Flares and variability from Sagittarius A(star): five nights of simultaneous multi-wavelength observations

Aims. We report on simultaneous observations and modeling of mid-infrared (MIR), near-infrared (NIR), and submillimeter (sub-mm) emission of the source Sgr A * associated with the supermassive black hole at the center of our Galaxy. Our goal was to monitor the activity of Sgr A* at different wavelengths in order to constrain the emitting processes and gain insight into the nature of the close environment of Sgr A*. Methods. We used the MIR instrument VISIR in the BURST imaging mode, the adaptive optics assisted NIR camera NACO, and the sub-mm antenna APEX to monitor Sgr A* over several nights in July 2007. Results. The observations reveal remarkable variability in the NIR and sub-mm during the five nights of observation. No source was detected in the MIR, but we derived the lowest upper limit for a flare at 8.59 mu m (22.4 mJy with A(8.59 mu m) = 1.6 +/- 0.5). This observational constraint makes us discard the observed NIR emission as coming from a thermal component emitting at sub-mm frequencies. Moreover, comparison of the sub-mm and NIR variability shows that the highest NIR fluxes (flares) are coincident with the lowest sub-mm levels of our five-night campaign involving three flares. We explain this behavior by a loss of electrons to the system and/or by a decrease in the magnetic field, as might conceivably occur in scenarios involving fast outflows and/or magnetic reconnection.

Competing regression models for longitudinal data

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretestposttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.