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Synthetic biology has captivated scientists' imagination. It promises answers to some of the grand challenges society is facing: worsening climate crisis, insufficient food supplies for ever growing populations, and many persisting infectious and genetic diseases. While many challenges remain unaddressed, after almost two decades since its inception a number of products created by engineered biology are starting to reach the public. European scientists and entrepreneurs have been participating in delivering on the promises of synthetic biology. Associations like the European Synthetic Biology Society (EUSynBioS) play a key role in disseminating advances in the field, connecting like-minded people and promoting scientific development. In this perspective article, we review the current landscape of the synthetic biology community in Europe, discussing the state of related academic research and industry. We also discuss how EUSynBioS has helped to build bridges between professionals across the continent.

BACKGROUND The COVID-19 pandemic has significantly impacted dental healthcare and training. Preventing droplet spread and surface contamination is paramount to keeping patients and practitioners safe. There have been rapid developments in this area and in aerosol reduction. OBJECTIVES To determine whether the use of an AerosolShield reduced droplet splatter during a dental procedure. METHODS We performed an assessment of dental splatter, with and without an AerosolShield, a small self-assembling plastic tent. We used fluorescein-stained dental fluid to mark the droplets and captured them on paper targets. We imaged the fluorescent droplet spots under UV light and analysed their size and position using astronomical image analysis techniques. RESULTS The mean number of spots in the AerosolShield group was 4 spots (SD 7), and 4900 (SD 3100) spots in the control group, a reduction of 99.2% (95% CI 0.99-1.00). The mean area covered of the closest targets was 0.98% (SD 0.43%) for the control (no shield) group, compared with 2.5×10-5 % (SD 2.3×10-5 %) for the AerosolShield procedures (P < 0.001). The mean maximum distance travelled by the smaller droplets in the control group procedures was 676.3mm (SD 53.9mm), compared with 213.2mm (SD 13.2mm) in the AerosolShield group. CONCLUSIONS These results show that the AerosolShield reduced the number of droplets detected by 99.2% (95% CI 0.99, 1.0), and the area of target sheets covered in droplets was also reduced by 99.7% (95% CI 0.99, 1.0). We found the AerosolShield a useful method of reducing droplet spread in a dental clinic model. More work on human volunteers and patients would be useful to assess whether the AerosolShield is practical, comfortable, and to confirm that these experimental results are representative.

Introduction COVID-19 has impacted ophthalmic care delivery, with many units closed and several ophthalmologists catching COVID-19. Understanding droplet spread in clinical and training settings is paramount in maintaining productivity, while keeping patients and practitioners safe. Objectives We aimed to assess the effectiveness of a breath-guard and a face mask in reducing droplet spread within an eye clinic. Methods We performed a randomised trial of droplet spread using a fluorescein-based cough model to assess the efficacy of a ‘breath-guard’ and ‘face-mask’ to prevent the spread of droplets. The ‘cough’ spray was collected on calibrated paper targets. The sheets were photographed under blue light, with an orange filter on the camera; the position and size of the spots was measured with software originally developed for astronomy. We performed 44 randomised coughs; 22 controls with no breath-guard or face-mask, 11 using breath-guard only and 11 with combined breath-guard and face-mask. We compared both the number of droplets detected and the area of drops on paper targets. Results The average number of droplets in the controls was 19,430 (SE 2691), the breath-guard group 80 (SE 19) droplets (P P = 0.008). The mean areas of each target covered by spots for each group were 5.7 ± 0.857% (95% CI), 0.004 ± 0.000104% (95% CI) and 0.001 ± 0.0000627% (95% CI) respectively. Conclusion These results show that the breath-guard alone reduced the droplet count by 99.93%. Combining the breath-guard with a face-mask reduced the droplet count by over 99.98%. Breath-guards are widely used in clinics and this trial demonstrates that breath-guards with face-masks effectively block droplet spray.

Background Thorough disinfection of dental facilities is of paramount importance during the COVID-19 pandemic. Patients, clinicians, students and nurses can all be infected by aerosols and dental droplets bearing COVID-19. However, droplets are transparent and often microscopic, so are difficult to detect in clinical practice.Methods To better understand the spread of dental droplets, we stained the dental irrigant with fluorescein and performed a series of procedures on a dental manikin. We then viewed droplets and fomite spread around the dental chair, with and without an ultraviolet (UV) light.Results Observations without the UV light showed minimal or no fluid spread. However, using UV light, we detected fluorescein on the dentist, chairs and the handpiece, as well as splatter on the floor and on the instrument tray. This was of educational value to the staff, who were reminded how far droplets had spread.Conclusion Fluorescein facilitates the detection of droplet spread and helps clinical staff to see high-risk areas that require in-depth cleaning. As clinical grade fluorescein is cheap and widely available, this technique may be useful for dental practices to train staff in the thorough decontamination of the clinical environment.

Full author list: A Pieres, L Girardi, E Balbinot, B Santiago, L N da Costa, A Carnero Rosell, A B Pace, K Bechtol, M A T Groenewegen, A Drlica-Wagner, T S Li, M A G Maia, R L C Ogando, M dal Ponte, H T Diehl, A Amara, S Avila, E Bertin, D Brooks, D L Burke, M Carrasco Kind, J Carretero, J De Vicente, S Desai, T F Eifler, B Flaugher, P Fosalba, J Frieman, J García-Bellido, E Gaztanaga, D W Gerdes, D Gruen, R A Gruendl, J Gschwend, G Gutierrez, D L Hollowood, K Honscheid, D J James, K Kuehn, N Kuropatkin, J L Marshall, R Miquel, A A Plazas, E Sanchez, S Serrano, I Sevilla-Noarbe, E Sheldon, M Smith, M Soares-Santos, F Sobreira, E Suchyta, M E C Swanson, G Tarle, D Thomas, V Vikram, A R Walker, We present a technique to fit the stellar components of the Galaxy by comparing Hess Diagrams (HDs) generated from TRILEGAL models to real data. We apply this technique, which we call MWFITTING, to photometric data from the first 3 yr of the Dark Energy Survey (DES). After removing regions containing known resolved stellar systems such as globular clusters, dwarf galaxies, nearby galaxies, the Large Magellanic Cloud, and the Sagittarius Stream, our main sample spans a total area of ~2300 deg2. We further explore a smaller subset (~1300 deg2) that excludes all regions with known stellar streams and stellar overdensities. Validation tests on synthetic data possessing similar properties to the DES data show that the method is able to recover input parameters with a precision better than 3 per cent. We fit the DES data with an exponential thick disc model and an oblate double power-law halo model. We find that the best-fitting thick disc model has radial and vertical scale heights of 2.67 ± 0.09 kpc and 925 ± 40 pc, respectively. The stellar halo is fit with a broken power-law density profile with an oblateness of 0.75 ± 0.01, an inner index of 1.82 ± 0.08, an outer index of 4.14 ± 0.05, and a break at 18.52 ± 0.27 kpc from the Galactic centre. Several previously discovered stellar overdensities are recovered in the residual stellar density map, showing the reliability of MWFITTING in determining the Galactic components. Simulations made with the best-fitting parameters are a promising way to predict Milky Way star counts for surveys such as the LSST and Euclid., The DESDM is supported by the National Science Foundation under grants AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and the Brazilian Instituto Nacional de Ci?ncia e Tecnologia (INCT) do e-Universe (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Centre for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Centre for Cosmology and AstroParticle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundac¸ão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovac¸ão, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the DES.

