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Ignacio Sevilla, 10/07/2014 09:14 PM

SG separation challenge

Purpose and status as of July 20th 2014:

Rounds 1 and 2 have served to verify that we could do better than standard DESDM classifiers, at least in the fields we have trained on. We are moving beyond these fields, in particular to SPTE, and applied some tests to understand the quality of the classification without truth values. We have found some puzzling behavior, specially for the stars. Before providing these catalogs to the collaboration, we have to understand these features. So round 3 will center on having calibration fields closer to DES survey characteristics, larger spectroscopic samples, including more stars, as well having an eye on the particular observing conditions of the training fields and check whether those regions in SPTE with similar conditions have an expected behavior. The goal is to provide a classifier(s) in the short term that is well backed up by plots/results from this challenge, showing their behavior.

Details and results

Now that several people are testing their own approaches:

  • Cut-based with DESDM info (Eli, Diego, Nacho, Ryan, William...).
  • Multi-class (Maayane)
  • Random Forests (Ryan)
  • Boosted Decision Trees (Nacho, Alex)
  • Alternative Neural Network with probabilistic output (Chris Bonnett).
  • Probability based on spread model and photometry (DES-Brazil)
  • Others...

I think the time is right and the codes are mature to launch a specific SG separation challenge, mimicking the successful photo-z WG exercise.

We have to establish:

  • The training/validation/testing sample (COSMOS, others).
    I have prepared a 70/30 training/testing with the deep COSMOS field matched to ACS imaging. About 280 parameters, up to each tester to choose which.
    Besides new datasets, also consider shallower COSMOS. Also consider fixed set of parameters as Eduardo suggests. Also need to add SLR corrections though I think not very important now.
  • Only stars and galaxies? What about QSOs, image artifacts?
    Star/galaxy for round 1.
  • The metrics (Fixed cut, Fixed purity, Fixed Efficiency, ROC -- see example below).
    I would prefer to use ROC, i.e., True Positive Rate vs False Positive Rate curve formed changing the threshold (thanks Alex for pointing out mistake in previous ROC!).
  • SVA1 systematics: correlations with depth, Galactic latitude, seeing, etc.
  • Who/how to run it.
    I suggest each group providing an output file with id (or ra,dec on first round) plus galaxy probability or binary value.
  • Is there any gain combining them (a committee)?
  • The schedule.

Comparison metrics

There are a number of metrics that can be used for comparing the performance of classifiers. Some especially useful metrics are those defined in the DES star/galaxy separation (on simulation) paper arXiv:1306.5236 and the receiver operating characteristic generally used for classifier comparison.

Completeness and Purity provided by a given classifier

We define the parameters used to quantify the quality of a star/galaxy classifier. For a given class of objects, X (stars or galaxies), we distinguish the surface density of properly classified objects, N_X , and the misclassified objects, M_X .

  • The galaxy completeness c^g is defined as the ratio of the number of true galaxies classified as galaxies to the total number of true galaxies.
  • The stellar contamination f_s is defined as the ratio of stars classified as galaxies to the total amount of objects classified as galaxies.
  • The purity p^g is defined as 1-f_s

Bellow are three different plots we suggest to use to assess the performances of each classifier.


Example, on simulations, from arXiv:1306.5236

purity as a function of magnitude (for fixed completeness, the threshold/cut is let free)

completeness as a function of magnitude (for fixed purity, the threshold/cut is let free )

Receiver operating characteristics

The receiver operating characteristic (ROC) provides another tool for evaluating the performance of classifiers. The ROC provides some information orthogonal to that in the completeness vs purity plots:

  • Because ROCs compare the true positive rate to the false positive rate, they do not depend on relative composition of the test sample. Thus, unlike the purity, they contain information only about the intrinsic performance of the classifier and not the test sample.
  • ROCs allow classifiers to be compared without requiring a threshold/cut to be placed on the output. This is useful because different projects possess different requirements on object sample, completeness, purity, etc. The area under the ROC can serve as a very high-level scalar metric for classifier performance.
  • Once a threshold/cut is placed, we can generate magnitude dependent true positive vs false positive rate plots which would be intrinsic to the classifiers.