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Stellar spectral template libraries play an important role in the automated analysis of stellar spectra. Synthetic template libraries cover a very large parameter space but suffer from poor matching with observed spectra. In this study, we propose a synthetic-to-observed spectral translation (SOST) method based on generative adversarial networks. The SOST method is able to calibrate synthetic spectra by converting them to the corresponding observed spectra. We applied this method to Kurucz synthetic spectra and observed spectra data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). After that, we constructed a stellar spectral library with uniform and broad parameter distributions using the SOST-corrected Kurucz synthetic spectra. Our stellar spectral template library contains 2431 spectra spanning a parameter space of 3500–8000 K for effective temperature (Teff), 0.0–5.0 dex for surface gravity (log g), and −2.0–0.5 dex for metallicity ([Fe/H]). The spectra in the library have a resolution of R ∼ 1800 and cover the wavelength range 3900–8700 Å. In order to verify the accuracy of this template library, we used the template library and the template-matching algorithm to derive the parameters of the PASTEL database. Compared to measurements using the original synthetic template library, the accuracies of the three parameters, Teff, log g, and [Fe/H], are improved, from 140 K, 0.31 dex, and 0.21 dex to 121 K, 0.26 dex, and 0.13 dex, respectively. In addition, we re-parameterised more than six million stellar spectra released by LAMOST DR8. [ABSTRACT FROM AUTHOR]

The ensemble of chemical element abundance measurements for stars, along with precision distances and orbit properties, provides high-dimensional data to study the evolution of the Milky Way. With this third data release of the Galactic Archaeology with HERMES (GALAH) survey, we publish 678 423 spectra for 588 571 mostly nearby stars (81.2 per cent of stars are within <2 kpc), observed with the HERMES spectrograph at the Anglo-Australian Telescope. This release (hereafter GALAH+ DR3) includes all observations from GALAH Phase 1 (bright, main, and faint survey, 70 per cent), K2-HERMES (17 per cent), TESS-HERMES (5 per cent), and a subset of ancillary observations (8 per cent) including the bulge and >75 stellar clusters. We derive stellar parameters T eff, log  g , [Fe/H], v mic, v broad, and v rad using our modified version of the spectrum synthesis code Spectroscopy Made Easy (sme) and 1D marcs model atmospheres. We break spectroscopic degeneracies in our spectrum analysis with astrometry from Gaia  DR2 and photometry from 2MASS. We report abundance ratios [X/Fe] for 30 different elements (11 of which are based on non-LTE computations) covering five nucleosynthetic pathways. We describe validations for accuracy and precision, flagging of peculiar stars/measurements and recommendations for using our results. Our catalogue comprises 65 per cent dwarfs, 34 per cent giants, and 1 per cent other/unclassified stars. Based on unflagged chemical composition and age, we find 62 per cent young low- |$\alpha$|⁠ , 9 per cent young high- |$\alpha$|⁠ , 27 per cent old high- |$\alpha$|⁠ , and 2 per cent stars with [Fe/H] ≤ −1. Based on kinematics, 4 per cent are halo stars. Several Value-Added-Catalogues, including stellar ages and dynamics, updated after Gaia eDR3, accompany this release and allow chrono-chemodynamic analyses, as we showcase. [ABSTRACT FROM AUTHOR]

The Galactic Archaeology with HERMES (GALAH) survey is a large-scale stellar spectroscopic survey of the Milky Way, designed to deliver complementary chemical information to a large number of stars covered by the Gaia mission. We present the GALAH second public data release (GALAH DR2) containing 342 682 stars. For these stars, the GALAH collaboration provides stellar parameters and abundances for up to 23 elements to the community. Here we present the target selection, observation, data reduction, and detailed explanation of how the spectra were analysed to estimate stellar parameters and element abundances. For the stellar analysis, we have used a multistep approach. We use the physics-driven spectrum synthesis of Spectroscopy Made Easy (SME) to derive stellar labels (T eff, log  g, [Fe/H], [X/Fe], v mic, v sin  i, $$A_{K_S}$$) for a representative training set of stars. This information is then propagated to the whole sample with the data-driven method of The Cannon. Special care has been exercised in the spectral synthesis to only consider spectral lines that have reliable atomic input data and are little affected by blending lines. Departures from local thermodynamic equilibrium (LTE) are considered for several key elements, including Li, O, Na, Mg, Al, Si, and Fe, using 1D marcs stellar atmosphere models. Validation tests including repeat observations, Gaia benchmark stars, open and globular clusters, and K2 asteroseismic targets lend confidence to our methods and results. Combining the GALAH DR2 catalogue with the kinematic information from Gaia will enable a wide range of Galactic Archaeology studies, with unprecedented detail, dimensionality, and scope. [ABSTRACT FROM AUTHOR]