Photometric redshifts are commonly used to measure the distribution of galaxies in large surveys. However, the demands of ongoing and future large-scale cosmology surveys place very stringent limits on the redshift performance that are difficult to meet. A new approach to meet this precision need is forward modelling, which is underpinned by realistic simulations. In the work presented here, we use simulations to study the sensitivity of redshift distributions to the underlying galaxy population demographics. We do this by varying the redshift evolving parameters of the Schechter function for two galaxy populations: star-forming and quenched galaxies. Each population is characterized by eight parameters. We find that the redshift distribution of shallow surveys, such as the Sloan Digital Sky Survey (SDSS), is mainly sensitive to the parameters for quenched galaxies. However, for deeper surveys such as the Dark Energy Survey (DES) and the Hyper Suprime-Cam (HSC), the star-forming parameters have a stronger impact on the redshift distribution. Specifically, the slope of the characteristic magnitude, aM, for star-forming galaxies has overall the strongest impact on the redshift distribution. Decreasing aM by 148 per cent (its given uncertainty) shifts the mean redshift by ∼45 per cent. We explore which combination of colour and magnitude measurements is most sensitive to aM and we find that each colour–magnitude pair studied is similarly affected by a modification of aM.

Narrow-band imaging surveys allow the study of the spectral characteristics of galaxies without the need of performing their spectroscopic follow-up. In this work, we forward-model the Physics of the Accelerating Universe Survey (PAUS) narrow-band data. The aim is to improve the constraints on the spectral coefficients used to create the galaxy spectral energy distributions (SED) of the galaxy population model in Tortorelli et al. 2020. In that work, the model parameters were inferred from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) data using Approximate Bayesian Computation (ABC). This led to stringent constraints on the B-band galaxy luminosity function parameters, but left the spectral coefficients only broadly constrained. To address that, we perform an ABC inference using CFHTLS and PAUS data. This is the first time our approach combining forward-modelling and ABC is applied simultaneously to multiple datasets. We test the results of the ABC inference by comparing the narrow-band magnitudes of the observed and simulated galaxies using Principal Component Analysis, finding a very good agreement. Furthermore, we prove the scientific potential of the constrained galaxy population model to provide realistic stellar population properties by measuring them with the SED fitting code \textsc{CIGALE}. We use CFHTLS broad-band and PAUS narrow-band photometry for a flux-limited ($\mathrm{i}, 53 pages, 17 figures, Accepted for publication in JCAP

Likelihood-free inference provides a rigorous approach to preform Bayesian analysis using forward simulations only. The main advantage of likelihood-free methods is its ability to account for complex physical processes and observational effects in forward simulations. Here we explore the potential of likelihood-free forward modeling for Bayesian cosmological inference using the redshift evolution of the cluster abundance combined with weak-lensing mass calibration. We use two complementary likelihood-free methods, namely Approximate Bayesian Computation (ABC) and Density-Estimation Likelihood-Free Inference (DELFI), to develop an analysis procedure for inference of the cosmological parameters $(\Omega_\mathrm{m},\sigma_8)$ and the mass scale of the survey sample. Adopting an eROSITA-like selection function and a 10-percent scatter in the observable-mass relation in a flat $\Lambda$CDM cosmology with $\Omega_\mathrm{m}=0.286$ and $\sigma_8=0.82$, we create a synthetic catalog of observable-selected NFW clusters in a survey area of 50 deg$^2$. The stacked tangential shear profile and the number counts in redshift bins are used as summary statistics for both methods. By performing a series of forward simulations, we obtain convergent solutions for the posterior distribution from both methods. We find that ABC recovers broader posteriors than DELFI, especially for the $\Omega_\mathrm{m}$ parameter. For a weak-lensing survey with a source density of $n_\mathrm{g}=20$ arcmin$^{-2}$, we obtain posterior constraints on $S_8=\sigma_8(\Omega_\mathrm{m}/0.3)^{0.3}$ of $0.836 \pm 0.032$ and $0.810 \pm 0.019$ from ABC and DELFI, respectively. The analysis framework developed in this study will be particularly powerful for cosmological inference with ongoing cluster cosmology programs, such as the XMM-XXL survey and the eROSITA all-sky survey, in combination with wide-field weak-lensing surveys., Comment: Version accepted by ApJ, with textual changes to improve clarity (e.g., more details on the simulation procedure added in Section 3; a general procedure for modeling the mass scaling relations added as Appendix A; toy model simulations moved to Appendix B); results and conclusions remain unchanged; 15 pages, 11 figures

SkyPy is an open-source Python package for simulating the astrophysical sky. It comprises a library of physical and empirical models across a range of observables and a command-line script to run end-to-end simulations. The library provides functions that sample realisations of sources and their associated properties from probability distributions. Simulation pipelines are constructed from these models using a YAML-based configuration syntax, while task scheduling and data dependencies are handled internally and the modular design allows users to interface with external software. SkyPy is developed and maintained by a diverse community of domain experts with a focus on software sustainability and interoperability. By fostering development, it provides a framework for correlated simulations of a range of cosmological probes including galaxy populations, large scale structure, the cosmic microwave background, supernovae and gravitational waves. Version 0.4 implements functions that model various properties of galaxies including luminosity functions, redshift distributions and optical photometry from spectral energy distribution templates. Future releases will provide additional modules, for example, to simulate populations of dark matter halos and model the galaxy-halo connection, making use of existing software packages from the astrophysics community where appropriate., Published by JOSS. The package is available at https://github.com/skypyproject/skypy. Comments, issues and pull requests are welcome

Stanford, S. A., et al., The Complete Calibration of the Color–Redshift Relation (C3R2) survey is obtaining spectroscopic redshifts in order to map the relation between galaxy color and redshift to a depth of i ∼ 24.5 (AB). The primary goal is to enable sufficiently accurate photometric redshifts for Stage iv dark energy projects, particularly Euclid and the Nancy Grace Roman Space Telescope (Roman), which are designed to constrain cosmological parameters through weak lensing. We present 676 new high-confidence spectroscopic redshifts obtained by the C3R2 survey in the 2017B–2019B semesters using the DEIMOS, LRIS, and MOSFIRE multiobject spectrographs on the Keck telescopes. Combined with the 4454 redshifts previously published by this project, the C3R2 survey has now obtained and published 5130 high-quality galaxy spectra and redshifts. If we restrict consideration to only the 0.2 < zp < 2.6 range of interest for the Euclid cosmological goals, then with the current data release, C3R2 has increased the spectroscopic redshift coverage of the Euclid color space from 51% (as reported by Masters et al.) to the current 91%. Once completed and combined with extensive data collected by other spectroscopic surveys, C3R2 should provide the spectroscopic calibration set needed to enable photometric redshifts to meet the cosmology requirements for Euclid, and make significant headway toward solving the problem for Roman.

