Bayesian Analysis for the Ranking of Transits
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1 Bayesian Analysis for the Ranking of Transits Pascal Bordé, Marc Ollivier, Alain Léger
2 Layout I. What is BART and what are its objectives? II. Description of BART III.Current results IV.Conclusions and outlooks 2
3 I. What is BART and what are its objectives? I.1) Issues 3
4 I.1) Issues of the transits detected by the detection teams turn out to be false positives; 4
5 I.1) Issues of the transits detected by the detection teams turn out to be false positives; The follow-up observations requires lots of time, money and man-power. CoRoT-7b : 70h (100 nigths) on HARPS; 5
6 I.1) Issues of the transits detected by the detection teams turn out to be false positives; The follow-up observations requires lots of time, money and man-power. CoRoT-7b : 70h (100 nigths) on HARPS; Most targets with magr can t be followed (especially in RV). 6
7 I.1) Issues of the transits detected by the detection teams turn out to be false positives; The follow-up observations requires lots of time, money and man-power. CoRoT-7b : 70h (100 nigths) on HARPS; Most targets with magr can t be followed (especially in RV). Probabilistic validation (BLENDER, PASTIS) is a long (computer time) and complex task (CoRoT-22b). 7
8 I.2) Objectives of BART 8
9 I.2) Objectives of BART Link between detection and follow-up teams 9
10 I.2) Objectives of BART Link between detection and follow-up teams Point at the most promising transits for follow-up observations and probabilistic validation; 10
11 I.2) Objectives of BART Link between detection and follow-up teams Point at the most promising transits for follow-up observations and probabilistic validation; Make a report on what we can infer with the light curve alone on each transit. 11
12 I.3) What is BART Software developed in Python; 12
13 I.3) What is BART Software developed in Python; Automated and quick enough to be applied on all the transit of a run within a week; 13
14 I.3) What is BART Software developed in Python; Automated and quick enough to be applied on all the transit of a run within a week; Use bayesian model comparison to infer the probabilities for a transit to be caused by PS, EB, CPS, CEB; 14
15 I.3) What is BART Software developed in Python; Automated and quick enough to be applied on all the transit of a run within a week; Use bayesian model comparison to infer the probabilities for a transit to be caused by PS, EB, CPS, CEB; Produce a ranking of transit based on the probability of each transit to a be PS; 15
16 I.3) What is BART Software developed in Python; Automated and quick enough to be applied on all the transits of a run within a week; Use bayesian model comparison to infer the probabilities for a transit to be caused by PS, EB, CPS, CEB; Produce a ranking of transit based on the probability of each transit to a be PS; Use bayesian inference of parameters to obtain the probability density function of all the parameters (density, radius ratio,... ) of all the scenari (PS, EB,...) 16
17 II) Description of BART 17
18 II.1) Bayesian model comparison Posterior probability Likelihood Prior probability M : model (PS, EB, CEB ) d : data I : context of the measurement Marginalized likelihood (Normalization factor) 18
19 II.1) Bayesian model comparison Posterior probability Likelihood Prior probability M : model (PS, EB, CEB ) d : data I : context of the measurement Marginalized likelihood (Normalization factor) Eclipsing Binary single and double periode Models, M, Planetary System, Contaminating Eclipsing Binary single and double periode Contaminating Planetary System 19
20 II.1) Bayesian model comparison Posterior probability Likelihood Prior probability M : model (PS, EB, CEB ) d : data I : context of the measurement Marginalized likelihood (Normalization factor) Time Data, d To take advantage of the work done by the detection teams, I use as data measured on the Normalized flux transit Phase angle 20
21 II.2) Bayesian inference parameters Posterior probability density Likelihood Prior probability density : Parameters PS impact parameter radius ratio Marginalized likelihood (Normalization factor) CPS impact parameter radius ratio EB (P, 2P) impact parameter R prim R sec som radius ratio system density CEB (P, 2P) impact parameter R prim R sec radius ratio som system density stellar density stellar density orbital periode orbital periode orbital periode orbital periode effective effective contamination factor temperature ratio temperature ratio contamination factor 21
22 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 22
23 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 23
24 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 24
25 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 25
26 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 26
27 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 27
28 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 28
29 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 29
30 II.3) Exploration of parameter space and integration Fasten execution time : MCMC exploration Monte carlo integration Automatisation : Personal adaptation of P.C. Gregory (2005) Adaptative Metropolis-Hasting algorithm (acceptance rate and trace correlation feedback) more efficient with correlated parameters 30
31 III) Current results 31
32 III.1) Current results : Ranking of 22 candidates from LRc02 32
33 III.2) Current results : Parameters inference CoRoT-6b -- PS 33
34 III.3) Current results : Correlation diagrams CoRoT-6b -- PS 34
35 IV) Conclusions and outlook The ranking is already quite good (except for high impact parameter planets) ; I think that it could be even better with a more detailed description of contamination (contaminant list and colors); Development of more detailed prior probabilities on models and parameters 35
36 Thank you for attention! 36
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