We present ELDAR, a new method that exploits the potential of medium- and narrow-band filter surveys to securely identify active galactic nuclei (AGN) and determine their redshifts. Our methodology improves on traditional approaches by looking for AGN emission lines expected to be identified against the continuum, thanks to the width of the filters. To assess its performance, we apply ELDAR to the data of the ALHAMBRA (Advance Large Homogeneous Area Medium Band Redshift Astronomical) survey, which covered an effective area of 2.38 deg2 with 20 contiguous medium-band optical filters down to F814W ≃ 24.5. Using two different configurations of ELDAR in which we require the detection of at least two and three emission lines, respectively, we extract two catalogues of type-I AGN. The first is composed of 585 sources (79 per cent of them spectroscopically unknown) down to F814W = 22.5 at zphot > 1, which corresponds to a surface density of 209 deg-2. In the second, the 494 selected sources (83 per cent of them spectroscopically unknown) reach F814W= 23 at zphot > 1.5, for a corresponding number density of 176 deg-2. Then, using samples of spectroscopically known AGN in the ALHAMBRA fields, for the two catalogues we estimate a completeness of 73 per cent and 67 per cent, and a redshift precision of 1.01 per cent and 0.86 per cent (with outliers fractions of 8.1 per cent and 5.8 per cent). At z > 2, where our selection performs best, we reach 85 per cent and 77 per cent completeness and we find no contamination from galaxies. [ABSTRACT FROM AUTHOR]

The Gaia mission will have a profound impact on our understanding of the structure and dynamics of the Milky Way. Gaia is providing an exhaustive census of stellar parallaxes, proper motions, positions, colors and radial velocities, but also leaves some glaring holes in an otherwise complete data set. The radial velocities measured with the on-board highresolution spectrograph will only reach some 10 % of the full sample of stars with astrometry and photometry from the mission, and detailed chemical information will be obtained for less than 1 %. Teams all over the world are organizing large-scale projects to provide complementary radial velocities and chemistry, since this can now be done very efficiently from the ground thanks to large and mid-size telescopes with a wide field-of-view and multi-object spectrographs. As a result, automated data processing is taking an ever increasing relevance, and the concept is applying to many more areas, from targeting to analysis. In this paper, I provide a quick overview of recent, ongoing, and upcoming spectroscopic surveys, and the strategies adopted in their automated analysis pipelines. [ABSTRACT FROM AUTHOR]

The Gaia SpectroPhotometric Standard Stars (SPSS) survey started in 2006, was awarded almost 450 observing nights and accumulated almost 100000 raw data frames with both photometric and spectroscopic observations. Such large observational effort requires careful, homogeneous, and automatic data reduction and quality control procedures. In this paper, we quantitatively evaluate instrumental effects that might have a significant (i.e., ≥1 %) impact on the Gaia SPSS flux calibration. The measurements involve six different instruments, monitored over the eight years of observations dedicated to the Gaia flux standards campaigns: DOLORES@TNG in La Palma, EFOSC2@NTT and ROSS@REM in La Silla, CAFOS@2.2m in Calar Alto, BFOSC@Cassini in Loiano, and LaRuca@1.5m in San Pedro Mártir. We examine and quantitatively evaluate the following effects: CCD linearity and shutter times, calibration frames stability, lamp flexures, second order contamination, light polarization, and fringing. We present methods to correct for the relevant effects which can be applied to a wide range of observational projects at similar instruments. (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by Ménard et al. This technique enables the inference of redshift distributions from measurements of the spatial clustering of arbitrary sources, using a set of reference objects for which redshifts are known. We apply it to a sample of spectroscopic galaxies from the Sloan Digital Sky Survey (SDSS) and show that, after carefully controlling the sampling efficiency over the sky, we can estimate redshift distributions with high accuracy. Probing the full colour space of the SDSS galaxies, we show that we can recover the corresponding mean redshifts with an accuracy ranging from δz = 0.001 to 0.01. We indicate that this mapping can be used to infer the redshift probability distribution of a single galaxy. We show how the lack of information on the galaxy bias limits the accuracy of the inference and show comparisons between clustering redshifts and photometric redshifts for this data set. This analysis demonstrates, using real data, that clustering-based redshift inference provides a powerful data-driven technique to explore the redshift distribution of arbitrary data sets, without any prior knowledge of the spectral energy distribution of the sources. [ABSTRACT FROM AUTHOR]

