HOLISMOKES. IX. Neural network inference of stronglens parameters and uncertainties from groundbased images
Abstract
Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. With the large number of detections in current and upcoming surveys, such as the Rubin Legacy Survey of Space and Time (LSST), it is pertinent to investigate automated and fast analysis techniques beyond the traditional and timeconsuming Markov chain Monte Carlo sampling methods. Building upon our (simple) convolutional neural network (CNN), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a singular isothermal ellipsoid (SIE) profile (lens center x and y, ellipticity e_{x} and e_{y}, Einstein radius θ_{E}) and the external shear (γ_{ext, 1}, γ_{ext, 2}) from groundbased imaging data. In contrast to our previous CNN, this ResNet further predicts the 1σ uncertainty for each parameter. To train our network, we use our improved pipeline to simulate lens images using real images of galaxies from the Hyper SuprimeCam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find very good recoveries overall for the SIE parameters, especially for the lens center in comparison to our previous CNN, while significant differences remain in predicting the external shear. From our multiple tests, it appears that most likely the low groundbased image resolution is the limiting factor in predicting the external shear. Given the run time of milliseconds per system, our network is perfectly suited to quickly predict the next appearing image and time delays of lensed transients. Therefore, we use the networkpredicted mass model to estimate these quantities and compare to those values obtained from our simulations. Unfortunately, the achieved precision allows only a firstorder estimate of time delays on real lens systems and requires further refinement through followup modeling. Nonetheless, our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU, meaning that we are able to efficiently process the huge amount of galaxyscale lenses expected in the near future.
The network code is available under https://github.com/shsuyu/HOLISMOKESpublic/tree/main/HOLISMOKES_IX
 Publication:

Astronomy and Astrophysics
 Pub Date:
 March 2023
 DOI:
 10.1051/00046361/202244325
 arXiv:
 arXiv:2206.11279
 Bibcode:
 2023A&A...671A.147S
 Keywords:

 methods: data analysis;
 gravitational lensing: strong;
 Astrophysics  Instrumentation and Methods for Astrophysics;
 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 17 pages, including 11 figures, published with A&