Constraints on dark and visible mass in galaxies from strong gravitational lensing

We give a non-exhaustive review of the use of strong gravitational lensing in placing constraints on the quantity of dark and visible mass in galaxies. We discuss development of the methodology and summarise some recent results.Comment: To appear in proceedings of IAU Symposium 244, 'Dark Galaxies and Lost Baryons', 25th - 29th June 2007. Nine pages, five figures. Version 2 updates bibliograph

The stellar masses of 25000 galaxies at 0.2<z<1.0 estimated by the COMBO-17 survey

We present an analysis of stellar mass estimates for a sample of 25000 galaxies from the COMBO-17 survey over the interval 0.2<z<1.0. We have developed, implemented, and tested a new method of estimating stellar mass-to-light ratios, which relies on redshift and spectral energy distribution (SED) classification from 5 broadband and 12 medium band filters. We find that the majority (>60%) of massive galaxies with M_* > 10^{11} solar masses at all z<1 are non-star-forming; blue star-forming galaxies dominate at lower masses. We have used these mass estimates to explore the evolution of the stellar mass function since z=1. We find that the total stellar mass density of the universe has roughly doubled since z~1. Our measurements are consistent with other measurements of the growth of stellar mass with cosmic time and with estimates of the time evolution of the cosmic star formation rate. Intriguingly, the integrated stellar mass of blue galaxies with young stars has not significantly changed since z~1, even though these galaxies host the majority of the star formation: instead, the growth of the total stellar mass density is dominated by the growth of the total mass in the largely passive galaxies on the red sequence.Comment: Astronomy and Astrophysics in press. 15 pages, 12 figure

Nearly 5000 Distant Early-Type Galaxies in COMBO-17: a Red Sequence and its Evolution since z~1

We present the rest-frame colors and luminosities of ~25000 m_R<24 galaxies in the redshift range 0.2<z<1.1, drawn from 0.78 square degrees of the COMBO-17 survey. We find that the rest-frame color distribution of these galaxies is bimodal at all redshifts out to z~1. This bimodality permits a model-independent definition of red, early-type galaxies and blue, late-type galaxies at any given redshift. The colors of the blue peak become redder towards the present day, and the number density of blue luminous galaxies has dropped strongly since z~1. Focusing on the red galaxies, we find that they populate a color-magnitude relation. Such red sequences have been identified in galaxy cluster environments, but our data show that such a sequence exists over this redshift range even when averaging over all environments. The mean color of the red galaxy sequence evolves with redshift in a way that is consistent with the aging of an ancient stellar population. The rest-frame B-band luminosity density in red galaxies evolves only mildly with redshift in a Lambda-dominated cold dark matter universe. Accounting for the change in stellar mass-to-light ratio implied by the redshift evolution in red galaxy colors, the COMBO-17 data indicate an increase in stellar mass on the red sequence by a factor of two since z~1. The largest source of uncertainty is large-scale structure, implying that considerably larger surveys are necessary to further refine this result. We explore mechanisms that may drive this evolution in the red galaxy population, finding that both galaxy merging and truncation of star formation in some fraction of the blue, star-forming population are required to fully explain the properties of these galaxies.Comment: To appear in the Astrophysical Journal 20 June 2004. 16 pages, 6 embedded figures. Substantial revision of photometric redshifts and extensive minor changes to the paper throughout: conclusions unchange

Auto-identification of unphysical source reconstructions in strong gravitational lens modelling

With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy–galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a convolutional neural network (CNN) that analyses the outputs of semi-analytic methods that parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialized lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically reinitialize the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed Sérsic sources, accurately classifies source reconstructions of the same type with a precision P > 0.99 and recall R > 0.99. The same CNN, without retraining, achieves P = 0.89 and R = 0.89 when classifying source reconstructions of more complex lensed Hubble Ultra-Deep Field (HUDF) sources. Using the CNN predictions to reinitialize the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling

Star formation histories from multi-band photometry: A new approach

A new method of determining galaxy star-formation histories (SFHs) is presented. Using the method, the feasibility of recovering SFHs with multi-band photometry is investigated. The method divides a galaxy's history into discrete time intervals and reconstructs the average rate of star formation in each interval. This directly gives the total stellar mass. A simple linear inversion solves the problem of finding the most likely discretised SFH for a given set of galaxy parameters. It is shown how formulating the method within a Bayesian framework lets the data simultaneously select the optimal regularisation strength and the most appropriate number of discrete time intervals for the reconstructed SFH. The method is demonstrated by applying it to mono-metallic synthetic photometric catalogues created with different input SFHs, assessing how the accuracy of the recovered SFHs and stellar masses depend on the photometric passband set, signal-to-noise and redshift. The results show that reconstruction of SFHs using multi-band photometry is possible, being able to distinguish an early burst of star formation from a late one, provided an appropriate passband set is used. Although the resolution of the recovered SFHs is on average inferior compared to what can be achieved with spectroscopic data, the multi-band approach can process a significantly larger number of galaxies per unit exposure time.Comment: MNRAS accepted. 14 pages, 11 figure

Identifying strong lenses with unsupervised machine learning using convolutional autoencoder

In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the strong gravitational lenses finding challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii, distorted arc structures, etc., without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up 3c63 per cent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of 77.25 \ub1 0.48 per cent in binary classification using the training set. Additionally, our unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique