe (with superior accuracy) only one or few stars at a time.…”

Section: Applicability To Other Photometric Data Setsmentioning

confidence: 99%

Machine learning search for variable stars

Pashchenko

Sokolovsky

Gavras

2017

Monthly Notices of the Royal Astronomical Society

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Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SV M), k Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF) and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC) photometry (30265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SV M and RF show a higher efficiency compared to LR and kNN.

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Section: Applicability To Other Photometric Data Setsmentioning

confidence: 99%

Machine learning search for variable stars

Pashchenko

Sokolovsky

Gavras

2017

Monthly Notices of the Royal Astronomical Society

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“…The strength of these new observations lies in the long time base and moderately high cadence, primarily enabling us to better investigate whether the star truly exhibits coherent (multi-)periodicity or if not, then to reveal the true source of the variability. The different modes of observations for BRITE are described by Pablo et al (2016) and Popowicz et al (2017).…”

Section: Introductionmentioning

confidence: 99%

The chaotic wind of WR 40 as probed by BRITE

Ramiaramanantsoa

Ignace

Moffat

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Among Wolf-Rayet stars, those of subtype WN8 are the intrinsically most variable. We have explored the long-term photometric variability of the brightest known WN8 star, WR 40, through four contiguous months of time-resolved, single-passband optical photometry with the BRIght Target Explorer (BRITE) nanosatellite mission. The Fourier transform of the observed light-curve reveals that the strong light variability exhibited by WR 40 is dominated by many randomly-triggered, transient, low-frequency signals. We establish a model in which the whole wind consists of stochastic clumps following an outflow visibility promptly rising to peak brightness upon clump emergence from the optically thick pseudo-photosphere in the wind, followed by a gradual decay according to the right-half of a Gaussian. Free electrons in each clump scatter continuum light from the star. We explore a scenario where the clump size follows a power-law distribution, and another one with an ensemble of clumps of constant size. Both scenarios yield simulated light curves morphologically resembling the observed light curve remarkably well, indicating that one cannot uniquely constrain the details of clump size distribution with only a photometric light curve. Nevertheless, independent evidence favours a negative-index power law, as seen in many other astrophysical turbulent media.

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“…A short summary of the characteristics of the BRITE data is given in Table 2. The photometry was obtained by means of the photometric pipeline described by Popowicz et al (2017) and then corrected for instrumental effects according to the procedure described by Pigulski (2018). The complete reduced BRITE dataset spans 173.5 days and consists of 152 112 photometric measurements.…”

Section: Space Photometric Observationsmentioning

confidence: 99%

Evolving pulsation of the slowly rotating magnetic β Cep star ξ1 CMa

Wade

Pigulski

Begy

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Recent BRITE-Constellation space photometry of the slowly rotating, magnetic β Cep pulsator ξ 1 CMa permits a new analysis of its pulsation properties. Analysis of the twocolour BRITE data reveals the well-known single pulsation period of 0.209 d, along with its first and second harmonics. A similar analysis of SMEI and TESS observations yields compatible results, with the higher precision TESS observations also revealing several low-amplitude modes with frequencies below 5 d −1 ; some of these are likely g modes. The phase lag between photometric and radial velocity maxima -equal to 0.334 cycles -is significantly larger than the typical value of 1/4 observed in other large-amplitude β Cep stars. The phase lag, as well as the strong dependence of phase of maximum light on wavelength, can be reconciled with seismic models only if the dominant mode is the fundamental radial mode. We employ all published photometric and radial velocity measurements, spanning over a century, to evaluate the stability of the pulsation period. The O − C diagram exhibits a clear parabolic shape consistent with a mean rate of period change P = 0.34±0.02 s/cen. The residuals from the best-fit parabola exhibit scatter that is substantially larger than the uncertainties. In particular, dense sampling obtained during the past ∼20 years suggests more complex and rapid period variations. Those data cannot be coherently phased with the mean rate of period change, and instead require P ∼ 0.9 s/cen. We examine the potential contributions of binarity, stellar evolution, and stellar rotation and magnetism to understand the apparent period evolution.

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BRITE Constellation: data processing and photometry

Cited by 61 publications

References 24 publications

Machine learning search for variable stars

Machine learning search for variable stars

The chaotic wind of WR 40 as probed by BRITE

Evolving pulsation of the slowly rotating magnetic β Cep star ξ1 CMa

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