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1. QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

Author

Priyamvada Natarajan, Kwok Sun Tang, Robert McGibbon, Sadegh Khochfar, Brian Nord, Steinn Sigurdsson, Joe Tricot, Nico Cappelluti, Daniel George, and Jack Hidary

Subjects
Quasars, Astrophysics, QB460-466
Abstract

We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z 3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at: https://www.kaggle.com/datasets/quotasplatform/quotas )

Published
2023
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2. A robust estimator of mutual information for deep learning interpretability

Author

Davide Piras, Hiranya V Peiris, Andrew Pontzen, Luisa Lucie-Smith, Ningyuan Guo, and Brian Nord

Subjects
deep learning, mutual information, interpretability, representation learning, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95
Abstract

We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository.

Published
2023
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3. Ten simple rules to cultivate transdisciplinary collaboration in data science.

Author

Faryad Sahneh, Meghan A Balk, Marina Kisley, Chi-Kwan Chan, Mercury Fox, Brian Nord, Eric Lyons, Tyson Swetnam, Daniela Huppenkothen, Will Sutherland, Ramona L Walls, Daven P Quinn, Tonantzin Tarin, David LeBauer, David Ribes, Dunbar P Birnie, Carol Lushbough, Eric Carr, Grey Nearing, Jeremy Fischer, Kevin Tyle, Luis Carrasco, Meagan Lang, Peter W Rose, Richard R Rushforth, Samapriya Roy, Thomas Matheson, Tina Lee, C Titus Brown, Tracy K Teal, Monica Papeș, Stephen Kobourov, and Nirav Merchant

Subjects
Biology (General), QH301-705.5
Published
2021
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6. Flare Statistics for Young Stars from a Convolutional Neural Network Analysis of TESS Data

Author

Adina D. Feinstein, Benjamin T. Montet, Megan Ansdell, Brian Nord, Jacob L. Bean, Maximilian N. Günther, Michael A. Gully-Santiago, and Joshua E. Schlieder

Subjects
Astronomy, Astrophysics
Abstract

All-sky photometric time-series missions have allowed for the monitoring of thousands of young (t(age) < 800 Myr) stars in order to understand the evolution of stellar activity. Here, we developed a convolutional neural network (CNN), stella, specifically trained to find flares in Transiting Exoplanet Survey Satellite (TESS) short-cadence data. We applied the network to 3200 young stars in order to evaluate flare rates as a function of age and spectral type. The CNN takes a few seconds to identify flares on a single light curve. We also measured rotation periods for 1500 of our targets and find that flares of all amplitudes are present across all spot phases, suggesting high spot coverage across the entire surface. Additionally, flare rates and amplitudes decrease for stars t(age) > 50 Myr across all temperatures T(eff) ≥ 4000 K, while stars from 2300 ≤ T(eff) < 4000 K show no evolution across 800 Myr. Stars of T(eff) ≤ 4000 K also show higher flare rates and amplitudes across all ages. We investigate the effects of high flare rates on photoevaporative atmospheric mass loss for young planets. In the presence of flares, planets lose 4%–7% more atmosphere over the first 1 Gyr. stella is an open-source Python toolkit hosted on GitHub and PyPI.

Published
2020
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9. Noise2Astro: Astronomical Image Denoising With Self-Supervised NeuralNetworks

Author

Amanda Pagul, Brian Nord, Michael Lepori, and Yunchong Zhang

Subjects
FOS: Physical sciences, General Medicine, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM)
Abstract

In observational astronomy, noise obscures signals of interest. Large-scale astronomical surveys are growing in size and complexity, which will produce more data and increase the workload of data processing. Developing automated tools, such as convolutional neural networks (CNN), for denoising has become a promising area of research. We investigate the feasibility of CNN-based self-supervised learning algorithms (e.g., Noise2Noise) for denoising astronomical images. We experimented with Noise2Noise on simulated noisy astronomical data. We evaluate the results based on the accuracy of recovering flux and morphology. This algorithm can well recover the flux for Poisson noise (98.13 − 0.90 + 0.77 % ) and for Gaussian noise when image data has a smooth signal profile (96.45 − 0.96 + 0.80 % ).

