CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding

By François Lanusse, Quanbin Ma, Nan Li, T. Collett, Chun-Liang Li, Siamak Ravanbakhsh, Rachel Mandelbaum, Barnab'as P'oczos. 2017

139 citations recorded by the source corpus.

Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution of massive galaxies, but can also provide valuable cosmological constraints, either by studying the population of strong lenses or by measuring time delays in lensed quasars. Due to the rarity of galaxy-scale strongly lensed systems, fast and reliable automated lens finding methods will be essential in the era of large surveys such as LSST, Euclid, and WFIRST. To tackle this challenge, we introduce CMU DeepLens, a new fully automated galaxy-galaxy lens finding method based on Deep Learning. This supervised machine learning approach does not require any tuning after the training step which only requires realistic image simulations of strongly lensed systems. We train and validate our model on a set of 20,000 LSST-like mock observations including a range of lensed systems of various sizes and signal-to-noise ratios (S/N). We find on our simulated data set that for a rejection rate of non-lenses of 99%, a completeness of 90% can be achieved for lenses with Einstein radii larger than 1.4" and S/N larger than 20 on individual $g$-band LSST exposures. Finally, we emphasize the importance of realistically complex simulations for training such machine learning methods by demonstrating that the performance of models of significantly different complexities cannot be distinguished on simpler simulations. We make our code publicly available at this https URL .

Subfields: Astronomy and Astrophysics, Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

1. Barnab'as P'oczos
2. Siamak Ravanbakhsh
3. Yin Li
4. Shirley Ho
5. Yu Feng
6. Siyu He

In Atomic and Molecular Physics, and Optics

1. Barnab'as P'oczos
2. Yu Feng

In Computer Vision and Pattern Recognition

1. Barnab'as P'oczos
2. Siamak Ravanbakhsh
3. Shirley Ho
4. Jeff Schneider

Citations in the corpus

• Learning to predict the cosmological structure formation (2018, background): “In particular, in astronomy and cosmology, a growing number of recent studies are using deep learning for a variety of tasks, ranging from analysis of cosmic microwave background (30–32), large-scale structure (33, 34), and gravitational lensing effects (35, 36) to classification of different light ”