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce LUMOS, a deep learning method to measure photometry from galaxy images. LUMOS builds on BKGNET, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed LUMOS for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, LUMOS increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artefacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10 to 2 per cent, comparing to aperture photometry. Furthermore, with LUMOS photometry, the photo-z scatter is reduced by ≈10 per cent with the Deepz machine-learning photo-z code and the photo-z outlier rate by 20 per cent. The photo-z improvement is lower than expected from the SNR increment, however, currently the photometric calibration and outliers in the photometry seem to be its limiting factor., The PAU Survey is partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509, PID2019-111317GB-C31 and Juan de la Cierva fellowship and LACEGAL and EWC Marie Sklodowska-Curie grant No 734374 and no.776247 with ERDF funds from the EU Horizon 2020 Programme, some of which include ERDF funds from the European Union. IEEC and IFAE are partially funded by the CERCA and Beatriu de Pinos program of the Generalitat de Catalunya. Funding for PAUS has also been provided by Durham University (via the ERC StG DEGAS-259586), ETH Zurich, Leiden University (via ERC StG ADULT-279396 and Netherlands Organisation for Scientific Research (NWO) Vici grant 639.043.512), Bochum University (via a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1) as well as an ERC Consolidator Grant (No. 770935)), University College London, Portsmouth support through the Royal Society Wolfson fellowship and from the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 776247 EWC.

lenstronomy is an Astropy-affiliated Python package for gravitational lensing simulations and analyses. lenstronomy was introduced by Birrer and Amara (2018) and is based on the linear basis set approach by Birrer et a. (2015). The user and developer base of lenstronomy has substantially grown since then, and the software has become an integral part of a wide range of recent analyses, such as measuring the Hubble constant with time-delay strong lensing or constraining the nature of dark matter from resolved and unresolved small scale lensing distortion statistics. The modular design has allowed the community to incorporate innovative new methods, as well as to develop enhanced software and wrappers with more specific aims on top of the lenstronomy API. Through community engagement and involvement, lenstronomy has become a foundation of an ecosystem of affiliated packages extending the original scope of the software and proving its robustness and applicability at the forefront of the strong gravitational lensing community in an open source and reproducible manner., Comment: published by JOSS. Software available at https://github.com/sibirrer/lenstronomy. Comments, issues and pull requests welcome!

Reducing COVID-19 transmission relies on controlling droplet and aerosol spread. Fluorescein staining reveals microscopic droplets.To compare the droplet spread in non-laminar and laminar air flow operating theatres.A 'cough-generator' was fixed to a theatre trolley at 45°. Fluorescein-stained 'secretions' were projected on to a series of calibrated targets. These were photographed under UV light and 'source detection' software measured droplet splatter size and distance.The smallest droplet detected was ∼120 μm and the largest ∼24,000 μm. An average of 25,862 spots was detected in the non-laminar theatre, compared with 11,430 in the laminar theatre (56% reduction). The laminar air flow mainly affected the smaller droplets (1000 μm). The surface area covered with droplets was: 6% at 50 cm, 1% at 2 m, and 0.5% at 3 m in the non-laminar air flow; and 3%, 0.5%, and 0.2% in the laminar air flow, respectively.Accurate mapping of droplet spread in clinical environments is possible using fluorescein staining and image analysis. The laminar air flow affected the smaller droplets but had limited effect on larger droplets in our 'aerosol-generating procedure' cough model. Our results indicate that the laminar air flow theatre requires similar post-surgery cleaning to the non-laminar, and staff should consider full personal protective equipment for medium- and high-risk patients.

Ilbert, O., et al. (Euclid Collaboration), The analysis of weak gravitational lensing in wide-field imaging surveys is considered to be a major cosmological probe of dark energy. Our capacity to constrain the dark energy equation of state relies on an accurate knowledge of the galaxy mean redshift ⟨ z⟩. We investigate the possibility of measuring ⟨ z»with an accuracy better than 0.002(1 + z) in ten tomographic bins spanning the redshift interval 0.2 99.8%. The zPDF approach can also be successful if the zPDF is de-biased using a spectroscopic training sample. This approach requires deep imaging data but is weakly sensitive to spectroscopic redshift failures in the training sample. We improve the de-biasing method and confirm our finding by applying it to real-world weak-lensing datasets (COSMOS and KiDS+VIKING-450)., H. Hildebrandt is supported by a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1) as well as an ERC Consolidator Grant (No. 770935). A.H. Wright is supported by the ERC Consolidator Grant (No. 770935). This work relied on the HPC resources of CINES (Jade) under the allocation 2013047012 and c2014047012 made by GENCI and on the Horizon Cluster hosted by Institut d’Astrophysique de Paris. ID acknowledges that he received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 896225. We warmly thank S. Rouberol for running the cluster on which the simulation was post-processed. This research is also partly supported by the Centre National d’Etudes Spatiales (CNES). We would also like to recognise the contributions from all of the members of the COSMOS team who helped in obtaining and reducing the large amount of multi-wavelength and spectroscopic data. Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme IDs 177.A-3016, 177.A-3017, 177.A-3018, 179.A-2004, and on data products produced by the KiDS consortium. The KiDS production team acknowledges support from: Deutsche Forschungsgemeinschaft, ERC, NOVA and NWO-M grants; Target; the University of Padova, and the University Federico II (Naples). SA thank the support PRIN MIUR2015 “Cosmology and Fundamental Physics: Illuminating the Dark Universe with Euclid”.

The combination of different cosmological probes offers stringent tests of the $\Lambda$CDM model and enhanced control of systematics. For this purpose, we present an extension of the lightcone generator UFalcon first introduced in Sgier et al. 2019 (arXiv:1801.05745), enabling the simulation of a self-consistent set of maps for different cosmological probes. Each realization is generated from the same underlying simulated density field, and contains full-sky maps of different probes, namely weak lensing shear, galaxy overdensity including RSD, CMB lensing, and CMB temperature anisotropies from the ISW effect. The lightcone generation performed by UFalcon is parallelized and based on the replication of a large periodic volume simulated with the GPU-accelerated $N$-Body code PkdGrav3. The post-processing to construct the lightcones requires only a runtime of about 1 walltime-hour corresponding to about 100 CPU-hours. We use a randomization procedure to increase the number of quasi-independent full-sky UFalcon map-realizations, which enables us to compute an accurate multi-probe covariance matrix. Using this framework, we forecast cosmological parameter constraints by performing a multi-probe likelihood analysis for a combination of simulated future stage-IV-like surveys. We find that the inclusion of the cross-correlations between the probes significantly increases the information gain in the parameter constraints. We also find that the use of a non-Gaussian covariance matrix is increasingly important, as more probes and cross-correlation power spectra are included. A version of the UFalcon package currently including weak gravitational lensing is publicly available., Comment: 49 pages, 24 pictures, The UFalcon weak lensing package is available here: $\href{https://cosmology.ethz.ch/research/software-lab/UFalcon.html}{https://cosmology.ethz.ch/research/software-lab/UFalcon.html}$

