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Variability amplitudes larger than 1 magnitude over time-scales of a few tens of minutes have recently been reported in the optical light-curves of several blazars. In order to independently verify the real occurrence of such extremely violent events, we undertook an observational study of a selected sample of three blazars: PKS 0048-097, PKS 0754+100, and PKS 1510-089. Possible systematic error sources during data acquisition and reduction were carefully evaluated. We indeed found flux variability at intra-night time-scales in all three sources, although no extremely violent behaviour, as reported by other authors, was detected. We show that an incorrect choice of the stars used for differential photometry will, under fairly normal conditions, lead to spurious variability with large amplitudes on short time-scales. Wrong results of this kind can be avoided with the use of simple error-control techniques.
Photon exchange due to nuclear bremsstrahlung during nuclear collisions can cause Coulomb excitation in the projectile and the target nuclei. The corresponding process originated in nuclear timescales can also be observed in atomic phenomenon experim
We present the results of flux density, spectral index, and polarization intra-night monitoring studies of a sample of eight optically bright blazars, carried out by employing several small to moderate aperture (0.4,m to 1.5,m diameter) telescopes fi
It has recently been argued that the inability to measure the absolute phase of an electromagnetic field prohibits the representation of a lasers output as a quantum optical coherent state. This argument has generally been considered technically corr
The First G-APD Cherenkov Telescope (FACT) was built on the Canary Island of La Palma in October 2011 as a proof of principle for silicon based photosensors in Cherenkov Astronomy. The scientific goal of the project is to study the variability of act
In this paper we perform an analytic comparison of a number of techniques used to detect fake and deceptive online reviews. We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of th