A morphological classification of light curves of equal-mass binary microlensing

1. Giuseppe D’AgoDepartmentofPhyiscs “E.R.Caianiello” - Salerno UniversityGravitationalPhysics and Astrophysicsgroup A morphological classification of light curves of equal-mass binary microlensing In collaborationwith: Cristine Liebig, Valerio Bozza and Martin Dominik 18th International Conference on Microlensing - January 20/24, 2014 Santa Barbara

2. A morphological classification of light curves in equal-mass binary microlensing Motivation: Classificationofbinarymicrolensingeventsbasedsolely on the observablefeaturesof the light curves: - numberofpeaks - typeofpeaks -> nature of the peaks For a fixed mass ratio, threetopologies are possibledepending on the lensseparation(Schneider & Weiss, 1986; Erdl & Schneider, 1991): close, intermediate, wide. Giuseppe D’Ago – University of Salerno

3. A morphological classification of light curves in equal-mass binary microlensing Intermediate topology: q=1, 2-0.5<s<2 Closetopology: q=1, s<2-0.5 Wide topology: q=1, s>2 Giuseppe D’Ago – University of Salerno

4. A morphological classification of light curves in equal-mass binary microlensing “C-” and “-C” will indicate respectivelyacusp entry and a cuspexit “F-” and “-F” will indicate respectivelyafold entry and a foldexit Giuseppe D’Ago – University of Salerno

5. A morphological classification of light curves in equal-mass binary microlensing will indicate a fold grazing will indicate a cusp grazing Giuseppe D’Ago – University of Salerno

6. A morphological classification of light curves in equal-mass binary microlensing [bb ab1 at2] A2 Giuseppe D’Ago – University of Salerno

7. A morphological classification of light curves in equal-mass binary microlensing Giuseppe D’Ago – University of Salerno

8. A morphological classification of light curves in equal-mass binary microlensing 2nd step: letα0 and u0vary and count the peaks on the light curve in orderto individuate the iso-peakregions on a 2-d plot 1st step: writemagnificationmapswith inverse rayshooting q=1, 0.5<s<2.5 (IRS python code byMarnach, 2010) Giuseppe D’Ago – University of Salerno

9. A morphological classification of light curves in equal-mass binary microlensing Howmagnificationmaps and iso-peakregions change across the threetopologies Giuseppe D’Ago – University of Salerno

10. A morphological classification of light curves in equal-mass binary microlensing • Classifyeachiso-peakregionaccordingto the numberof the peaks • Collect in the samemorphologyclassregionswith the same nature of the peaks Exampleof the classificationofaniso-peakregion plot Giuseppe D’Ago – University of Salerno

11. A morphological classification of light curves in equal-mass binary microlensing IIa IIIc Giuseppe D’Ago – University of Salerno

12. A morphological classification of light curves in equal-mass binary microlensing VIIIa Giuseppe D’Ago – University of Salerno

13. A morphological classification of light curves in equal-mass binary microlensing Giuseppe D’Ago – University of Salerno

14. A morphological classification of light curves in equal-mass binary microlensing • Magnificationmapswith a scale of 3.310-4E/pixelfor q=1 and 0.5<s<2.5 • Source radiusof 210-2E and source trackparameters: u0>0, 0<0</2 • Iso-peakregions plot of 1600x1600 pixel • Lookedinto more than 500 light curves (differentpointsforeachiso-peakregion) • Classified 72 differentmorphologyclassesamong the threepossibletopologies; • Completeness: the sizeof the source usedwas 210-2E, so wecouldhavemissedpeakswithseparationsmallerthan 410-2E • Nextstep: to probe different mass ratiosapplying the sameclassificationschemepresentedhere Giuseppe D’Ago – University of Salerno

15. Thank you!