Current and upcoming cosmological experiments open a new era of precision cosmology, thus demanding accurate theoretical predictions for cosmological observables. Because of the complexity of the codes delivering such predictions, reaching a high level of numerical accuracy is challenging. Among the codes already fulfilling this task, PyCosmo is a Python-based framework providing solutions to the Einstein–Boltzmann equations and accurate predictions for cosmological observables. We present the first public release of the code, which is valid in ΛCDM cosmology. The novel aspect of this version is that the user can work within a Python framework, either locally or through an online platform, called PyCosmo Hub. In this work we first describe how the observables are implemented. Then, we check the accuracy of the theoretical predictions for background quantities, power spectra and Limber and beyond-Limber angular power spectra by comparison with other codes: the Core Cosmology Library (CCL), CLASS, HMCode and iCosmo. In our analysis we quantify the agreement of PyCosmo with the other codes, for a range of cosmological models, monitored through a series of unit tests. PyCosmo, conceived as a multi-purpose cosmology calculation tool in Python, is designed to be interactive and user-friendly. The PyCosmo Hub is accessible from this link: https://cosmology.ethz.ch/research/software-lab/PyCosmo.html. On this platform the users can perform their own computations using Jupyter Notebooks without the need of installing any software, access to the results presented in this work and benefit from tutorial notebooks illustrating the usage of the code. The link above also redirects to the code release and documentation., Astronomy and Computing, 36, ISSN:2213-1337

We present deep $g$- and $r$-band Magellan/Megacam photometry of two dwarf galaxy candidates discovered in the Dark Energy Survey (DES), Grus I and Indus II (DES J2038-4609). For the case of Grus I, we resolved the main sequence turn-off (MSTO) and $\sim 2$ mags below it. The MSTO can be seen at $g_0\sim 24$ with a photometric uncertainty of $0.03$ mag. We show Grus I to be consistent with an old, metal-poor ($\sim 13.3$ Gyr, [Fe/H]$\sim-1.9$) dwarf galaxy. We derive updated distance and structural parameters for Grus I using this deep, uniform, wide-field data set. We find an azimuthally averaged half-light radius more than two times larger ($\sim 151^{+21}_{-31}$ pc; $\sim 4.^{\prime} 16^{+0.54}_{-0.74}$) and an absolute $V$-band magnitude $\sim-4.1$ that is $\sim 1$ magnitude brighter than previous studies. We obtain updated distance, ellipticity, and centroid parameters which are in agreement with other studies within uncertainties. Although our photometry of Indus II is $\sim 2-3$ magnitudes deeper than the DES Y1 Public release, we find no coherent stellar population at its reported location. The original detection was located in an incomplete region of sky in the DES Y2Q1 data set and was flagged due to potential blue horizontal branch member stars. The best fit isochrone parameters are physically inconsistent with both dwarf galaxies and globular clusters. We conclude that Indus II is likely a false-positive, flagged due to a chance alignment of stars along the line of sight., 16 pages, 8 figures; submitted to ApJ

DES Collaboration: et al., The DES-CMASS sample (DMASS) is designed to optimally combine the weak lensing measurements from the Dark Energy Survey (DES) and redshift-space distortions (RSD) probed by the CMASS galaxy sample from the Baryonic Oscillation Spectroscopic Survey. In this paper, we demonstrate the feasibility of adopting DMASS as the equivalent of CMASS for a joint analysis of DES and BOSS in the framework of modified gravity. We utilize the angular clustering of the DMASS galaxies, cosmic shear of the DES METACALIBRATION sources, and cross-correlation of the two as data vectors. By jointly fitting the combination of the data with the RSD measurements from the CMASS sample and Planck data, we obtain the constraints on modified gravity parameters μ0=−0.37+0.47−0.45 and Σ0=0.078+0.078−0.082⁠. Our constraints of modified gravity with DMASS are tighter than those with the DES Year 1 REDMAGIC sample with the same external data sets by 29 per cent for μ0 and 21 per cent for Σ0, and comparable to the published results of the DES Year 1 modified gravity analysis despite this work using fewer external data sets. This improvement is mainly because the galaxy bias parameter is shared and more tightly constrained by both CMASS and DMASS, effectively breaking the degeneracy between the galaxy bias and other cosmological parameters. Such an approach to optimally combine photometric and spectroscopic surveys using a photometric sample equivalent to a spectroscopic sample can be applied to combining future surveys having a limited overlap such as DESI and LSST., AC acknowledges support from NASA grant no. 15-WFIRST15-0008. During the preparation of this paper, C.H. was supported by the Simons Foundation, NASA, and the U.S. Department of Energy. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant no. 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science.

We forecast astrophysical and cosmological parameter constraints from synergies between 21 cm intensity mapping and wide field optical galaxy surveys (both spectroscopic and photometric) over $z \sim 0-3$. We focus on the following survey combinations in this work: (i) a CHIME-like and DESI-like survey in the northern hemisphere, (ii) an LSST-like and SKA I MID-like survey and (ii) a MeerKAT-like and DES-like survey in the southern hemisphere. We work with the $\Lambda$CDM cosmological model having parameters $\{h, \Omega_m, n_s, \Omega_b, \sigma_8\}$, parameters $v_{c,0}$ and $\beta$ representing the cutoff and slope of the HI-halo mass relation in the previously developed HI halo model framework, and a parameter $Q$ that represents the scale dependence of the optical galaxy bias. Using a Fisher forecasting framework, we explore (i) the effects of the HI and galaxy astrophysical uncertainties on the cosmological parameter constraints, assuming priors from the present knowledge of the astrophysics, (ii) the improvements on astrophysical constraints over their current priors in the three configurations considered, (ii) the tightening of the constraints on the parameters relative to the corresponding HI auto-correlation surveys alone., Comment: 9 pages, 5 figures, 2 tables; accepted for publication in MNRAS