Context. A successful identification of an interstellar compound requires that its spectrum has first been assigned in the laboratory. New and sensitive radiotelescopes, such as ALMA, will make it possible to detect interstellar molecules in much smaller concentrations than before. Cyanoallene (CH2=C=CH-CN) has recently been observed in the dense molecular cloud TMC-1 by means of its rotational spectrum. Its methyl congener, 4-methylcyanoallene (CH3CH=C=CH-CN), may also be present in the interstellar medium (ISM). This chiral compound exists in two forms, which are mirror images. Chirality is an essential feature of life. So far, no chiral compounds have been detected in the ISM. Aims. The synthesis and assignment of the rotational spectrum of CH3CH=C=CH-CN, will facilitate the potential detection of this compound in the ISM. Methods. The spectrum of 4-methylcyanoallene has been recorded between 13 and 116 GHz using the microwave spectrometer at the University of Oslo. The spectroscopic study has been augmented with high-level quantum chemical calculations at the B3LYP/ccpVTZ and CCSD/cc-pVTZ levels of theory. Results. The rotational spectra of CH3CH=C=CH-CN in the ground vibrational state and in the first vibrationally excited state are reported for the first time and accurate spectroscopic constants have been obtained from a large number of transitions. [ABSTRACT FROM AUTHOR]

Context. Accurate determination of the redshifts of galaxies comes from the identification of key lines in their spectra. Large sky surveys, and the sheer volume of data they produce, have made it necessary to tackle this identification problem in an automated and reliable fashion. Current methods attempt to do this with careful modelling of the spectral lines and the continua, or by employing a flux/magnitude or a signal-to-noise cut to the dataset in order to obtain reliable redshift estimates for the majority of galaxies in the sample. Aims. In this paper, we present the Darth Fader algorithm (denoised and automatic redshifts thresholded with a false detection rate), which is a new wavelet-based method for estimating redshifts of galaxy spectra. Automated, simple, and largely empirical, we highlight how the Darth Fader algorithm performs in the very low signal-to-noise regime, and demonstrate its effectiveness at removing catastrophic failures from the catalogue of redshift estimates. Methods. We present a new, nonparametric method for estimating and removing the continuum in noisy data that requires no a priori information about the galaxy properties. This method employs wavelet filtering based on a tuneable false detection rate (FDR) threshold, which effectively removes the noise in the spectrum, and extracts features at different scales. After removal of the continuum, the galaxy spectra are then cross-correlated with the eigentemplates, and a standard χ2-minimisation used to determine the redshift of the spectrum. FDR filtering is applied to the spectra a second time to determine the number of spectral features in each galaxy spectrum, and those with fewer than six total features are removed from the catalogue as we are unlikely to obtain a reliable and correct estimate of the redshift of such spectra. Results. Applying our wavelet-based cleaning algorithm on a simulated testing set, we can successfully build a clean catalogue including extremely low signal-to-noise data (S/N = 2.0), for which we are able to obtain a 5.1% catastrophic failure rate in the redshift estimates (compared with 34.5% prior to cleaning). We also show that for a catalogue with uniformly mixed signal-to-noise ratios between 1.0 and 20.0, with realistic pixel-dependent noise, it is possible to obtain redshifts with a catastrophic failure rate of 3.3% after cleaning (as compared to 22.7% before cleaning). Whilst we do not test this algorithm exhaustively on real data, we present a proof of concept of the applicability of this method to real data, showing that the wavelet filtering techniques perform well when applied to some typical spectra from the Sloan Digital Sky Survey archive. Conclusions. The Darth Fader algorithm provides a robust method for extracting spectral features from very noisy spectra: spectra for which a reliable redshift cannot be measured are automatically identified and removed from the input data set. The resulting clean catalogue, although restricted in number, gives an extremely low rate of catastrophic failures, even when the spectra have a very low S/N. For very large sky surveys, this technique may offer a significant boost in the number of faint galaxies with accurately determined redshifts. [ABSTRACT FROM AUTHOR]

