{"action":"created","issue":{"url":"https://api.github.com/repos/fred3m/scarlet/issues/27","repository_url":"https://api.github.com/repos/fred3m/scarlet","labels_url":"https://api.github.com/repos/fred3m/scarlet/issues/27/labels{/name}","comments_url":"https://api.github.com/repos/fred3m/scarlet/issues/27/comments","events_url":"https://api.github.com/repos/fred3m/scarlet/issues/27/events","html_url":"https://github.com/fred3m/scarlet/issues/27","id":291602737,"number":27,"title":"Deblender \"overfitting\"?","user":{"login":"lorena21395","id":31394370,"avatar_url":"https://avatars3.githubusercontent.com/u/31394370?v=4","gravatar_id":"","url":"https://api.github.com/users/lorena21395","html_url":"https://github.com/lorena21395","followers_url":"https://api.github.com/users/lorena21395/followers","following_url":"https://api.github.com/users/lorena21395/following{/other_user}","gists_url":"https://api.github.com/users/lorena21395/gists{/gist_id}","starred_url":"https://api.github.com/users/lorena21395/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lorena21395/subscriptions","organizations_url":"https://api.github.com/users/lorena21395/orgs","repos_url":"https://api.github.com/users/lorena21395/repos","events_url":"https://api.github.com/users/lorena21395/events{/privacy}","received_events_url":"https://api.github.com/users/lorena21395/received_events","type":"User","site_admin":false},"labels":[{"id":600218154,"url":"https://api.github.com/repos/fred3m/scarlet/labels/wontfix","name":"wontfix","color":"ffffff","default":true}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2018-01-25T15:10:12Z","updated_at":"2018-02-05T13:50:17Z","closed_at":null,"author_association":"NONE","body":"We think that deblender may be overfitting images by including some of the noise belonging to the original image, in the model. Here are two images where we see this effect (with background rms = 10). The residual from after the model has been subtracted from the original image looks a bit smoothed out.\r\n\r\n![](https://raw.githubusercontent.com/lorena21395/blender/master/run007_7.png)\r\n![](https://raw.githubusercontent.com/lorena21395/blender/master/run007_11.png)\r\n\r\nWe use metacalibration on the 'Original - Model of Neighbor' image to measure shear and we also calculate any bias in the process. We measured a bias of -0.0215581 +/- 0.00180595 from a set of 1e6 images with background rms = 10. In a control test with 1e6 images and a much lower background rms of 0.001, we measured a smaller bias of -0.00534828 +/- 9.46265e-05. Finally, we also ran another control in which we didn't use deblender and ran metacalibration on images of only a single object in the center. We used 1e6 images and a background rms = 10 for this test and measured a smaller bias of 0.000828241 +/- 0.00177186.\r\n\r\nDo you have any suggestions as to how we can prevent this 'overfitting'?"},"comment":{"url":"https://api.github.com/repos/fred3m/scarlet/issues/comments/363089832","html_url":"https://github.com/fred3m/scarlet/issues/27#issuecomment-363089832","issue_url":"https://api.github.com/repos/fred3m/scarlet/issues/27","id":363089832,"user":{"login":"lorena21395","id":31394370,"avatar_url":"https://avatars3.githubusercontent.com/u/31394370?v=4","gravatar_id":"","url":"https://api.github.com/users/lorena21395","html_url":"https://github.com/lorena21395","followers_url":"https://api.github.com/users/lorena21395/followers","following_url":"https://api.github.com/users/lorena21395/following{/other_user}","gists_url":"https://api.github.com/users/lorena21395/gists{/gist_id}","starred_url":"https://api.github.com/users/lorena21395/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lorena21395/subscriptions","organizations_url":"https://api.github.com/users/lorena21395/orgs","repos_url":"https://api.github.com/users/lorena21395/repos","events_url":"https://api.github.com/users/lorena21395/events{/privacy}","received_events_url":"https://api.github.com/users/lorena21395/received_events","type":"User","site_admin":false},"created_at":"2018-02-05T13:50:17Z","updated_at":"2018-02-05T13:50:17Z","author_association":"NONE","body":"We did a new run using the same set up as before except this time we included the constraint, `constraints['l0'] = bg_rms`. Our bias measurement was initially -0.0215581±0.00180595, but now it is -0.0404289 +/- 0.00179187. We would like to try using the 'l1' sparsity penalty next. Could you explain how to implement this? Thank you."}}
{"actor":{"display_login":"lorena21395"}}