TY - JOUR AB - Brightness variations due to dark spots on the stellar surface encode information about stellar surface rotation and magnetic activity. In this work, we analyze the Kepler long-cadence data of 26,521 main-sequence stars of spectral types M and K in order to measure their surface rotation and photometric activity level. Rotation-period estimates are obtained by the combination of a wavelet analysis and autocorrelation function of the light curves. Reliable rotation estimates are determined by comparing the results from the different rotation diagnostics and four data sets. We also measure the photometric activity proxy Sph using the amplitude of the flux variations on an appropriate timescale. We report rotation periods and photometric activity proxies for about 60% of the sample, including 4431 targets for which McQuillan et al. did not report a rotation period. For the common targets with rotation estimates in this study and in McQuillan et al., our rotation periods agree within 99%. In this work, we also identify potential polluters, such as misclassified red giants and classical pulsator candidates. Within the parameter range we study, there is a mild tendency for hotter stars to have shorter rotation periods. The photometric activity proxy spans a wider range of values with increasing effective temperature. The rotation period and photometric activity proxy are also related, with Sph being larger for fast rotators. Similar to McQuillan et al., we find a bimodal distribution of rotation periods. AU - Santos, A. R. G. AU - García, R. A. AU - Mathur, S. AU - Bugnet, Lisa Annabelle AU - van Saders, J. L. AU - Metcalfe, T. S. AU - Simonian, G. V. A. AU - Pinsonneault, M. H. ID - 11623 IS - 1 JF - The Astrophysical Journal Supplement Series KW - Space and Planetary Science KW - Astronomy and Astrophysics KW - methods: data analysis KW - stars: activity KW - stars: low-mass KW - stars: rotation KW - starspots KW - techniques: photometric SN - 0067-0049 TI - Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars VL - 244 ER - TY - GEN AB - For a solar-like star, the surface rotation evolves with time, allowing in principle to estimate the age of a star from its surface rotation period. Here we are interested in measuring surface rotation periods of solar-like stars observed by the NASA mission Kepler. Different methods have been developed to track rotation signals in Kepler photometric light curves: time-frequency analysis based on wavelet techniques, autocorrelation and composite spectrum. We use the learning abilities of random forest classifiers to take decisions during two crucial steps of the analysis. First, given some input parameters, we discriminate the considered Kepler targets between rotating MS stars, non-rotating MS stars, red giants, binaries and pulsators. We then use a second classifier only on the MS rotating targets to decide the best data analysis treatment. AU - Breton, S. N. AU - Bugnet, Lisa Annabelle AU - Santos, A. R. G. AU - Saux, A. Le AU - Mathur, S. AU - Palle, P. L. AU - Garcia, R. A. ID - 11627 KW - asteroseismology KW - rotation KW - solar-like stars KW - kepler KW - machine learning KW - random forest T2 - arXiv TI - Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques ER - TY - GEN AB - The second mission of NASA’s Kepler satellite, K2, has collected hundreds of thousands of lightcurves for stars close to the ecliptic plane. This new sample could increase the number of known pulsating stars and then improve our understanding of those stars. For the moment only a few stars have been properly classified and published. In this work, we present a method to automaticly classify K2 pulsating stars using a Machine Learning technique called Random Forest. The objective is to sort out the stars in four classes: red giant (RG), main-sequence Solar-like stars (SL), classical pulsators (PULS) and Other. To do this we use the effective temperatures and the luminosities of the stars as well as the FliPer features, that measures the amount of power contained in the power spectral density. The classifier now retrieves the right classification for more than 80% of the stars. AU - Saux, A. Le AU - Bugnet, Lisa Annabelle AU - Mathur, S. AU - Breton, S. N. AU - Garcia, R. A. ID - 11630 KW - asteroseismology - methods KW - data analysis - thecniques KW - machine learning - stars KW - oscillations T2 - arXiv TI - Automatic classification of K2 pulsating stars using machine learning techniques ER - TY - CONF AB - The diameter, radius and eccentricities are natural graph parameters. While these problems have been studied extensively, there are no known dynamic algorithms for them beyond the ones that follow from trivial recomputation after each update or from solving dynamic All-Pairs Shortest Paths (APSP), which is very computationally intensive. This is the situation for dynamic approximation algorithms as well, and even if only edge insertions or edge deletions need to be supported. This paper provides a comprehensive study of the dynamic approximation of Diameter, Radius and Eccentricities, providing both conditional lower bounds, and new algorithms whose bounds are optimal under popular hypotheses in fine-grained complexity. Some of the highlights include: - Under popular hardness hypotheses, there can be no significantly better fully dynamic approximation algorithms than recomputing the answer after each update, or maintaining full APSP. - Nearly optimal partially dynamic (incremental/decremental) algorithms can be achieved via efficient reductions to (incremental/decremental) maintenance of Single-Source Shortest Paths. For instance, a nearly (3/2+epsilon)-approximation to Diameter in directed or undirected n-vertex, m-edge graphs can be maintained decrementally in total time m^{1+o(1)}sqrt{n}/epsilon^2. This nearly matches the static 3/2-approximation algorithm for the problem that is known to be conditionally optimal. AU - Ancona, Bertie AU - Henzinger, Monika H AU - Roditty, Liam AU - Williams, Virginia Vassilevska AU - Wein, Nicole ID - 11826 SN - 1868-8969 T2 - 46th International Colloquium on Automata, Languages, and Programming TI - Algorithms and hardness for diameter in dynamic graphs VL - 132 ER - TY - CONF AB - Modern networked systems are increasingly reconfigurable, enabling demand-aware infrastructures whose resources can be adjusted according to the workload they currently serve. Such dynamic adjustments can be exploited to improve network utilization and hence performance, by moving frequently interacting communication partners closer, e.g., collocating them in the same server or datacenter. However, dynamically changing the embedding of workloads is algorithmically challenging: communication patterns are often not known ahead of time, but must be learned. During the learning process, overheads related to unnecessary moves (i.e., re-embeddings) should be minimized. This paper studies a fundamental model which captures the tradeoff between the benefits and costs of dynamically collocating communication partners on l servers, in an online manner. Our main contribution is a distributed online algorithm which is asymptotically almost optimal, i.e., almost matches the lower bound (also derived in this paper) on the competitive ratio of any (distributed or centralized) online algorithm. AU - Henzinger, Monika H AU - Neumann, Stefan AU - Schmid, Stefan ID - 11850 SN - 978-1-4503-6678-6 T2 - SIGMETRICS'19: International Conference on Measurement and Modeling of Computer Systems TI - Efficient distributed workload (re-)embedding ER -