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L. Mancini

Publications and source records attributed to L. Mancini.

236 records · Page 14Linked to original sources

Microlensing by Compact Objects associated to Gas Clouds

We investigate gravitational microlensing of point-like lenses surrounded by diffuse gas clouds. Besides gravitational bending, one must also consider refraction and absorption phenomena. According to the cloud density, the light curves may suffer small to large deviations from Paczynski curves, up to complete eclipses. Moreover, the presence of the cloud endows this type of microlensing events with a high chromaticity and absorption lines recognizable by spectral analysis. It is possible that these objects populate the halo of our galaxy, giving a conspicuous contribution to the fraction of the baryonic dark matter. The required features for the extension and the mass of the cloud to provide appreciable signatures are also met by several astrophysical objects.

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Microlensing search towards M31

We present the first results of the analysis of data collected during the 1998-99 observational campaign at the 1.3 meter McGraw-Hill Telescope, towards the Andromeda galaxy (M31), aimed to the detection of gravitational microlensing effects as a probe of the presence of dark matter in our and in M31 halo. The analysis is performed using the pixel lensing technique, which consists in the study of flux variations of unresolved sources and has been proposed and implemented by the AGAPE collaboration. We carry out a shape analysis by demanding that the detected flux variations be achromatic and compatible with a Paczynski light curve. We apply the Durbin-Watson hypothesis test to the residuals. Furthermore, we consider the background of variables sources. Finally five candidate microlensing events emerge from our selection. Comparing with the predictions of a Monte Carlo simulation, assuming a standard spherical model for the M31 and Galactic haloes, and typical values for the MACHO mass, we find that our events are only marginally consistent with the distribution of observable parameters predicted by the simulation.

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