Combining Kepler, TESS and ground based data for characterising exoplanets and stellar activity

2020 ◽  
Author(s):  
Victoria Foing ◽  
Ana Heras ◽  
Bernard Foing
Keyword(s):  
Machine Learning ◽  
Functional Form ◽  
Component Model ◽  
Stellar Activity ◽  
Machine Learning Methods ◽  
Rotation Periods ◽  
Monte Carlo Algorithms ◽  
Rotational Modulation ◽  
Activity Cycles ◽  
Transit Signals

<p class="x_MsoNormal">This work compares the information obtained from TESS and Kepler lightcurves, and integrates information obtained from ground based observatories. We apply Machine learning methods for modelling stellar and instrumental systematics in lightcurves because they can quickly identify patterns in data without prior knowledge of the functional form. We use a Gaussian Process to model the stellar activity, background granulation, and transit signals simultaneously because we expect that using a multi-component model can improve planetary characterisation.  This work seeks to address the following questions:</p> <p class="x_MsoNormal">RQ1: How accurately can we model the stellar activity and transit signals in TESS and Kepler lightcurves with machine learning?</p> <p class="x_MsoNormal">RQ2: To what extent can we use these models to interpret the rotation periods and activity cycles of the stars?</p> <p class="x_MsoNormal">RQ3: To what extent can we use these models to detrend the lightcurves and improve transit exoplanet characterization?</p> <p class="x_MsoNormal">The model is initialized using information from Box Least Squares, LombScargle analysis, and Autocorrelation functions, and then Markov Chain Monte Carlo algorithms are run to fit rotational modulation parameters and planet parameters.  We compare the results of this method across different missions (TESS and Kepler) and compare the results of this method with results obtained from ground based surveys. We illustrate the comparison and the astrophysical results in the case of WASP62 and Kepler 78 targets.</p> <p> </p>

scholarly journals Transition from spot to faculae domination

2018 ◽  
Vol 621 ◽  
pp. A21 ◽  
Author(s):  
Timo Reinhold ◽  
Keaton J. Bell ◽  
James Kuszlewicz ◽  
Saskia Hekker ◽  
Alexander I. Shapiro
Keyword(s):  
Time Series ◽  
Phase Difference ◽  
Process Model ◽  
Activity Cycle ◽  
Magnetic Activity ◽  
Cycle Period ◽  
Stellar Activity ◽  
Rotation Periods ◽  
Active Stars ◽  
Activity Cycles

Context. The study of stellar activity cycles is crucial to understand the underlying dynamo and how it causes magnetic activity signatures such as dark spots and bright faculae. Having knowledge about the dominant source of surface activity might allow us to draw conclusions about the stellar age and magnetic field topology, and to put the solar cycle in context. Aims. We investigate the underlying process that causes magnetic activity by studying the appearance of activity signatures in contemporaneous photometric and chromospheric time series. Methods. Lomb-Scargle periodograms are used to search for cycle periods present in the photometric and chromospheric time series. To emphasize the signature of the activity cycle we account for rotation-induced scatter in both data sets by fitting a quasi-periodic Gaussian process model to each observing season. After subtracting the rotational variability, cycle amplitudes and the phase difference between the two time series are obtained by fitting both time series simultaneously using the same cycle period. Results. We find cycle periods in 27 of the 30 stars in our sample. The phase difference between the two time series reveals that the variability in fast-rotating active stars is usually in anti-phase, while the variability of slowly rotating inactive stars is in phase. The photometric cycle amplitudes are on average six times larger for the active stars. The phase and amplitude information demonstrates that active stars are dominated by dark spots, whereas less-active stars are dominated by bright faculae. We find the transition from spot to faculae domination to be at the Vaughan–Preston gap, and around a Rossby number equal to one. Conclusions. We conclude that faculae are the dominant ingredient of stellar activity cycles at ages ≳2.55 Gyr. The data further suggest that the Vaughan–Preston gap cannot explain the previously detected dearth of Kepler rotation periods between 15 and 25 days. Nevertheless, our results led us to propose an explanation for the lack of rotation periods to be due to the non-detection of periodicity caused by the cancelation of dark spots and bright faculae at ∼800 Myr.

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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