Context. The large amount of spectra obtained during the epoch of extensive spectroscopic surveys of Galactic stars needs the development of automatic procedures to derive their atmospheric parameters and individual element abundances. Aims. Starting from the widely-used code MOOG by C. Sneden, we have developed a new procedure to determine atmospheric parameters and abundances in a fully automatic way. The code FAMA (Fast Automatic MOOG Analysis) is presented describing its approach to derive atmospheric stellar parameters and element abundances. The code, freely distributed, is written in Perl and can be used on di erent platforms. Methods. The aim of FAMA is to render the computation of the atmospheric parameters and abundances of a large number of stars using measurements of equivalent widths (EWs) as automatic and as independent of any subjective approach as possible. It is based on the simultaneous search for three equilibria: excitation equilibrium, ionization balance, and the relationship between log n(Fe i) and the reduced EWs. FAMA also evaluates the statistical errors on individual element abundances and errors due to the uncertainties in the stellar parameters. The convergence criteria are not fixed "a priori" but are based on the quality of the spectra. Results. In this paper we present tests performed on the solar spectrum EWs that assess the method's dependency on the initial parameters and we analyze a sample of stars observed in Galactic open and globular clusters. [ABSTRACT FROM AUTHOR]

ABSTRACT We propose a method to estimate non-Gaussian error bars on the matter power spectrum from galaxy surveys in the presence of non-trivial survey selection functions. The estimators are often obtained from formalisms like Feldmann, Kaiser and Peacock (FKP) and pseudo-Karhunen-Loève (PKL), which rely on the assumption that the underlying field is Gaussian. The Monte Carlo method is more accurate but involves the tedious process of running and cross-correlating a large number of N-body simulations, in which the survey volume is embedded. From 200 N-body simulations, we extract a non-linear covariance matrix as a function of two scales and of the angle between two Fourier modes. All the non-Gaussian features of that matrix are then simply parametrized in terms of a few fitting functions and eigenvectors. We furthermore develop a fast and accurate strategy that combines our parametrization with a general galaxy survey selection function, and incorporate non-Gaussian Poisson uncertainty. We describe how to incorporate these two distinct non-Gaussian contributions into a typical analysis pipeline, and apply our method with the selection function from the 2dFGRS. We find that the observed Fourier modes correlate at much larger scales than that predicted by both FKP formalism or pure N-body simulations in a 'top hat' selection function. In particular, the observed Fourier modes are already 50 per cent correlated at k∼ 0.1 h Mpc−1, and the non-Gaussian fractional variance on the power spectrum [] is about a factor of 3.0 larger than the FKP prescription. At k∼ 0.4 h Mpc−1, the deviations are an order of magnitude. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper describes the duchamp source finder, a piece of software designed to find and describe sources in three-dimensional, spectral-line data cubes. duchamp has been developed with H i (neutral hydrogen) observations in mind, but is widely applicable to many types of astronomical images. It features efficient source detection and handling methods, noise suppression via smoothing or multi-resolution wavelet reconstruction, and a range of graphical and text-based outputs to allow the user to understand the detections. This paper details some of the key algorithms used and illustrates the effectiveness of the finder on different data sets. [ABSTRACT FROM AUTHOR]

In this paper, we release accurate photometric redshifts for 1692 counterparts to Chandra sources in the central square degree of the Cosmic Evolution Survey (COSMOS) field. The availability of a large training set of spectroscopic redshifts that extends to faint magnitudes enabled photometric redshifts comparable to the highest quality results presently available for normal galaxies. We demonstrate that morphologically extended, faint X-ray sources without optical variability are more accurately described by a library of normal galaxies (corrected for emission lines) than by active galactic nucleus (AGN) dominated templates, even if these sources have AGN-like X-ray luminosities. Preselecting the library on the bases of the source properties allowed us to reach an accuracy σΔz/(1+spec) ~ 0.015 with a fraction of outliers of 5.8% for the entire Chandra-COSMOS sample. In addition, we release revised photometric redshifts for the 1735 optical counterparts of the XMM-detected sources over the entire 2 deg² of COSMOS. For 248 sources, our updated photometric redshift differs from the previous release by Δz > 0.2. These changes are predominantly due to the inclusion of newly available deep H-band photometry (HAB = 24 magi. We illustrate once again the importance of a spectroscopic training sample and how an assumption about the nature of a source together, with the number and the depth of the available bands, influences the accuracy of the photometric redshifts determined for AGN. These considerations should be kept in mind when defining the observational strategies of upcoming large surveys targeting AGNs, such as eROSITA at X-ray energies and the Australian Square Kilometre Array Pathfinder Evolutionary Map of the Universe in the radio band. [ABSTRACT FROM AUTHOR]

