Page 1 Completeness of Kepler Data and Implications for K2 Jessie Christiansen, Bruce D. Clarke, Christopher J. Burke, Fergal Mullally, Jeff L. Coughlin, and the Wombats NASA Exoplanet Science Institute/Caltech K2 Science Conference, Nov 3rd 2015

Page 2 Determining η Earth We need to calculate both: N measured : the number of real Earth-like planets in the Kepler sample (i.e. understanding the reliability, or false positive rate) N detectable : the number of stars around which the Kepler pipeline would have detected such planets (i.e. understanding the completeness) detectable, where detectable Geometric probability of ith planet to transit Probability of ith planet to having strength SNR being detected The aim of my research has been to characterise P i,SNR for the Kepler pipeline, which we can then use to calculate the pipeline detection efficiency.

Page 3 Historical estimates of P i,SNR detectable In a perfect world: Pipeline threshold = 7.1σ (Catanzarite & Shao 2011, Borucki et al 2011, Traub 2012, Dressing & Charbonneau 2013, Kopparapu 2013) In a conservative world: Howard et al (2011, >2Re, 0.5Re,<50d) Dong & Zhu (2012, < 250d) Fressin et al (2013), Mulders et al (2014), These teams report significant variation in derived occurrence rates, but examine different parameters spaces… hard to disentangle the impact of the decision re: detectability Burke, Christiansen et al. 2015

Page 4 KSCI : Pipeline Detection Metrics – Pipeline Detection Efficiency (Christiansen et al. 2015b, exoplanetarchive.ipac.caltech.edu) Four quarters (one year) of data  All seventeen quarters of data 15 CCDs (out of 84)  All 84 CCDs 10,000 targets  159,000 targets Into the pixels themselves (not the flux time series)  still true Version 9.1 of the Kepler pipeline  Version 9.2 of the Kepler pipeline The 2nd transit injection experiment 3rd Applicable to Mullally et al planet candidate catalogue (Q1-Q16) Applicable to Coughlin et al planet candidate catalogue (Q1-Q17) (Previous results available in Christiansen et al. 2013, 2015a)

Page 5 Injected versus recoveries Radius Re Period d FGK dwarfs 105,000 injections

Page 6 Sensitivity function

Page had a new metric The pipeline detection threshold (7.1 sigma) was chosen to achieve a certain false alarm probability, assuming that the whitened light curves would be dominated by Gaussian noise For a target with non-Gaussian noise, we would not achieve that false alarm probability The statistical bootstrap metric was introduced which calculates, for each light curve, what threshold would need to be reached to achieve a uniform false alarm probability across the whole set of stellar light curves A flaw in the implementation introduced noise into the calculated false alarm probabilities, which adversely affected long period detections This resulted in a period-dependent, non-uniform search Fixed in 9.3! (Jenkins et al. 2015)

Page 8 What does this mean for occurrence rate calculations? Details laid out in Christiansen et al. 2015b Executive Summary: -Continue using SOC 9.1 completeness products from the Q1-Q16 pipeline run (Burke et al. 2015, Christiansen et al. 2015) and planet candidate sample (Mullally et al. 2015) -Wait until the next (i.e. final) SOC 9.3 DR25 completeness and planet candidate sample become available -Play with the SOC 9.2 products only if you care about periods (recalculate the sensitivity using the provided table) – see Natalie Batalha’s talk from Mon am

Page 9 How to measure the detection efficiency of your own sample Details laid out in Christiansen et al. 2015b Full table of injected and recovered parameters available at NExScI, on the Kepler Completeness and Reliability Products page (among many other useful products!) You can define your own parameter space – period range, SNR threshold (e.g. >15), stellar sample (e.g. M dwarfs) Proceed with caution

Page 10 Completeness doesn’t end with the pipeline! Lists of Threshold Crossing Events are passed on for further analysis (Seader et al. 2015, Tenenbaum et al. 2014, Tenenbaum et al. 2013) Up to and including the Mullally et al catalogue, this vetting included human intervention How much coffee have YOU had today?? From Data Release 24 (e.g. Coughlin et al catalogue) onwards, there are two complementary autonomous vetting algorithms A machine-learning algorithm that learns from a training set which light curves look like planets and which look like false positives A(n) heuristic-based method which reproduces the decisions and cuts made by the human vetters (Thompson et al. accepted, Mullally et al. submitted) Both of these have been challenged by the transit injection run. Sensitivity of the vetting procedures

Page 11 Flux and centroid vetting: The fraction of simulated transiting planets that would make it through to the end of the robovetter marked as a PC KSCI (Coughlin et al. 2015b, NASA Exoplanet Archive) Challenging the robovetter False positive identification: The fraction of simulated false positives that would be marked as a FP by the robovetter Mullally et al. (submitted)

Page 12 Variation in sensitivity with stellar type Mulders et al. (twitter or arxiv.org/pdf/ ) suggest occurrence rates differ as a function of stellar type Gijs Mulders and Steve Bryson working on latest injection results…

Page 13 Variation in sensitivity with planet multiplicity (1/3) Compare sensitivity for targets with and without existing KOIs No significant difference (KS test) between blue curves

Page 14 Variation in sensitivity with planet multiplicity (2/3) What if the injected target was not detected first? Significant difference (KS test) between blue curves

Page 15 Variation in sensitivity with planet multiplicity (3/3) Seems to be – but small numbers (only 35 injections between 15-20sig!) Is this accounted for by the window function? Detectability of injected signals when first signal removes < 10% of cadences

Page 16 Running now on a NASA supercomputer near you… Four quarters (one year) of data  All seventeen quarters of data ✔ 15 CCDs (out of 84)  All 84 CCDs ✔ 10,000 targets  159,000 targets ✔ Into the pixels themselves (not the flux time series)  still true ✔ Version 9.1 of the Kepler pipeline  Version 9.2  Version 9.3 New injected planet parameter range for M-dwarfs The 2nd transit injection experiment 3rd 4th Period d Radius Re

Page 17 K2 represents an exciting opportunity to measure occurrence rates, particularly for M dwarfs (see many talks on coolness of M dwarf planets) Kepler only had a few thousand M dwarfs – K2 has a few thousand per campaign – although Dan Huber (Mon am) shows large variations from field to field. (This may be tricky for disentangling variations across spectral type!) Already seeing differences from expectations (Crossfield talk Mon am, Martinez poster) – fewer planet candidates being found than early predictions, even for the achieved CDPP Also potentially address questions about variation of occurrence rates across the Galaxy, different populations of stars Implications for K2 – 1/2

Page 18 General lessons learned (not pipeline specific) Measure your detection efficiency! Assumptions so far are not borne out Automate as much as possible – eyeballing is not well quantifiable Light curves with increased non-Gaussian noise (e.g. M dwarfs) are less amenable to whitening/harder to find planets in Short-period planets – a few multi-planet systems per K2 field. Do watch the window function since short-period planets have a higher duty cycle - preferable to divide out a transit model and preserve observations if possible Implications for K2 – 2/2