We describe the observation and confirmation of nine new strong gravitational lenses discovered in Year 1 data from the Dark Energy Survey (DES). We created candidate lists based on (i) galaxy group and cluster samples, and (ii) photometrically selected galaxy samples. We selected 46 candidates through visual inspection and then used the Gemini Multi-Object Spectrograph (GMOS) at the Gemini South telescope to acquire a spectroscopic follow-up of 21 of these candidates. Through an analysis of these spectroscopic follow-up data, we confirmed nine new lensing systems and rejected two candidates, and the analysis was inconclusive on 10 candidates. For each of the confirmed systems, our report measured spectroscopic properties, estimated source image-lens separations, and estimated enclosed masses as well. The sources that we targeted have an i-band surface brightness range of i ∼ 22-24 mag arcsec and a spectroscopic redshift range of z ∼ 0.8−2.6. The lens galaxies have a photometric redshift range of z ∼ 0.3−0.7. The lensing systems range in source image-lens separation from 2 to 9 arcsec and in enclosed mass from 10 to 10 M, Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundac¸ão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência e Tecnologia, the Deutsche Forschungsge-meinschaft, and the Collaborating Institutions in the DES. The DES data management system is supported by the National Science Foundation under Grant Number AST-1138766. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2012-39559, ESP2013-48274, FPA2013-47986, and Centro de Excelencia Severo Ochoa SEV-2012-0234, some of which include ERDF funds from the European Union. This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

DES Collaboration: et al., Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. Monte Carlo control loops (MCCL) is a forward modeling method designed to tackle this problem. It relies on the ultra fast image generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. We present the first end-to-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum Cℓ and the redshift distribution n(z) of the background galaxy sample. The method includes maps of the systematic sources, point spread function (PSF), an approximate Bayesian computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and a fast method to estimate the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a nontomographic setting, we derive a set of Cℓ and n(z) curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of Ωm, σ8, and intrinsic alignment amplitude AIA, defined as S8DIA=σ8(Ωm/0.3)0.5DIA, where DIA=1−0.11(AIA−1). We find S8DIA=0.895+0.054−0.039, where systematics are at the level of roughly 60% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys., Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministerio da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DESBrazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the LudwigMaximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grants No, AST-1138766 and No. AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under Grants No. AYA2015-71825, No. ESP2015-66861, No. FPA2015-68048, No. SEV-2016-0588, No. SEV2016-0597, and No. MDM-2015-0509, some of which include ERDF funds from the European Union. I. F. A. E. is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC Grant agreements No. 240672, No. 291329, and No. 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant No. 465376/2014-2).

In any imaging survey, measuring accurately the astronomical background light is crucial to obtain good photometry. This paper introduces BKGNET, a deep neural network to predict the background and its associated error. BKGNET has been developed for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). The images obtained with PAUCam are affected by scattered light: an optical effect consisting of light multiply reflected that deposits energy in specific detector regions affecting the science measurements. Fortunately, scattered light is not a random effect, but it can be predicted and corrected for. We have found that BKGNET background predictions are very robust to distorting effects, while still being statistically accurate. On average, the use of BKGnet improves the photometric flux measurements by 7 per cent and up to 20 per cent at the bright end. BKGNET also removes a systematic trend in the background error estimation with magnitude in the i band that is present with the current PAU data management method. With BKGNET, we reduce the photometric redshift outlier rate by 35 per cent for the best 20 per cent galaxies selected with a photometric quality parameter., Funding for PAUS has been provided by Durham University (via the ERC StG DEGAS-259586), ETH Zurich, Leiden University (via ERC StG ADULT-279396 and Netherlands Organisation for Scientific Research (NWO) Vici grant 639.043.512) and University College London. The PAUS participants from Spanish institutions are partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2015-88861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IEEC and IFAE are partially funded by the CERCA program of the Generalitat de Catalunya. The PAU data center is hosted by the Port d’Informacio Cientifica (PIC), maintained through a collaboration of CIEMAT and IFAE, with additional support from Universitat Autonoma de Barcelona and ERDF. CosmoHub has been developed by PIC and was partially funded by the ‘Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion program of the Spanish government. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776247. AA is supported by a Royal Society Wolfson Fellowship. MS has been supported by the National Science Centre (grant UMO2016/23/N/ST9/02963).

Steinhardt et al., arXiv:2001.09999v2, The Beyond Ultra-deep Frontier Fields and Legacy Observations (BUFFALO) is a 101 orbit + 101 parallel Cycle 25 Hubble Space Telescope (HST) Treasury program taking data from 2018 to 2020. BUFFALO will expand existing coverage of the Hubble Frontier Fields (HFF) in Wide Field Camera 3/IR F105W, F125W, and F160W and Advanced Camera for Surveys/WFC F606W and F814W around each of the six HFF clusters and flanking fields. This additional area has not been observed by HST but is already covered by deep multiwavelength data sets, including Spitzer and Chandra. As with the original HFF program, BUFFALO is designed to take advantage of gravitational lensing from massive clusters to simultaneously find high-redshift galaxies that would otherwise lie below HST detection limits and model foreground clusters to study the properties of dark matter and galaxy assembly. The expanded area will provide the first opportunity to study both cosmic variance at high redshift and galaxy assembly in the outskirts of the large HFF clusters. Five additional orbits are reserved for transient follow-up. BUFFALO data including mosaics, value-added catalogs, and cluster mass distribution models will be released via MAST on a regular basis as the observations and analysis are completed for the six individual clusters., C.S. acknowledges support from the ERC Consolidator Grant funding scheme (project ConTExT, grant No. 648179). Y.M.B. acknowledges funding from the EU Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement 747645 (ClusterGal). J.M.D. acknowledges the support of project PGC2018-101814-B-100 (MCIU/AEI/MINECO/FEDER, UE), Ministerio de Ciencia, Investigación y Universidades.

The galaxy Luminosity Function (LF) is a key observable for galaxy formation, evolution studies and for cosmology. In this work, we propose a novel technique to forward model wide-field broad-band galaxy surveys using the fast image simulator UFig and measure the LF of galaxies in the B-band. We use Approximate Bayesian Computation (ABC) to constrain the galaxy population model parameters of the simulations and match data from CFHTLS. We define a number of distance metrics between the simulated and the survey data. By exploring the parameter space of the galaxy population model through ABC to find the set of parameters that minimize these distance metrics, we obtain constraints on the LFs of blue and red galaxies as a function of redshift. We find that $\mathrm{M^*}$ fades by $\Delta \mathrm{M}^*_{\mathrm{0.1-1.0,b}} = 0.68 \pm 0.52$ and $\Delta \mathrm{M}^*_{\mathrm{0.1-1.0,r}} = 0.54 \pm 0.48$ magnitudes between redshift $\mathrm{z = 1}$ and $\mathrm{z = 0.1}$ for blue and red galaxies, respectively. We also find that $\phi^*$ for blue galaxies stays roughly constant between redshift $\mathrm{z = 0.1}$ and $\mathrm{z=1}$, while for red galaxies it decreases by $\sim 35\%$. We compare our results to other measurements, finding good agreement at all redshifts, for both blue and red galaxies. To further test our results, we compare the redshift distributions for survey and simulated data. We use the spectroscopic redshift distribution from the VIMOS Public Extragalactic Redshift Survey (VIPERS) and we apply the same selection in colours and magnitudes on our simulations. We find a good agreement between the survey and the simulated redshift distributions. We provide best-fit values and uncertainties for the parameters of the LF. This work offers excellent prospects for measuring other galaxy population properties as a function of redshift using ABC., Comment: Accepted for publication in JCAP, 44 pages, 20 figures, 4 appendices