We present a comparison between Gaussian processes (GPs) and artificial neural networks (ANNs) as methods for determining photometric redshifts for galaxies, given training-set data. In particular, we compare their degradation in performance as the training-set size is degraded in ways which might be caused by the observational limitations of spectroscopy. Using publicly available regression codes, we find that performance with large, complete training sets is very similar, although the ANN achieves slightly smaller rms errors. Training sets with brighter magnitude limits than the test data do not strongly affect the performance of either algorithm, until the limits are so severe that they remove almost all of the high-redshift training objects. Similarly, the introduction of a plausible number (up to 10 per cent) of inaccurate redshifts into the training set has little effect on either method. However, if the size of the training set is reduced by random sampling, the rms errors of both methods increase, but they do so to a lesser extent and in a much smoother manner for the case of GP regression; for the example presentedannz has rms errors 20 per cent worse than GP regression in the small training-set limit. Also, when training objects are removed at redshifts , to simulate the effects of the ‘redshift desert’ of optical spectroscopy, the GP regression is successful at interpolating across the redshift gap, while the ANN suffers from strong bias for test objects in this redshift range. Overall, GP regression has attractive properties for photometric redshift estimation, particularly for deep, high-redshift surveys where it is difficult to obtain a large, complete training set. At present, unlike the ANN code, public GP regression codes do not take account of inhomogeneous measurement errors on the photometric data, and thus cannot estimate reliable uncertainties on the predicted redshifts. However, a better treatment of errors is in principle possible, and the promising results in this paper suggest that such improved GP algorithms should be pursued. [ABSTRACT FROM AUTHOR]

To further our knowledge of the complex physical process of galaxy formation, it is essential that we characterize the formation and evolution of large data bases of galaxies. The spectral synthesisstarlight code of Cid Fernandes et al. was designed for this purpose. Results ofstarlight are highly dependent on the choice of input basis of simple stellar population (SSP) spectra. Speed of the code, which uses random walks through the parameter space, scales as the square of the number of the basis spectra, making it computationally necessary to choose a small number of SSPs that are coarsely sampled in age and metallicity. In this paper, we develop methods based on a diffusion map that, for the first time, choose appropriate bases of prototype SSP spectra from a large set of SSP spectra designed to approximate the continuous grid of age and metallicity of SSPs of which galaxies are truly composed. We show that our techniques achieve better accuracy of physical parameter estimation for simulated galaxies. Specifically, we show that our methods significantly decrease the age–metallicity degeneracy that is common in galaxy population synthesis methods. We analyse a sample of 3046 galaxies in Sloan Digital Sky Survey Data Release 6 and compare the parameter estimates obtained from different basis choices. [ABSTRACT FROM AUTHOR]

We develop and demonstrate a probabilistic method for classifying rare objects in surveys with the particular goal of building very pure samples. It works by modifying the output probabilities from a classifier so as to accommodate our expectation (priors) concerning the relative frequencies of different classes of objects. We demonstrate our method using the Discrete Source Classifier (DSC), a supervised classifier currently based on support vector machines, which we are developing in preparation for the Gaia data analysis. DSC classifies objects using their very low resolution optical spectra. We look in detail at the problem of quasar classification, because identification of a pure quasar sample is necessary to define the Gaia astrometric reference frame. By varying a posterior probability threshold in DSC, we can trade off sample completeness and contamination. We show, using our simulated data, that it is possible to achieve a pure sample of quasars (upper limit on contamination of 1 in 40 000) with a completeness of 65 per cent at magnitudes of , and 50 per cent at , even when quasars have a frequency of only 1 in every 2000 objects. The star sample completeness is simultaneously 99 per cent with a contamination of 0.7 per cent. Including parallax and proper motion in the classifier barely changes the results. We further show that not accounting for class priors in the target population leads to serious misclassifications and poor predictions for sample completeness and contamination. We discuss how a classification model prior may, or may not, be influenced by the class distribution in the training data. Our method controls this prior and so allows a single model to be applied to any target population without having to tune the training data and retrain the model. [ABSTRACT FROM AUTHOR]

