OverSampling Mode

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OverSampling Mode

• C. Quentin, R. Cautain, C. Surace,
• R. Savalle, J-C. Meunier

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Scientific interest for oversampling

• To better reconstruct the shape of the transits
• Limb darkening (atmospheres)
• Asymmetry of the transit shape (rings, moons, )
• To help identify secondary transits (eclipsing
  binaries)
• To get a better estimate of the phase and the
  timing of the transits (systems of planets)
• By products
• Weaker instrumental noises (no piling up on
  board)
• Additional possibilities of data corrections (
  glitches , etc..)

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OSM General Overview
12000 Light Curves N1 data (8.5mn)
Initial list of targets
Priority Management Conf(i), Likelihood(i),
Scien(i), tend(i)
Detection, Estimation and Sorting Procedures
List of new candidates Conf(i)
Sorted List of targets
Scientific Oversamp. Staff
Discrimination (Planet/EB) Likelihood(i)
Oversampling List
CMC
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The main objectives

• - To identify transit events with a high
  confidence level or/and a confirmed periodicity.
• - To discriminate, in the case of periodic
  events, possible planetary transits from
  eclipsing binaries.
• - To sort transit candidates as a function of the
  confidence level and the likelihood index.
• - To check the scientific interest of the
  sorting, in the case of peculiar events
  (Scientific Oversampling Staff).
• - To draw up the list of the targets to be
  oversampled (CoRoTID).
• The frequency of all these operations is once a
  week.
• (short delay to process 12000 LCs)

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The various steps of the OSM

• Preprocessing
• To filter residuals of the SAA
• To remove disturbing low frequencies of Stellar
  Variability
• To filter (possible) orbital perturbations
• Detection of transit candidates
• Two complementary algorithms running in parallel
• Estimate of a confidence level for each detection
• Discrimination
• Use of simple procedures to identify most
  striking ambiguities
• Estimate of a likelihood index
• Sorting and list management

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1. Preprocessing

• Goals 
• (a) To reduce the level of instrumental noise
• (b) To remove the most disturbing frequencies (at
  low frequency)
• Possible Methods
• Based on individual target
• Based on collective analysis (multiplex approach
  ? PCA, SysRem, )
• Developed Procedures (individual targets)
• Moving Box
• Low Filter (fft)
• Filtering_saa (?)
• Gauging Filter

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2. Detection

• Goal 
• To identify transit like events in the
  Light-curves and to estimate a confidence level.
• Methods
• Based on the search of individual shape (transit
  like event)
• Based on the search of periodic features
• Developed algorithms 
• MID (Morph. Individual Detector) EPF (Event
  Periodicity Finder)
• BLS (Box fitting Least Square)
• Other algorithms

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Simulated Light-Curves

• Blindtest1
• 1000 Light-Curves in white color with 20
  planetary transits 16 other astronomical
  signals (sampling 512s).
• Used to test the complete chain and the list
  sorting.
• ABAC
• 850 Light-Curves for a G type star of magnitude
  14, with 3 level of stellar activity.
• Used to calibrate the detection algorithms.
• Blindtest2
• 236 colored Light-curves with periodic signals
  (either planetary transits or eclipsing
  binaries).
• Used to test the discrimination capacities.
• 
  (transits simulated by C. Moutou)

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3. Discrimination

• Goal 
• To identify in the list of candidates the most
  striking ambiguities and estimate a likelihood
  index.
• Possible Methods
• Identification of secondary transits at ½ period
  (EBs)
• Events are incompatible in the three colors
• To reconstruct the transit shape
• To use the knowledge of Exodat
• Developed procedure (the simplest one)
• BinTest identification of secondary transits
  ? EBs
• To fold the signal after a phase shift of
  a ½ period and compare with the folding without
  shift.

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4. Sorting and list management

• Goal 
• To draw up a list of targets that merit to be
  oversampled.
• The list must be sorted following
• a confidence level in the detections
• a likelihood index that the candidate be a planet
• a number of scientific priorities
• Procedure to be developed 
• Management of the lists issued from the previous
  steps.

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Sketch of the OSM procedures
1. Preprocessing
Corot_ID, Raw_lightcurve
Moving Box Norbit10
Low Filter Tcut1.5 jd
Gauging Filter Nech6, dff5E-3
Corot_ID, Filter_lightcurve
Corot_ID ListTransitEvent date, duration,
deltaF, surface, SNRE
2. Detection

• MID (maille)
• ? SNRE

BLS (Nharm,Pmin,Pmax,DT) ? SDE
BD Alarm Corot_ID, WinID
Corot_ID, ListObjetPeriodique
Objet_periodique, type, ListeObjetTransit, VR
EPF , WPDM ? VR
Corot_ID Objet_Periodique period, phase, deltaF,
duration, SDE
3. Discrimination
Bin-Test ? type
ListCorot_ID SDE gt (SDE)o
4. Sorting
ListCorot_ID SNRE gt (SRE)o
Merging (SDE)o, (SNRE)o, (VR)o
ListCorot_ID VR gt (VR)o
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Current status and prospect

• The proposed methods
• Are ready to be implemented in an operational
  chain
• MID PEF is well suited for weak transit numbers
  
• Standard BLS is well suited for large transit
  numbers
• Oversampling data base
• Constructed to store and manage the versions of
  the various software, lists of candidates and
  targets
• Possible Improvements during the preprocessing
  stage
• Filtering likely will change when using true
  data.
• Removal of instrumental noise will possibly
  benefit of using collective information as for
  example with PCA or SysRem.