Extrasolar-system planet detection problem: radiometric signal and resolution considerations

Author(s):  
Gonzalo Paez ◽  
Marija S. Scholl
Author(s):  
Faith Ellen ◽  
Rati Gelashvili ◽  
Philipp Woelfel ◽  
Leqi Zhu

2010 ◽  
Vol 6 (S276) ◽  
pp. 527-529
Author(s):  
Xavier Dumusque ◽  
Nuno C. Santos ◽  
Stéphane Udry ◽  
Cristophe Lovis ◽  
Xavier Bonfils

AbstractSpectrographs like HARPS can now reach a sub-ms−1 precision in radial-velocity (RV) (Pepe & Lovis 2008). At this level of accuracy, we start to be confronted with stellar noise produced by 3 different physical phenomena: oscillations, granulation phenomena (granulation, meso- and super-granulation) and activity. On solar type stars, these 3 types of perturbation can induce ms−1 RV variation, but on different time scales: 3 to 15 minutes for oscillations, 15 minutes to 1.5 days for granulation phenomena and 10 to 50 days for activity. The high precision observational strategy used on HARPS, 1 measure per night of 15 minutes, on 10 consecutive days each month, is optimized, due to a long exposure time, to average out the noise coming from oscillations (Dumusque et al. 2011a) but not to reduce the noise coming from granulation and activity (Dumusque et al. 2011a and Dumusque et al. 2011b). The smallest planets found with this strategy (Mayor et al. 2009) seems to be at the limit of the actual observational strategy and not at the limit of the instrumental precision. To be able to find Earth mass planets in the habitable zone of solar-type stars (200 days for a K0 dwarf), new observational strategies, averaging out simultaneously all type of stellar noise, are required.


Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 443
Author(s):  
Inmaculada Gutiérrez ◽  
Juan Antonio Guevara ◽  
Daniel Gómez ◽  
Javier Castro ◽  
Rosa Espínola

In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the risk of polarization. Particularly, we are interested in dealing with the problem of taking into account the polarization of nodes in the community detection problem. Adding this type of information to the community detection problem makes it more realistic, as a community is more likely to be defined if the corresponding elements are willing to maintain a peaceful dialogue. The polarization capacity is modeled by a fuzzy measure based on the JDJpol measure of polarization related to two poles. We also present an efficient algorithm for finding groups whose elements are no polarized. Hereafter, we work in a real case. It is a network obtained from Twitter, concerning the political position against the Spanish government taken by several influential users. We analyze how the partitions obtained change when some additional information related to how polarized that society is, is added to the problem.


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