ANÁLISIS TEXTO ORAL JULIÁN ALBERTO GIRALDO

We presented an overview of the Wendelstein Calar Alto Pixellensing Project (WeCAPP). Observing simultaneously at two sites (Wendelstein and Calar Alto) we obtained a time coverage of 53% and 61% during the observed periods and up to 70% and 89% during the best month. Our best season was 2001/2002 with a 69% time coverage over the observed period, reaching around 90% during the 3 months (July 2001, October 2001, January 2002). This comes from a lucky coincidence that weather is correlated such, that observing conditions are hardly ever bad at both observatories at the same time. We demonstrated that despite observing at different sites with different instruments all data can be used for optimal image subtraction followingAlard & Lupton(1998). This method can be applied for very crowded fields like M31 and gives residual errors at the photon noise level. A red clump giant ofMI=0 mag, which is amplified by a factor of 10 by a microlensing event, can be detected with

our data. We showed how the data are reduced and how light curves are extracted. For illustration we presented a small sample of light curves. In future publications we will present a full catalogue of variable sources which we found in our M31 field, including potential MACHO light curves.

4.6. SUMMARY 91

Figure 4.6: Light curve of aδ-Cephei variable star, upper panel: R0band, lower panel: I0band.

Figure 4.7: Light curve of theδ-Cephei star of Fig.4.6in the R0 band, convolved with its period of

P=15.76±0.01 d. Plotted without (left panel) and with (centre panel) 1σerror bars, which represent fully propagated errors through all reduction steps. Right panel: Binned R0light curve of this star.

Figure 4.8: Light curve of a nova, representing the brightest variable source detected in our M 31- field. This nova was previously published by Modjaz & Li(1999). Upper panel: R0-Band, lower panel: I0-Band.

Figure 4.9: Light curve of an eruptive variable, which could be mistaken as a microlensing event, if the time coverage were insufficient. Upper panel: R0band, lower panel: I0band.

4.6. SUMMARY 93

Figure 4.10: Light curve of a longperiodic variable. Upper panel: R0band, lower panel: I0band. Note, that insufficient time coverage could result in a false identification of this variable as a microlensing event.

Figure 4.12: Light curve of a longperiodic variable star. Upper panel: R0 band, lower panel: I0band.

Figure 4.13: Light curve of a longperiodic variable star with a very large variation in the I0 band. Upper panel: R0 band, lower panel: I0band.

4.6. SUMMARY 95

Figure 4.14: Light curve of a longperiodic variable. Upper panel: R0band, lower panel: I0band.

Figure 4.15: Light curve of a RV Tauri star in the R0(upper panel) and I0(lower panel) bands. Due to the optimal time coverage, the typical double-wave shape with alternating deep and shallow maxima of the light curves of this class of variable stars is uncovered.

Chapter 5

Optimal image analysis

5.1

Abstract

The largest technical challenge of the WeCAPP project is the photometry of variable sources in the highly crowded center of M31. The so called “difference imaging analysis” (DIA) allows to identify variable sources and to measure their excess flux relative to a reference image; at the same time this technique is (up to their photon noise) “insensitive” to the presence of non-variable sources. The application of the DIA requires an optimal reduction of images since they have to be ’equal’ before subtraction except for their variable sources. To achieve that optimal reduction a lot of new data reduction tools have been developed, which are partly described already inG¨ossl & Riffeser(2002) and partly presented in this chapter for the first time.

We present a reduction pipeline for CCD (charge-coupled device) images which was built to search for variable sources in highly crowded fields like the M 31 bulge and to handle extensive large time series databases. We describe all steps of the standard reduction in detail with emphasis on the realiza- tion of per pixel error propagation: Bias correction, treatment of bad pixels, flatfielding, and filtering of cosmic rays. The problems of conservation of the PSF (point spread function) and error propa- gation in our image alignment procedure as well as the detection algorithm for variable sources are discussed: We build difference images via image convolution with a technique called OIS (optimal image subtraction, Alard & Lupton,1998), proceed with an automatic detection of variable sources in noise dominated images and finally apply a PSF-fitting, relative photometry to the sources found. The complete per pixel error propagation allows us to give accurate errors for each measurement.

5.2

Introduction

Astronomical imaging in optical wavebands is performed nearly exclusively with charge-coupled devices1 today. Despite the numerous advantages of modern CCDs, their images still have to be corrected for a couple of disturbing influences and effects before one can base advance in science on them. Here, we will focus on the problems arising with optical, ground based imaging and time-series observations to find and measure variable sources either hidden in a bright background (e.g. a variable star in its host galaxy) or a crowded field or even in a combination of both.

1 _{The history of CCDs in astronomy and a basic description of them can be found inMcLean(1997);Buil(1991);Jacoby}

The search for variable objects with common photometry methods becomes very ineffective in crowded fields because of blending.Phillips & Davis(1995) show algorithms for registering, match- ing the point spread functions (PSFs), and matching the intensity scales of two or more images in order to detect transient events.Tomaney & Crotts(1996) propose a method calledDifference Image Analysis (DIA) where the point spread function (PSF), describing the projection of a point source onto the image plane, is matched by calculating a convolution kernel in Fourier space. This method has been applied to Galactic microlensing (Alcocket al.,1999) as well as for microlensing in M 31

(Crottset al.,1999a). A new method forOptimal Image Subtraction(OIS) of two images has been de-

signed byAlard & Lupton(1998). They derive an optimal kernel solution from a simple least-squares analysis using all pixels of both images. This method has been used successfully in different projects (OGLE,Wozniak,2000; MOA,Bondet al.,2001; DIRECT,Mochejskaet al.,2001; etc.).

Figure 5.1: Example of a raw image. This is a 300×300 pixel region within a raw CCD image showing a part of the M 31 bulge taken at the Calar Alto 1.23 m telescope, Feb. 3rd, 2001. (WeCAPP project, Riffeser et al. 2001.)