For the next generation of spectroscopic galaxy surveys, it is important to forecast their performances and to accurately interpret their large data sets. For this purpose, it is necessary to consistently simulate different populations of galaxies, in particular Emission Line Galaxies (ELGs), less used in the past for cosmological purposes. In this work, we further the forward modeling approach presented in Fagioli et al. 2018, by extending the spectra simulator Uspec to model galaxies of different kinds with improved parameters from Tortorelli et al. 2020. Furthermore, we improve the modeling of the selection function by using the image simulator Ufig. We apply this to the Sloan Digital Sky Survey (SDSS), and simulate $\sim157,000$ multi-band images. We pre-process and analyse them to apply cuts for target selection, and finally simulate SDSS/BOSS DR14 galaxy spectra. We compute photometric, astrometric and spectroscopic properties for red and blue, real and simulated galaxies, finding very good agreement. We compare the statistical properties of the samples by decomposing them with Principal Component Analysis (PCA). We find very good agreement for red galaxies and a good, but less pronounced one, for blue galaxies, as expected given the known difficulty of simulating those. Finally, we derive stellar population properties, mass-to-light ratios, ages and metallicities, for all samples, finding again very good agreement. This shows how this method can be used not only to forecast cosmology surveys, but it is also able to provide insights into studies of galaxy formation and evolution., Comment: 28 pages, 10 figures, accepted for publication in JCAP

Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow band photometry, provided by the PAUS (Physics of the Accelerating Universe) survey. We find that, with the photometric fluxes from the 40 narrow band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, 98.4% purity with a completeness of 98.8% for objects brighter than I = 22.5. This precision is obtained with a Convolutional Neural Network as a classification algorithm, applied to the objects' spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample., 13 pages, 10 figures, the catalog with the ALHAMBRA classification is available at http://cosmohub.pic.es

Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density $\Omega_m$, the fluctuation amplitude $\sigma_8$, and the intrinsic alignment amplitude $A_{\rm{IA}}$. We use a grid of N-body simulations to generate a training set of tomographic weak lensing maps. We test the robustness of the expected constraints to various effects, such as baryonic feedback, simulation accuracy, different value of $H_0$, or the lightcone projection technique. We train a set of ResNet-based CNNs with varying depths to analyze sets of tomographic KiDS mass maps divided into 20 flat regions, with applied Gaussian smoothing of $\sigma=2.34$ arcmin. The uncertainties on shear calibration and $n(z)$ error are marginalized in the likelihood pipeline. Following a blinding scheme, we derive constraints of $S_8 = \sigma_8 (\Omega_m/0.3)^{0.5} = 0.777^{+0.038}_{-0.036}$ with our CNN analysis, with $A_{\rm{IA}}=1.398^{+0.779}_{-0.724}$. We compare this result to the power spectrum analysis on the same maps and likelihood pipeline and find an improvement of about $30\%$ for the CNN. We discuss how our results offer excellent prospects for the use of deep learning in future cosmological data analysis., Comment: 22 pages, 15 figures

Feedback processes from baryons are expected to strongly affect weak-lensing observables of current and future cosmological surveys. In this paper we present a new parametrisation of halo profiles based on gas, stellar, and dark matter density components. This parametrisation is used to modify outputs of gravity-only $N$-body simulations (following the prescription of Schneider and Teyssier [1]) in order to mimic baryonic effects on the matter density field. The resulting baryonic correction model relies on a few well motivated physical parameters and is able to reproduce the redshift zero clustering signal of hydrodynamical simulations at two percent accuracy below $k\sim10$ h/Mpc. A detailed study of the baryon suppression effects on the matter power spectrum and the weak lensing shear correlation reveals that the signal is dominated by two parameters describing the slope of the gas profile in haloes and the maximum radius of gas ejection. We show that these parameters can be constrained with the observed gas fraction of galaxy groups and clusters from X-ray data. Based on these observations we predict a beyond percent effect on the power spectrum above $k=0.2-1.0$ h/Mpc with a maximum suppression of 15-25 percent around $k\sim 10$ h/Mpc. As a result, the weak lensing angular shear power spectrum is suppressed by 15-25 percent at scales beyond $\ell\sim 100-600$ and the shear correlations $\xi_{+}$ and $\xi_{-}$ are affected at the 10-25 percent level below 5 and 50 arc-minutes, respectively. The relatively large uncertainties of these predictions are a result of the poorly known hydrostatic mass bias of current X-ray observations as well as the generic difficulty to observe the low density gas outside of haloes., Comment: Corrected typo in labelling of Fig 3, 13, and 14

The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and PSF subtraction algorithms are equally relevant for maximizing the scientific yield, both from new and archival data sets. We aim at developing a generic and modular data reduction pipeline for processing and analysis of high-contrast imaging data obtained with pupil-stabilized observations. The package should be scalable and robust for future implementations and in particular well suitable for the 3-5 micron wavelength range where typically (ten) thousands of frames have to be processed and an accurate subtraction of the thermal background emission is critical. PynPoint is written in Python 2.7 and applies various image processing techniques, as well as statistical tools for analyzing the data, building on open-source Python packages. The current version of PynPoint has evolved from an earlier version that was developed as a PSF subtraction tool based on PCA. The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules. Modules have been implemented for dedicated processing and analysis steps, including background subtraction, frame registration, PSF subtraction, photometric and astrometric measurements, and estimation of detection limits. The pipeline package enables end-to-end data reduction of pupil-stabilized data and supports classical dithering and coronagraphic data sets. As an example, we processed archival VLT/NACO L' and M' data of beta Pic b and reassessed the planet's brightness and position with an MCMC analysis, and we provide a derivation of the photometric error budget., 16 pages, 9 figures, accepted for publication in A&A, PynPoint is available at https://github.com/PynPoint/PynPoint

We present a combined cosmic shear analysis of the modeling of line-of-sight distortions on strongly lensed extended arcs and galaxy shape measurements in the COSMOS field. We develop a framework to predict the covariance of strong lensing and galaxy shape measurements of cosmic shear on the basis of the small scale matter power-spectrum. The weak lensing measurement is performed using data from the COSMOS survey calibrated with a cloning scheme using the Ultra Fast Image Generator UFig (Berge 2013). The strong lensing analysis is performed by forward modeling the lensing arcs with a main lensing deflector and external shear components from the same Hubble Space Telescope imaging data set. With a sample of three strong lensing shear measurements we present a 2-sigma detection of the cross-correlation signal between the two complementary measurements of cosmic shear along the identical line of sight. With large samples of lenses available with the next generation ground and space based observatories, the covariance of the signal of the two probes with large samples of lenses allows for systematic checks, cross-calibration of either of the two measurement and the measurement of the small scale shear power-spectrum., 27 pages, 7 figures, 4 tables

