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Mireille Louys1, Benjamin Perret 1

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1 LSB Galaxies Detection Using Markovian Segmentation on Astronomical Images
Mireille Louys1, Benjamin Perret 1 Bernd Vollmer 2, François Bonnarel 2 Sebastien Lefèvre1, Christophe Collet1 Laboratoire des Sciences de l’Informatique , de l’Image et de la Télédétection 2 Centre de donnees astronomiques de Strasbourg,

2 Rationale Surface brightness is highly variable from one galaxy to another (Disney, 1976) Low surface brightness galaxies are close to the image background level and satisfy: SB > 22.5 mag.arcsec-2 Found inside and outside galaxy clusters How complete are our detections? More understanding in the baryonic fraction of the Dark Matter in the universe

3 Noise and background Easy case More difficult

4 Visual Inspection and Validation
Overview Original Image Segmentation Map Objects list Segmentation Objects Profiles Fitting Selection Visual Inspection and Validation DetectLSB VO Objects list Ellipse parameters Fit error Magnitude profile LinLog, LinLin Measured Surface brightness Estimated Surface brightness Scale length Markovian Quadtree 3D mathematical morphology VOTable XML

5 Markovian segmentation
The goal is to classify pixel values and discriminate bright objects, sky background and faint sources to be considered later as LSB candidates . It works as an inverse method : Try to find the Bayesian estimate of the most likely class partition that maps the observation We make 2 assumptions : on the noise and inner class statistics (here gaussian), on the statistical phenomenon: Markovian quadtree.

6 Markovian quadtree The succesive tree levels correspond to increasing scales of the image. We want to compute the a posteriori probability of having site s in one class c, knowing the observation Ys and the class number of the parent node within the Markov tree. The Quadtree provides an in-scale regularisation framework, providing exact and non iterative solution It is fully applicable to multiband observations

7 Markovian Segmentation
Original Segmentation Map: here 3 classes The adequate number of classes is set by a trial and error process

8 Steps achieved with DetectLSB
For each detection map: Identify pixel connected components Remove very extended components : > 300 pixels Remove components at the edges of the image Ellipse fitting Luminosity profile analysis Selection criteria: Sort out stars according to their profile (steep slope, etc.) Remove bad centered and/or overlaping objects (crowded regions) Check for central surface brightness

9 Object processing DetectLSB allows to extract potential faint objects and compute various criteria for LSB validation Examine each detected candidate Fitting an ellipse surface on the object

10 Luminosity profile analysis
For each object in the detection map: Average profile along each ellipse radius f(r)=U0*exp(-r/R0) Green lines correspond to RMS background levels: lowest at 0.5 * RMS_background , highest at 1 * RMS_ background At least 3 points aligned above background level

11 Source Measurements Fitted SB Surface Brightness Radius
Profile Fit Error

12 Data set B-images of a region of the Virgo cluster obtained with the Isaac Newton Telescope WFCS (Wild Field Camera Survey) Analysed by Sabina Sabatini et coll. in SABATINI S.; DAVIES J.; SCARAMELLA R.; SMITH R.; BAES M.; LINDER S.M.; ROBERTS S.; TESTA V. Mon. Not. R. Astron. Soc., 341, (2003) . and provided to us as a test set Full data set = 80 images of 4096x2048 pixels Analysed: 18 images with X-match with the detection lists provided by the authors

13 Results on the INT data set
Found 79 % of our LSB candidates are confirmed by Sabatini et al. 23 (Markov+detectLSB) compared to 29 (Sabatini) Found new LSB candidates The validation on the full data set is still an on-going collaboration effort with S. Sabatini and W. van Driel.

14 Conclusion The Markovian segmentation approach allows to study LSB candidates even in a noisy environment Multiband image analysis is beeing currently applied to the same data set in B band completed with I band First tests are promising Such a procedure has been implemented in the AIDA image processing workflow project at CDS (Cf Schaaff et al, this conference)

15 Segmentation Alternative: 3D- mathematical morphology
Re-use a priori knowledge of LSB profiles Extend pattern matching techniques : Allow shape variances due to noise Define a tolerance volume within which the LSB disturbed profile can be located

16 3D Morphological Segmentation
One compute the image surface included in this volume with respect to the surface measured at the volume basis. Original Signal Compute pairing score Two 3D surfaces to determine the tolerance envelop

17 Markovian Segmentation
Original Segmentation Map: here 3 classes Build up the segmentation map for different nb of classes ~ 3 to 7 The adequate number of classes is set by a trial and error process Apply different thresholds values between classes to provide a set of detection maps. 17

18 Segmentation morphologique – Pratique 2/3
Le score retenu en chaque point est celui du motif qui a fourni le meilleur appariement; L’effet bloc est dû à l’estimation locale du niveau de fond qui est réalisée sur un sous maillage de l’image.

19 Segmentation morphologique – Pratique 3/3
La carte des scores est ensuite seuillée; L’ensemble du processus étant basé sur des critères physiques et adapté dynamiquement aux caractéristiques de l’image, un seuil générique a pu être déterminé; La base du motif qui a permis l’obtention du score est redessinée; Des classes différentes sont attribuées en fonction de l’intensité du pixel central.

20 2 detections for the same object

21 performances Markov segmentation Computation : 512x512 images for 3m

22 Targets: Low Surface Brightness Galaxies
Galaxie dont la brillance est supérieur à 22.5 mag/arcsec² La magnitude est une échelle logarithmique inverse, une augmentation de 5° de magnitude correspond à une brillance 100 fois moindre. Galaxie traditionnelle (HSB) Galaxie LSB

23 Introduction: Définition d’une galaxie LSB 2/4
Objet très difficile à détecter car leur luminosité les place dans la zone d’incertitude liée au bruit; Les méthodes de segmentation par seuillages successifs (méthode des 3σ) échouent car elles éliminent la LSB en même temps que le fond.

24 candidates along ellipses profiles.
Computing luminosity profiles of source candidates along ellipses profiles. Measuring different parameters: Ellipse Fit error Magnitude profile LinLog, LinLin Measured Surface brightness Estimated Surface brightness Scale length

25 Segmentation morphologique – Pratique 1/3
Les motifs doivent pouvoir prendre toutes les tailles, orientations et élongations possibles; Ils sont tracés à une hauteur normalisée, celle-ci étant ajustée selon la valeur du pixel courant, et le niveau du fond local estimé.


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