Multi-threshold vs. Topological Clustering: A deblending comparison Valerio Roscani PhD student – INAF/OAR LSST2018 Project & Community Workshop August 15, 2018 #lsst2018 #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 1
Euclid & LSST - Euclid VIS ≤ 24.5 AB Magnitude (Rhodes et al. 2017) - LSST ≤ 27.5 AB Magnitude (Rhodes et al. 2017) - Euclid VIS PSF = 0.18’’ (Cropper et al. 2016) - LSST median seeing 0.7’’ (Rhodes et al. 2017) Euclid Survey Area 15000 !"# $ (Euclid site) LSST Main Survey Area 18000 !"# $ (LSST site) - - Blending has a non-trivial impact on the Euclid survey, even more on LSST survey (PSF 3-4 times wider) Estimated number of blended sources will be about 40% of all LSST objects (Dawson et al. 2016) Main Survey LSST overlapping Euclid area 7000 deg^2 (46%) - 11000 deg^2 (73%) (Rhodes et al. 2017) The ideal situation is to find an algorithm valid to both the surveys Deblending in the Euclid survey as a starting point for deblending in LSST overlapping areas can be considered #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 2
SExtractor SExtractor (Bertin and Arnouts 1996) Multi-threshold approach Two Main Deblending parameters: DEBLEND_MINCONT: Minimum contrast parameter for deblending (% flux for single branch) DEBLEND_NTHRESH: Number of deblending sub-threshold - Status: First version implemented in the Euclid MER pipeline, tested on MER-PHZ Data- challenges and ran in Scientific Challenge 3 (SC3) - (SExtractor) with parameters optimized after tests on Euclid-like images #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 3
ASTErIsM #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 4
ASTErIsM-DENCLUE #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 5
Datasets Examining the ambigous blends in HST catalog ∼ 75% of blendings are composed of just two objects (Dawson et al. 2016) Datasets: We create cubes of simulated images with related “true map” Our datasets are used to measure the deblending efficiency of different algorithms taking care of N t r N u e i m over/under deblending and fraction of correctly m a a g e p s s reconstructed objects Datasets are produced: The objects have different: magnitudes, - with bulges or disks objects using Skymaker sizes, distances, morphology, etc. (Sersic models) (E.Bertin 2009) - with real CANDELS-GS cutouts #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 6
Metrics #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 7
Preliminary Results ASTErIsM Parameters map of deblending performance 2-objects dataset single object dataset Reddish zones mean higher deblending performance #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 8
Preliminary Results SExtractor Parameters map of deblending performance 2-objects dataset single object dataset Reddish zones mean higher deblending performance #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 9
original True map asterism original True map asterism sextractor Asterism_overlap sextractor Asterism_overlap original True map asterism original True map asterism sextractor Asterism_overlap sextractor Asterism_overlap #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 10
Conclusion & Future developments Conclusions: ASTErIsM and Sextractor have similar performances with the right set of parameters For ASTErIsM a set of parameters which reduces over/under deblending has been found For Sextractor it’s seems to be not so trivial Future developments: - New datasets will be used for tests - Comparison with other Deblenders - Possible implementation of CNN (M.Huertas-Company, A.Boucaud) #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 11
Backup #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 12
Backup ASTErIsM SExtractor #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 13
Reconstruction/overlap indicators ● Sim_msk == True where (sim[image_ID] == sim_ID and sim[Image_ID] == overlap_id ) ● Det_msk == True where (Det[image_ID] == det_ID ● Roi_msk == True where np.logical_and(sim_msk, det_msk) : Reconstruction/overlap indicators ● Overlap_th : A threshold to remove sources detected out of the simulation area, and to asses that a det_ID overlaps sim_ID ● Recovery_sim_frac = roi_msk.sum() / sim_msk.sum() : ● Recovery_det_frac = roi_msk.sum() / det_msk.sum() ● Overlap_size = Roi_msk.sum() Candidates ● Candidates = all detected objects which have a number of overlapped pixels > overlap_th are the, and overlapping a simulated source #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 14
Association and contamination ● clean_candidates_list = List of candidates which are filtered by a second overlap_th ● Contaminated_list : list of all the candidates not associated to the sim_ID ● Clean_contaminated_list : list[from 0 to N_candidates or to N_candidates - 1] = list of all the candidates which have a contamination_frac > contamination_th Deblending quality indicators and thresholds ● Purity_sim_th = The minimum threshold defined on the recovery_sim_frac ● Purity_det_th = The minimum threshold defined on the recovery_det_frac ● Debl_ok = Number of assoc_id == N_sim and clean_contamination_list = [empty] ● Delta_debl = Number of assoc_ID + length(clean_contamination_list) - N_sim ● Debl = average(Debl_ok over all the N_imgs) ● Delta_debl_avg = average(Delta_debl over all the N_imgs) #lsst2018 LSST Project and Community Workshop 2018 • Tucson • August 13 - 17 15
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