Holismokes

Abstract

Modeling the mass distributions of strong gravitational lenses is often necessary in order to use them as astrophysical and cosmological probes. With the large number of lens systems (≳105) expected from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional Markov chain Monte Carlo techniques that are time consuming. We train a convolutional neural network (CNN) on images of galaxy-scale lens systems to predict the five parameters of the singular isothermal ellipsoid (SIE) mass model (lens center x and y, complex ellipticity ex and ey, and Einstein radius θE). To train the network we simulate images based on real observations from the Hyper Suprime-Cam Survey for the lens galaxies and from the Hubble Ultra Deep Field as lensed galaxies. We tested different network architectures and the effect of different data sets, such as using only double or quad systems defined based on the source center and using different input distributions of θE. We find that the CNN performs well, and with the network trained on both doubles and quads with a uniform distribution of θE > 0.5″ we obtain the following median values with 1σ scatter: Δx = (0.00−0.30+0.30)″, Δy = (0.00−0.29+0.30)″, ΔθE = (0.07−0.12+0.29)″, Δex = −0.01−0.09+0.08, and Δey = 0.00−0.09+0.08. The bias in θE is driven by systems with small θE. Therefore, when we further predict the multiple lensed image positions and time-delays based on the network output, we apply the network to the sample limited to θE > 0.8″. In this case the offset between the predicted and input lensed image positions is (0.00−0.29+0.29)″ and (0.00−0.31+0.32)″ for the x and y coordinates, respectively. For the fractional difference between the predicted and true time-delay, we obtain 0.04−0.05+0.27. Our CNN model is able to predict the SIE parameter values in fractions of a second on a single CPU, and with the output we can predict the image positions and time-delays in an automated way, such that we are able to process efficiently the huge amount of expected galaxy-scale lens detections in the near future.

HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program

“…Similar to procedures used in Cañameras et al (2020Cañameras et al ( , 2021) and Schuldt et al (2021b), we model the effective lensing potential as two components, a projected lens mass component characterised by a singular isothermal ellipsoid (SIE) profile and an external shear. The axis ratio and position angle of the SIE profiles are set to values inferred from the lens surface-brightness distribution in the HSC i band.…”

Holismokes

“…This approach accounts for the quality of HSC imaging and for the presence of artifacts and neighboring galaxies. We followed the procedure described in Schuldt et al (2021a) and C20, by modeling the lens mass distributions with Singular Isothermal Ellipsoids (SIE) using LRG redshifts and velocity dispersions from SDSS, and inferring axis ratios and position angles from the light profiles. Unlike in C20 we included external shear, and we chose lens-source pairs to produce a uniform Einstein radius distribution in the range 0.75 −2.5 , increasing the number of wide separations and of fainter (z > 0.7) lens galaxies to help recover these configurations.…”

Statistical strong lensing

“…Machine learning can also be used to perform fast automated lens parameter inferences (see e.g. Hezaveh et al 2017;Chianese et al 2020;Schuldt et al 2021;Park et al 2021): in particular, Wagner-Carena et al (2021) showed how it is possible to carry out hierarchical inferences on lens populations with Bayesian neural networks (Charnock et al 2020). However, it is not clear whether or not these methods are able to sample the posterior probability distribution of the lens parameters in a way that is sufficiently accurate for our purposes: dedicated tests are needed.…”