F l i P e r C l a s s Towards the classification of

all Kepler DR25 stars

Laboratoire AIM DRF//IRFU/DAP/LDE3, CEA Saclay, France

1

Lisa Bugnet

R.A. García, S. Mathur, G.R. Davies, O.J. Hall, M. Lund & B.M. Rendle

F l i P e r : A Powerful Tool to Detect and

Characterise Solar-like Pulsators.

KeplerScienceConf V, Glendale, USA

Mars 2019

[Debosscher+ 2011] [Mathur+ 2016, 2017]

C o n t e x t : W h a t i s h i d d e n i n s i d e t h e D R 2 5 c a t a l o g ?

2Lisa Bugnet, KeplerScienceConfV

7 Mars 2009

KIC: logg, [Fe/H], R, etc.

[Prsa+ 2011]

[Berger+, 2018]

“ ≈67% (≈120,000) of all Kepler targets are main-sequence stars, ≈21%

(≈37,000) are subgiants, and ≈12% (≈21,000) are red giants ”

[Hon+ accepted]

600 newly identified oscillating red giants

~1,800 Eclispsing binaries

May 2013

26 gDor

~150,000 light curves analysed, ~12,000 classified

RRLyrae/Cepheids

[Kolenberg+ 2010]

[Tkatchenko+ 2012]

[Balona 2018]

[Molnár+ 2018]

∂Scuti, gDor

750 candidate A-F type stars

[Uytterhoeven+ 2011]

[Kurtz+ 2015]

SPB, gDor

[García+ in prep]

30,000 RG

RRLyrae

[Yu+ 2018]

RG

C o n t e x t : W h a t i s h i d d e n i n s i d e t h e D R 2 5 c a t a l o g ?

3Lisa Bugnet, KeplerScienceConfV

Classification of all

~197,000 Kepler main

mission targets

Classification of all K2 targets

Classification of all TESS targets

[Bugnet+ in prep]

[Bugnet+ in prep]

T’DA (TESS Data for Asteroseismology) collaboration [Tkachenko et al., in prep]

F L I C K E R

The granule size/number depends on the evolutionary state, varying inversely with logg

Trampedach et al.,2013 Schwarzschild, 1975

Ludwig et al., 2006 Kallinger et al., 2014

The rms of brightness variations should scale at the square root of the number of granules

4

Bastien et al., 2013-2015

Lisa Bugnet, KeplerScienceConfV

F8: Measure of the rms of the light curve on time scales

shorter than 8 hours.

“At some point, around logg ∼ 2.7, the dominant granulation timescale begins to cross 8 hr into longer timescales excluded

from the F8 metric, such that the contribution of granulation to the F8 signal decreases. The timescales and amplitudes of the solar-like oscillations, on the other

hand, remain comparatively small but increase to levels detectable with flicker,

presumably becoming the dominant driver of flicker by log g ∼ 2.”

Fp = PSD� Pn

PSD

mean value of the power density between fmin and LC Nyquist frequencyfmin

5

Pn Photon noise [Jenkins+, 2010]

Lisa Bugnet, KeplerScienceConfV[Bugnet+ 2018]

F l i P e r ( F l i c k e r i n P o w e r )

F l i P e r ( F l i c k e r i n P o w e r )

Main-sequence stars (MS)

Red giants (RGB, RC)

High luminosity stars (AGB, RGB)

6Lisa Bugnet, KeplerScienceConfV

Flicker

[Bugnet+ 2018]

Fp = PSD� Pn

C h a r a c t e r i s t i c p a t t e r n s i n t h e P S D

7Lisa Bugnet, KeplerScienceConfV

Solar-like ∂Scuti

8Lisa Bugnet, KeplerScienceConfV

Solar-like Binaries∂Scuti

C h a r a c t e r i s t i c p a t t e r n s i n t h e P S D

9Lisa Bugnet, KeplerScienceConfV

BinariesSolar-like ∂Scuti RRLyrae

C h a r a c t e r i s t i c p a t t e r n s i n t h e P S D

10Lisa Bugnet, KeplerScienceConfV

F l i P e r

200.7 7 50

Fp = PSD� Pn

11Lisa Bugnet, KeplerScienceConfV

F l i P e rTESS simulated Data (T’DA)

[Bugnet+, Accepted]

Decision tree

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Set of parameters Xi: F07, F7, F20, F50, Teff, L

Supervised machine learning: need a known set of data

Yi: Class predicted

A decision tree algorithm construct a binary tree during the training, with each node representing and a split point on a single input variable (Xi). The leaf

nodes of the tree contain the output possible predictions (YPredicted)

Lisa Bugnet, KeplerScienceConfV

[Bugnet+ 2018]

F l i P e r C l a s s

Random Forest Algorithm

13Lisa Bugnet, KeplerScienceConfV

F l i P e r C l a s s

Random Forest Algorithm

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Mean prediction of the individual trees

[Breiman, 2001]

Lisa Bugnet, KeplerScienceConfV

F l i P e r C l a s s

15Lisa Bugnet, KeplerScienceConfV

Solar-like

Solar-likeClassical Pulsators

Classical Pulsators

Binaries

Binaries

Set of 1,942 stars

Tr a i n s e t : c o n t a i n s

8 0 % o f a l l c l a s s e s

Te s t s e t : c o n t a i n s t h e

r e m a i n i n g 2 0 %

F l i P e r C l a s s PRELIMINARYSolar-like

Classical Pulsators (=non Solar-like)

Binaries/ Transits/ pollution

16Lisa Bugnet, KeplerScienceConfV

Solar-like

Classical Pulsators (=non Solar-like)

Binaries/ Transits/ pollution

F l i P e r C l a s s PRELIMINARY

[Bugnet+, in prep]

17Lisa Bugnet, KeplerScienceConfV

Solar-like BinariesClassical pulsators

[Bugnet+, in prep]

PRELIMINARYF l i P e r C l a s s

18Lisa Bugnet, KeplerScienceConfV

logg for Solar-type stars using FliPer

Classification of TESS simulated data with FliPerClass: 98 %. [Bugnet+, accepted]

Classification of Kepler DR25 catalog with FliPerClass. [Bugnet+ in prep]

Classification of K2. [Bugnet+ in prep]

T’DA classifier for TESS data. [Tkachenko+ in prep]

Misclassified RG for legacy RG catalog see talk by Rafael García. [García+ in prep]

Improvements: Gaia colors, etc.

Call for inputs & collaborations!

GitHub repositories in development

F l i P e r C l a s s

Thank you for your attent ion !

github.com/lbugnet/FLIPER_CLASSgithub.com/lbugnet/FLIPER

PRELIMINARY