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Abstract.We have carried out a survey of the Andromeda galaxy for unresolved microlensing (pixel lensing). We present a subset of four short timescale, high signal-to-noise microlensing candidates found by imposing severe selection criteria: the source flux variation exceeds the flux of an R = 21 magnitude star and the full width at half maximum timescale is less than 25 days. Remarkably, in three out of four cases, we have been able to measure or strongly constrain the Einstein crossing time of the event. One event, which lies projected on the M 31 bulge, is almost certainly due to a stellar lens in the bulge of M 31. The other three candidates can be explained either by stars in M 31 and M 32 or by MACHOs.

We report the discovery of a short-duration microlensing candidate in the northern field of the POINT-AGAPE pixel lensing survey toward M31. Almost certainly, the source star has been identified on

POINT-AGAPE is an Anglo-French collaboration which is employing the Isaac Newton Telescope (INT) to conduct a pixel-lensing survey towards M31. Pixel lensing is a technique which permits the detection of microlensing against unresolved stellar fields. The survey aims to constrain the stellar population in M31 and the distribution and nature of massive compact halo objects (MACHOs) in both M31 and the Galaxy.In this paper we investigate what we can learn from pixel-lensing observables about the MACHO mass and fractional contribution in M31 and the Galaxy for the case of spherically-symmetric near-isothermal haloes. We employ detailed pixel-lensing simulations which include many of the factors which affect the observables, such as non-uniform sampling and signal-to-noise ratio degradation due to changing observing conditions. For a maximum MACHO halo we predict an event rate in V of up to 100 per season for M31 and 40 per season for the Galaxy. However, the Einstein radius crossing time is generally not measurable and the observed full-width halfmaximum duration provides only a weak tracer of lens mass. Nonetheless, we find that the near-far asymmetry in the spatial distribution of M31 MACHOs provides significant information on their mass and density contribution. We present a likelihood estimator for measuring the fractional contribution and mass of both M31 and Galaxy MACHOs which permits an unbiased determination to be made of MACHO parameters, even from data-sets strongly contaminated by variable stars. If M31 does not have a significant population of MACHOs in the mass range 10 −3 M ⊙ − 1 M ⊙ strong limits will result from the first season of INT observations. Simulations based on currently favoured density and mass values indicate that, after three seasons, the M31 MACHO parameters should be constrained to within a factor four uncertainty in halo fraction and an order of magnitude uncertainty in mass (90% confidence). Interesting constraints on Galaxy MACHOs may also be possible. For a campaign lasting ten years, comparable to the lifetime of current LMC surveys, reliable estimates of MACHO parameters in both galaxies should be possible.

Automatic classification of variability is now possible with tools such as neural networks. Here, we present two neural networks for the identification of microlensing events: the first discriminates against variable stars and the second against supernovae. The inputs to the networks include parameters describing the shape and the size of the light curve, together with the colour of the event. The network computes the posterior probability of microlensing, together with an estimate of the likely error. An algorithm is devised for direct calculation of the microlensing rate from the output of the neural networks. We present a new analysis of the microlensing candidates towards the Large Magellanic Cloud (LMC). The neural networks confirm the microlensing nature of only seven of the possible 17 events identified by the MACHO experiment. This suggests that earlier estimates of the microlensing optical depth towards the LMC may have been overestimated. A smaller number of events is consistent with the assumption that all the microlensing events are caused by the known stellar populations in the outer Galaxy/LMC.

Abstract. We present the AGAPE astrometric and photometric catalogue of 1579 variable stars in a 14 × 10 field centred on M 31. This work is the first survey devoted to variable stars in the bulge of M 31. The R magnitudes of the objects and the B − R colours suggest that our sample is dominated by red long-period variable stars (LPV), with a possible overlap with Cepheid-like type II stars. Fits of the light curves with sinusoids suggest that a large fraction of the stars correspond to periodic or semi-periodic objects with periods longer than 100 days. Twelve nova candidates are identified. Correlations with other catalogues suggest that 2 novae could be recurrent novae and provide possible optical counterparts for 2 supersoft X-ray sources candidates observed with Chandra.

This paper exploits neural networks to provide a fast and automatic way to classify light curves in massive photometric data sets. As an example, we provide a working neural network that can distinguish microlensing light curves from other forms of variability, such as eruptive, pulsating, cataclysmic and eclipsing variable stars. The network has five input neurons, a hidden layer of five neurons and one output neuron. The five input variables for the network are extracted by spectral analysis from the light‐curve data points and are optimized for the identification of a single, symmetric, microlensing bump. The output of the network is the posterior probability of microlensing. The committee of neural networks successfully passes tests on noisy data taken by the MACHO collaboration. When used to process ∼5000 light curves on a typical tile towards the bulge, the network cleanly identifies the single microlensing event. When fed with a subsample of 36 light curves identified by the MACHO collaboration as microlensing, the network corroborates this verdict in the case of 27 events, but classifies the remaining nine events as other forms of variability. For some of these discrepant events, it looks as though there are secondary bumps or the bump is noisy or not properly contained. Neural networks naturally allow for the possibility of novelty detection; that is, new or unexpected phenomena which we may want to follow‐up. The advantages of neural networks for microlensing rate calculations, as well as the future developments of massive variability surveys, are both briefly discussed.

We report the discovery of a microlensing candidate projected 2Ј54Љ from the center of M32, on the side closest to M31. The blue color (RϪ ) of the source argues strongly that it lies in the disk of I p 0.00 ‫ע‬ 0.14 M31, while the proximity of the line of sight to M32 implies that this galaxy is the most likely host of the lens. If this interpretation is correct, it would confirm previous arguments that M32 lies in front of M31. If more events are discovered in this direction in a dedicated experiment, they could be used to measure the mass function of M32 up to an unknown scale factor. By combining microlensing observations of a binary-lens event with a measurement of the M31-M32 relative proper motion using the astrometric satellites Space Interferometry Mission or Global Astrometric Interferometer for Astrophysics, it will be possible to measure the physical separation of M31 and M32, the last of the six phase-space coordinates needed to assign M32 an orbit.