We present a new method to measure the redshift-dependent galaxy bias by combining information from the galaxy density field and the weak lensing field. This method is based on Amara et al. (2012), where they use the galaxy density field to construct a bias-weighted convergence field kg. The main difference between Amara et al. (2012) and our new implementation is that here we present another way to measure galaxy bias using tomography instead of bias parameterizations. The correlation between kg and the true lensing field k allows us to measure galaxy bias using different zero-lag correlations, such as / or /. Our method measures the linear bias factor on linear scales under the assumption of no stochasticity between galaxies and matter. We use the MICE simulation to measure the linear galaxy bias for a flux-limited sample (i < 22.5) in tomographic redshift bins using this method. This paper is the first that studies the accuracy and systematic uncertainties associated with the implementation of the method, and the regime where it is consistent with the linear galaxy bias defined by projected 2-point correlation functions (2PCF). We find that our method is consistent with linear bias at the percent level for scales larger than 30 arcmin, while nonlinearities appear at smaller scales. This measurement is a good complement to other measurements of bias, since it does not depend strongly on sigma8 as the 2PCF measurements. We apply this method to the Dark Energy Survey Science Verification data in a follow-up paper., Comment: 15 pages, 9 figures

The nature of dark matter, dark energy and large-scale gravity pose some of the most pressing questions in cosmology today. These fundamental questions require highly precise measurements, and a number of wide-field spectroscopic survey instruments are being designed to meet this requirement. A key component in these experiments is the development of a simulation tool to forecast science performance, define requirement flow-downs, optimize implementation, demonstrate feasibility, and prepare for exploitation. We present SPOKES (SPectrOscopic KEn Simulation), an end-to-end simulation facility for spectroscopic cosmological surveys designed to address this challenge. SPOKES is based on an integrated infrastructure, modular function organization, coherent data handling and fast data access. These key features allow reproducibility of pipeline runs, enable ease of use and provide flexibility to update functions within the pipeline. The cyclic nature of the pipeline offers the possibility to make the science output an efficient measure for design optimization and feasibility testing. We present the architecture, first science, and computational performance results of the simulation pipeline. The framework is general, but for the benchmark tests, we use the Dark Energy Spectrometer (DESpec), one of the early concepts for the upcoming project, the Dark Energy Spectroscopic Instrument (DESI). We discuss how the SPOKES framework enables a rigorous process to optimize and exploit spectroscopic survey experiments in order to derive high-precision cosmological measurements optimally.

We study the distribution of dark matter in the nonlinear regime in a model in which the primordial fluctuations include, in addition to the dominant primordial Gaussian fluctuations generated by the standard $\Lambda CDM$ cosmological model, the effects of a cosmic string wake set up at the time of equal matter and radiation, making use of cosmological $N$-body simulations. At early times the string wake leads to a planar overdensity of dark matter. We study how this non-Gaussian pattern of a cosmic string wake evolves in the presence of the Gaussian perturbations, making use of wavelet and ridgelet-like statistics specifically designed to extract string wake signals. At late times the Gaussian fluctuations disrupt the string wake. We find that for a string tension of $G \mu = 10^{-7}$, a value just below the current observational limit, the effects of a string wake can be identified in the dark matter distribution, using the current level of the statistical analysis, down to a redshift of $z = 10$., Comment: 18 pages, 15 figures

With the high-precision data from current and upcoming experiments, it becomes increasingly important to perform consistency tests of the standard cosmological model. In this work, we focus on consistency measures between different data sets and methods that allow us to assess the goodness of fit of different models. We address both of these questions using the relative entropy or Kullback-Leibler (KL) divergence [Kullback et al., 1951]. First, we revisit the relative entropy as a consistency measure between data sets and further investigate some of its key properties, such as asymmetry and path dependence. We then introduce a novel model rejection framework, which is based on the relative entropy and the posterior predictive distribution. We validate the method on several toy models and apply it to Type Ia supernovae data from the JLA and CMB constraints from Planck 2015, testing the consistency of the data with six different cosmological models., 31 pages, 10 figures, 4 tables, updated following referee's comments, matches version accepted by JCAP

Weak Gravitational Lensing is a powerful probe of the dark sector of the Universe. One of the main challenges for this technique is the treatment of systematics in the measurement of cosmic shear from galaxy shapes. In an earlier work, Refregier & Amara (2014) have proposed the Monte Carlo Control Loops (MCCL) to overcome these effects using a forward modeling approach. We focus here on one of the control loops in this method, the task of which is the calibration of the shear measurement. For this purpose, we first consider the requirements on the shear systematics for a given survey and propagate them to different systematics terms. We use two one-point statistics to calibrate the shear measurement and six further one-point statistics as diagnostics. We also propagate the systematics levels that we estimate from the one-point functions to the two-point functions for the different systematic error sources. This allows us to assess the consistency between the systematics levels measured in different ways. To test the method, we construct synthetic sky surveys with an area of 1,700 deg$^2$. With some simplifying assumptions, we are able to meet the requirements on the shear calibration for this survey configuration. Furthermore, we account for the total residual shear systematics in terms of the contributing sources. We discuss how this MCCL framework can be applied to current and future weak lensing surveys., 23 pages, 4 figures, prepared for submission to JCAP

We present Lenstronomy, a multi-purpose open-source gravitational lens modeling python package. Lenstronomy is able to reconstruct the lens mass and surface brightness distributions of strong lensing systems using forward modelling. Lenstronomy supports a wide range of analytic lens and light models in arbitrary combination. The software is also able to reconstruct complex extended sources (Birrer et. al 2015) as well as being able to model point sources. We designed Lenstronomy to be stable, flexible and numerically accurate, with a clear user interface that could be deployed across different platforms. Throughout its development, we have actively used Lenstronomy to make several measurements including deriving constraints on dark matter properties in strong lenses, measuring the expansion history of the universe with time-delay cosmography, measuring cosmic shear with Einstein rings and decomposing quasar and host galaxy light. The software is distributed under the MIT license. The documentation, starter guide, example notebooks, source code and installation guidelines can be found at https://lenstronomy.readthedocs.io., software and documentation see https://lenstronomy.readthedocs.io, matches accepted version of Physics of the Dark Universe

Ground-based observations at thermal infrared wavelengths suffer from large background radiation due to the sky, telescope and warm surfaces in the instrument. This significantly limits the sensitivity of ground-based observations at wavelengths longer than 3 microns. We analyzed this background emission in infrared high contrast imaging data, show how it can be modelled and subtracted and demonstrate that it can improve the detection of faint sources, such as exoplanets. We applied principal component analysis to model and subtract the thermal background emission in three archival high contrast angular differential imaging datasets in the M and L filter. We describe how the algorithm works and explain how it can be applied. The results of the background subtraction are compared to the results from a conventional mean background subtraction scheme. Finally, both methods for background subtraction are also compared by performing complete data reductions. We analyze the results from the M dataset of HD100546 qualitatively. For the M band dataset of beta Pic and the L band dataset of HD169142, which was obtained with an annular groove phase mask vortex vector coronagraph, we also calculate and analyze the achieved signal to noise (S/N). We show that applying PCA is an effective way to remove spatially and temporarily varying thermal background emission down to close to the background limit. The procedure also proves to be very successful at reconstructing the background that is hidden behind the PSF. In the complete data reductions, we find at least qualitative improvements for HD100546 and HD169142, however, we fail to find a significant increase in S/N of beta Pic b. We discuss these findings and argue that in particular datasets with strongly varying observing conditions or infrequently sampled sky background will benefit from the new approach., 12 pages, 17 figures, 1 table, Accepted for publication in A&A