We aim to obtain a complete sample of redshift radio quasi-stellar objects (QSOs) from the Faint Images of the Radio Sky at Twenty cm survey (FIRST) sources having star-like counterparts in the Sloan Digital Sky Survey (SDSS) Data Release 5 (DR5) photometric survey . Our starting sample of 8665 FIRST–DR5 pairs includes 4250 objects with spectra in DR5, 52 of these being QSOs. We found that simple supervised neural networks, trained on the sources with DR5 spectra, and using optical photometry and radio data, are very effective for identifying high- z QSOs in a sample without spectra. For the sources with DR5 spectra the technique yields a completeness (fraction of actual high- z QSOs classified as such by the neural network) of 96 per cent, and an efficiency (fraction of objects selected by the neural network as high- z QSOs that actually are high- z QSOs) of 62 per cent. Applying the trained networks to the 4415 sources without DR5 spectra we found QSO candidates. We obtained spectra of 27 of them, and 17 are confirmed as high- z QSOs. Spectra of 13 additional candidates from the literature and from SDSS Data Release 6 (DR6) revealed seven more QSOs, giving an overall efficiency of 60 per cent (24/40). None of the non-candidates with spectra from NASA/IPAC Extragalactic Database (NED) or DR6 is a QSO, consistently with a high completeness. The initial sample of high- z QSOs is increased from 52 to 76 sources, i.e. by a factor of 1.46. From the new identifications and candidates we estimate an incompleteness of SDSS for the spectroscopic classification of FIRST QSOs of 15 per cent for . [ABSTRACT FROM AUTHOR]

We compare the performance of two automated classification algorithms, k-dimensional tree (kd-tree) and support vector machines (SVMs), to separate quasars from stars in the data bases of the Sloan Digital Sky Survey (SDSS) and the Two-Micron All Sky Survey (2MASS) catalogues. The two algorithms are trained on subsets of SDSS and 2MASS objects whose nature is known via spectroscopy. We choose different attribute combination as input patterns to train the classifier using photometric data only and present the classification results obtained by these two methods. Performance metrics, such as precision and recall, true positive rate and true negative rate, F-measure, G-mean and Weighted Accuracy, are computed to evaluate the performance of the two algorithms. The study shows that both kd-tree and SVMs are effective automated algorithms to classify point sources. SVMs show slightly higher accuracy, but kd-tree requires less computation time. Given different input patterns based on various parameters (e.g. magnitudes, colour information), we conclude that both kd-tree and SVMs show better performance with fewer features. What is more, our results also indicate that the accuracy using the four colours ( u− g, g− r, r− i and i− z) and r magnitude based on SDSS model magnitudes adds up to the highest value. The classifiers trained by kd-tree and SVMs can be used to solve the automated classification problems faced by the virtual observatory (VO); moreover, they can all be applied for the photometric preselection of quasar candidates for large survey projects in order to optimise the efficiency of telescopes. [ABSTRACT FROM AUTHOR]

The Shear Testing Programme (STEP) is a collaborative project to improve the accuracy and reliability of weak-lensing measurement, in preparation for the next generation of wide-field surveys. We review 16 current and emerging shear-measurement methods in a common language, and assess their performance by running them (blindly) on simulated images that contain a known shear signal. We determine the common features of algorithms that most successfully recover the input parameters. A desirable goal would be the combination of their best elements into one ultimate shear-measurement method. In this analysis, we achieve previously unattained discriminatory precision via a combination of more extensive simulations and pairs of galaxy images that have been rotated with respect to each other. That removes the otherwise overwhelming noise from their intrinsic ellipticities. Finally, the robustness of our simulation approach is confirmed by testing the relative calibration of methods on real data. Weak-lensing measurements have improved since the first STEP paper. Several methods now consistently achieve better than 2 per cent precision, and are still being developed. However, we can now distinguish all methods from perfect performance. Our main concern continues to be the potential for a multiplicative shear calibration bias: not least because this cannot be internally calibrated with real data. We determine which galaxy populations are responsible for bias and, by adjusting the simulated observing conditions, we also investigate the effects of instrumental and atmospheric parameters. The simulated point spread functions are not allowed to vary spatially, to avoid additional confusion from interpolation errors. We have isolated several previously unrecognized aspects of galaxy shape measurement, in which focused development could provide further progress towards the sub-per cent level of precision desired for future surveys. These areas include the suitable treatment of image pixellization and galaxy morphology evolution. Ignoring the former effect affects the measurement of shear in different directions, leading to an overall underestimation of shear and hence the amplitude of the matter power spectrum. Ignoring the second effect could affect the calibration of shear estimators as a function of galaxy redshift, and the evolution of the lensing signal, which will be vital to measure parameters including the dark energy equation of state. [ABSTRACT FROM AUTHOR]