Spot activity of the RS Canum Venaticorum star σ Geminorum^⋆

¹ Department of Physics, PO Box 6400014 University of Helsinki, 00014 Helsinki, Finland
e-mail: perttu.kajatkari@helsinki.fi
² Finnish Centre for Astronomy with ESO (FINCA), University of Turku, Väisäläntie 20, 21500 Piikkiö, Finland
³ Center of Excellence in Information Systems, Tennessee State University, 3500 John A. Merritt Blvd., Box 9501, Nashville, TN 37209, USA

Received: 14 February 2013
Accepted: 6 November 2013

Abstract

Aims. We model the photometry of RS CVn star σ Geminorum to obtain new information on the changes of the surface starspot distribution, that is, activity cycles, differential rotation, and active longitudes.

Methods. We used the previously published continuous period search (CPS) method to analyse V-band differential photometry obtained between the years 1987 and 2010 with the T3 0.4 m Automated Telescope at the Fairborn Observatory. The CPS method divides data into short subsets and then models the light-curves with Fourier-models of variable orders and provides estimates of the mean magnitude, amplitude, period, and light-curve minima. These light-curve parameters are then analysed for signs of activity cycles, differential rotation and active longitudes.

Results. We confirm the presence of two previously found stable active longitudes, synchronised with the orbital period P_orb = 1960, and found eight events where the active longitudes are disrupted. The epochs of the primary light-curve minima rotate with a shorter period P_min,1 = 1947 than the orbital motion. If the variations in the photometric rotation period were to be caused by differential rotation, this would give a differential rotation coefficient of α ≥ 0.103.

Conclusions. The presence of two slightly different periods of active regions may indicate a superposition of two dynamo modes, one stationary in the orbital frame and the other one propagating in the azimuthal direction. Our estimate of the differential rotation is much higher than previous results. However, simulations show that this may be caused by insufficient sampling in our data.