344 research outputs found
Sunspot waves and flare energy release
We address a possibility of the flare process initiation and further
maintenance of its energy release due to a transformation of sunspot
longitudinal waves into transverse magnetic loop oscillations with initiation
of reconnection. This leads to heating maintaining after the energy release
peak and formation of a flat stage on the X-ray profile. We applied the
time-distance plots and pixel wavelet filtration (PWF) methods to obtain
spatio-temporal distribution of wave power variations in SDO/AIA data. To find
magnetic waveguides, we used magnetic field extrapolation of SDO/HMI
magnetograms. The propagation velocity of wave fronts was measured from their
spatial locations at specific times. In correlation curves of the 17 GHz (NoRH)
radio emission we found a monotonous energy amplification of 3-min waves in the
sunspot umbra before the 2012 June 7 flare. This dynamics agrees with an
increase in the wave-train length in coronal loops (SDO/AIA, 171 {\AA})
reaching the maximum 30 minutes prior to the flare onset. A peculiarity of this
flare time profile in soft X-rays (RHESSI, 3-25 keV) is maintaining the
constant level of the flare emission for 10 minutes after the short impulse
phase, which indicates at the energy release continuation. Throughout this
time, we found 30-sec period transverse oscillations of the flare loop in the
radio-frequency range (NoRH, 17 GHz). This periodicity is apparently related to
the transformation of propagating longitudinal 3-min waves from the sunspot
into the loop transverse oscillations. The magnetic field extrapolation showed
the existence of the magnetic waveguide (loop) connecting the sunspot with the
energy release region. A flare loop heating can be caused by the interaction
(reconnections) of this transversally oscillating waveguide with the underlying
twisted loops.Comment: 7 pages, 9 figure
Contextual Bandit Learning with Predictable Rewards
Contextual bandit learning is a reinforcement learning problem where the
learner repeatedly receives a set of features (context), takes an action and
receives a reward based on the action and context. We consider this problem
under a realizability assumption: there exists a function in a (known) function
class, always capable of predicting the expected reward, given the action and
context. Under this assumption, we show three things. We present a new
algorithm---Regressor Elimination--- with a regret similar to the agnostic
setting (i.e. in the absence of realizability assumption). We prove a new lower
bound showing no algorithm can achieve superior performance in the worst case
even with the realizability assumption. However, we do show that for any set of
policies (mapping contexts to actions), there is a distribution over rewards
(given context) such that our new algorithm has constant regret unlike the
previous approaches
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