We describe the first public data release of the Dark Energy Survey, DES DR1, consisting of reduced single epoch images, coadded images, coadded source catalogs, and associated products and services assembled over the first three years of DES science operations. DES DR1 is based on optical/near-infrared imaging from 345 distinct nights (August 2013 to February 2016) by the Dark Energy Camera mounted on the 4-m Blanco telescope at Cerro Tololo Inter-American Observatory in Chile. We release data from the DES wide-area survey covering ~5,000 sq. deg. of the southern Galactic cap in five broad photometric bands, grizY. DES DR1 has a median delivered point-spread function of g = 1.12, r = 0.96, i = 0.88, z = 0.84, and Y = 0.90 arcsec FWHM, a photometric precision of < 1% in all bands, and an astrometric precision of 151 mas. The median coadded catalog depth for a 1.95" diameter aperture at S/N = 10 is g = 24.33, r = 24.08, i = 23.44, z = 22.69, and Y = 21.44 mag. DES DR1 includes nearly 400M distinct astronomical objects detected in ~10,000 coadd tiles of size 0.534 sq. deg. produced from ~39,000 individual exposures. Benchmark galaxy and stellar samples contain ~310M and ~ 80M objects, respectively, following a basic object quality selection. These data are accessible through a range of interfaces, including query web clients, image cutout servers, jupyter notebooks, and an interactive coadd image visualization tool. DES DR1 constitutes the largest photometric data set to date at the achieved depth and photometric precision., 30 pages, 20 Figures. Release page found at this url https://des.ncsa.illinois.edu/releases/dr1

Modeling the Point Spread Function (PSF) of wide-field surveys is vital for many astrophysical applications and cosmological probes including weak gravitational lensing. The PSF smears the image of any recorded object and therefore needs to be taken into account when inferring properties of galaxies from astronomical images. In the case of cosmic shear, the PSF is one of the dominant sources of systematic errors and must be treated carefully to avoid biases in cosmological parameters. Recently, forward modeling approaches to calibrate shear measurements within the Monte-Carlo Control Loops ($MCCL$) framework have been developed. These methods typically require simulating a large amount of wide-field images, thus, the simulations need to be very fast yet have realistic properties in key features such as the PSF pattern. Hence, such forward modeling approaches require a very flexible PSF model, which is quick to evaluate and whose parameters can be estimated reliably from survey data. We present a PSF model that meets these requirements based on a fast deep-learning method to estimate its free parameters. We demonstrate our approach on publicly available SDSS data. We extract the most important features of the SDSS sample via principal component analysis. Next, we construct our model based on perturbations of a fixed base profile, ensuring that it captures these features. We then train a Convolutional Neural Network to estimate the free parameters of the model from noisy images of the PSF. This allows us to render a model image of each star, which we compare to the SDSS stars to evaluate the performance of our method. We find that our approach is able to accurately reproduce the SDSS PSF at the pixel level, which, due to the speed of both the model evaluation and the parameter estimation, offers good prospects for incorporating our method into the $MCCL$ framework., 25 pages, 8 figures, 1 table

Upcoming weak lensing surveys will probe large fractions of the sky with unprecedented accuracy. To infer cosmological constraints, a large ensemble of survey simulations are required to accurately model cosmological observables and their covariances. We develop a parallelized multi-lens-plane pipeline called UFalcon, designed to generate full-sky weak lensing maps from lightcones within a minimal runtime. It makes use of L-PICOLA, an approximate numerical code, which provides a fast and accurate alternative to cosmological $N$-Body simulations. The UFalcon maps are constructed by nesting 2 simulations covering a redshift-range from $z=0.1$ to $1.5$ without replicating the simulation volume. We compute the convergence and projected overdensity maps for L-PICOLA in the lightcone or snapshot mode. The generation of such a map, including the L-PICOLA simulation, takes about 3 hours walltime on 220 cores. We use the maps to calculate the spherical harmonic power spectra, which we compare to theoretical predictions and to UFalcon results generated using the full $N$-Body code GADGET-2. We then compute the covariance matrix of the full-sky spherical harmonic power spectra using 150 UFalcon maps based on L-PICOLA in lightcone mode. We consider the PDF, the higher-order moments and the variance of the smoothed field variance to quantify the accuracy of the covariance matrix, which we find to be a few percent for scales $\ell \sim 10^2$ to $10^3$. We test the impact of this level of accuracy on cosmological constraints using an optimistic survey configuration, and find that the final results are robust to this level of uncertainty. The speed and accuracy of our developed pipeline provides a basis to also include further important features such as masking, varying noise and will allow us to compute covariance matrices for models beyond $\Lambda$CDM. [abridged], Comment: 20 pages, 7 figures

Weak gravitational lensing is a powerful probe of the dark sector, once measurement systematic errors can be controlled. In Refregier & Amara (2014), a calibration method based on forward modeling, called MCCL, was proposed. This relies on fast image simulations (e.g., UFig; Berge et al. 2013) that capture the key features of galaxy populations and measurement effects. The MCCL approach has been used in Herbel et al. (2017) to determine the redshift distribution of cosmological galaxy samples and, in the process, the authors derived a model for the galaxy population mainly based on broad-band photometry. Here, we test this model by forward modeling the 40 narrow-band photometry given by the novel PAU Survey (PAUS). For this purpose, we apply the same forced photometric pipeline on data and simulations using Source Extractor (Bertin & Arnouts 1996). The image simulation scheme performance is assessed at the image and at the catalogues level. We find good agreement for the distribution of pixel values, the magnitudes, in the magnitude-size relation and the interband correlations. A principal component analysis is then performed, in order to derive a global comparison of the narrow-band photometry between the data and the simulations. We use a `mixing' matrix to quantify the agreement between the observed and simulated sets of Principal Components (PCs). We find good agreement, especially for the first three most significant PCs. We also compare the coefficients of the PCs decomposition. While there are slight differences for some coefficients, we find that the distributions are in good agreement. Together, our results show that the galaxy population model derived from broad-band photometry is in good overall agreement with the PAUS data. This offers good prospect for incorporating spectral information to the galaxy model by adjusting it to the PAUS narrow-band data using forward modeling., Submitted to JCAP, 28 pages, 15 figures, 3 appendices