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Giuseppe D’Ago Department of Phyiscs “ E.R. Caianiello ” - Salerno University Gravitational Physics and Astrophysics group. A morphological classification of light curves of equal-mass binary microlensing. In collaboration with : Cristine Liebig, Valerio Bozza and Martin Dominik.

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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

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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

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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

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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

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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

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

[bb ab1 at2] A2

Giuseppe D’Ago – University of Salerno

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

Giuseppe D’Ago – University of Salerno

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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

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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

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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

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

IIa

IIIc

Giuseppe D’Ago – University of Salerno

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

VIIIa

Giuseppe D’Ago – University of Salerno

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

Giuseppe D’Ago – University of Salerno

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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