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We revisit the issue of non-parametric gravitational lens reconstruction and present a new method to obtain the cluster mass distribution using strong lensing data without using any prior information on the underlying mass. The method relies on the decomposition of the lens plane into individual cells. We show how the problem in this approximation can be expressed as a system of linear equations for which a solution can be found. Moreover, we propose to include information about the null space. That is, make use of the pixels where we know there are no arcs above the sky noise. The only prior information is an estimation of the physical size of the sources. No priors on the luminosity of the cluster or shape of the halos are needed thus making the results very robust. In order to test the accuracy and bias of the method we make use of simulated strong lensing data. We find that the method reproduces accurately both the lens mass and source positions and provide error estimates.Comment: This is the accepted version in MNRAS. Thsi includes improvements suggested by the referee and one new plot. Additional material can be found in http://darwin.cfa.harvard.edu/SLAP/index.asp

An approach is presented for the theoretical calculation of self-diffusion coefficients of liquid metals. The basic assumption is that the self-diffusion coefficient of a liquid metal is equal to that of an appropriate hard sphere fluid. The hard sphere diameter is dependent upon temperature, and a method is developed for estimating this temperature dependence by exploring the relationship between the diameter and the interatomic potential energy function of the liquid metal. The theory gives accurate results for the magnitude and temperature dependence of the self-diffusion coefficient for many liquid metals. In addition, the physical basis for the theory is consistent with what has been learned about the liquid state from molecular dynamics calculations.

We use the latest Planck constraints, and in particular constraints on the derived parameters (Hubble constant and age of the Universe) for the local universe and compare them with local measurements of the same quantities. We propose a way to quantify whether cosmological parameters constraints from two different experiments are in tension or not. Our statistic, T , is an evidence ratio and therefore can be interpreted with the widely used Jeffrey's scale. We find that in the framework of the ΛCDM model, the Planck inferred two dimensional, joint, posterior distribution for the Hubble constant and age of the Universe is in "strong" tension with the local measurements; the odds being ∼ 1:50. We explore several possibilities for explaining this tension and examine the consequences both in terms of unknown errors and deviations from the ΛCDM model. In some one-parameter ΛCDM model extensions, tension is reduced whereas in other extensions, tension is instead increased. In particular, small total neutrino masses are favored and a total neutrino mass above 0.15 eV makes the tension "highly significant" (odds ∼ 1:150). A consequence of accepting this interpretation of the tension is that the degenerate neutrino hierarchy is highly disfavoured by cosmological data and the direct hierarchy is slightly favored over the inverse.

The EPOCH (EROS-2 periodic variable star classification using machine learning) project aims to detect periodic variable stars in the EROS-2 light curve database. In this paper, we present the first result of the classification of periodic variable stars in the EROS-2 LMC database. To classify these variables, we first built a training set by compiling known variables in the Large Magellanic Cloud area from the OGLE and MACHO surveys. We crossmatched these variables with the EROS-2 sources and extracted 22 variability features from 28 392 light curves of the corresponding EROS-2 sources. We then used the random forest method to classify the EROS-2 sources in the training set. We designed the model to separate not only δ Scuti stars, RR Lyraes, Cepheids, eclipsing binaries, and long-period variables, the superclasses, but also their subclasses, such as RRab, RRc, RRd, and RRe for RR Lyraes, and similarly for the other variable types. The model trained using only the superclasses shows 99% recall and precision, while the model trained on all subclasses shows 87% recall and precision. We applied the trained model to the entire EROS-2 LMC database, which contains about 29 million sources, and found 117 234 periodic variable candidates. Out of these 117 234 periodic variables, 55 285 have not been discovered by either OGLE or MACHO variability studies. This set comprises 1906 δ Scuti stars, 6607 RR Lyraes, 638 Cepheids, 178 Type II Cepheids, 34 562 eclipsing binaries, and 11 394 long-period variables.

We describe a method to estimate the mass distribution of a gravitational lens and the position of the sources from combined strong and weak lensing data. The algorithm combines weak and strong lensing data in a unified way producing a solution which is valid in both the weak and strong lensing regimes. We study how the result depends on the relative weighting of the weak and strong lensing data and on choice of basis to represent the mass distribution. We find that combining weak and strong lensing information has two major advantages: it eliminates the need for priors and/or regularization schemes for the intrinsic size of the background galaxies (this assumption was needed in previous strong lensing algorithms) and it corrects for biases in the recovered mass in the outer regions where the strong lensing data is less sensitive. The code is implemented into a software package called WSLAP (Weak & Strong Lensing Analysis Package) which is publicly available at http://darwin.cfa.harvard.edu/SLAP/Comment: 10 pages. 9 figures. MNRAS submitte

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We present a methodology to discover outliers in catalogues of periodic light curves. We use a cross‐correlation as the measure of ‘similarity’ between two individual light curves, and then classify light curves with lowest average ‘similarity’ as outliers. We performed the analysis on catalogues of periodic variable stars of known type from the MACHO and OGLE projects. This analysis was carried out in Fourier space and we established that our method correctly identifies light curves that do not belong to those catalogues as outliers. We show how an approximation to this method, carried out in real space, can scale to large data sets that will be available in the near future such as those anticipated from the Panoramic Survey Telescope & Rapid Response System (Pan‐STARRS) and Large Synoptic Survey Telescope (LSST).