Published
2022
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10. DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation

Author

Gourav Khullar, Brian Nord, Aleksandra Ćiprijanović, Jason Poh, and Fei Xu

Subjects
Human-Computer Interaction, Artificial Intelligence, Astrophysics of Galaxies (astro-ph.GA), FOS: Physical sciences, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Instrumentation and Methods for Astrophysics (astro-ph.IM), Software
Abstract

With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference (SBI) and amortized Neural Posterior Estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with MCMC methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2$\times$10$^6$ spectra (corresponding to a 5-parameter SED model) with NPE. We show that SBI -- with its combination of fast and amortized posterior estimations -- is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modeling with Bayesian MCMC methods (e.g., 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9-1.2$\times$10$^5$ galaxy spectra via SBI/SNPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys., Comment: Manuscript accepted in Machine Learning: Science and Technology (MLST) as a Letter (October 10th, 2022); 12 Pages, 6 Figures and 1 Table; Data and code can be found in published github repository

Published
2022
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11. DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification

Author

Aleksandra Ćiprijanović, Diana Kafkes, Gregory Snyder, F Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, and Stefan M Wild

Subjects
Human-Computer Interaction, FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Artificial Intelligence, Computer Science - Artificial Intelligence, Astrophysics of Galaxies (astro-ph.GA), Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, FOS: Physical sciences, Astrophysics - Astrophysics of Galaxies, Software, Machine Learning (cs.LG)
Abstract

With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: 1) increased observational noise as represented by higher levels of Poisson noise and 2) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness. Without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy by 23% on data with higher observational noise. Domain adaptation also increases by a factor of ~2.3 the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations., 20 pages, 6 figures, 5 tables; accepted in MLST

Published
2021

12. SkyPy: A package for modelling the Universe

Author

Sut-Ieng Tam, Keiichi Umetsu, Ian Harrison, Coleman Krawczyk, L. Nuttall, A. E. Tolley, Nicolas Tessore, Philipp Sudek, Richard P. Rollins, A. R. Williamson, Ginevra Favole, Brian Nord, Lucia F. de la Bella, Andrew Lundgren, J. Cordero, Sarah Bridle, Simon Birrer, Adam Amara, I. W. Harry, Laura Wolz, and William G. Hartley

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), Computer science, Dark matter, Cosmic microwave background, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Cosmology, Computational science, Software, 0103 physical sciences, 010303 astronomy & astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Astrophysics::Galaxy Astrophysics, computer.programming_language, 010308 nuclear & particles physics, business.industry, YAML, computer.file_format, Modular design, Python (programming language), Probability distribution, business, Astrophysics - Instrumentation and Methods for Astrophysics, computer, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

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

Published
2021

13. Ten simple rules to cultivate transdisciplinary collaboration in data science

Author

Dunbar P. Birnie, Tracy K. Teal, Ramona Walls, Eric Lyons, C. Titus Brown, Carol Lushbough, Samapriya Roy, Daven P. Quinn, David Ribes, Meagan Lang, David Shaner LeBauer, Stephen G. Kobourov, Daniela Huppenkothen, Mercury Fox, Grey Nearing, Jeremy Fischer, Will Sutherland, Peter W. Rose, Thomas Matheson, Richard Rushforth, Tyson L. Swetnam, Kevin R. Tyle, Tonantzin Tarin, Brian Nord, Chi-kwan Chan, Meghan A. Balk, Monica Papeş, Tina Lee, Eric A. Carr, Nirav Merchant, Luis Carrasco, Marina Kisley, and Faryad Darabi Sahneh

Subjects
Science and Technology Workforce, Computer and Information Sciences, Science Policy, Computer science, QH301-705.5, MEDLINE, Social Sciences, Careers in Research, Research and Analysis Methods, Research Ethics, Education, Computer Software, Human Learning, Cellular and Molecular Neuroscience, Learning and Memory, Sociology, Simple (abstract algebra), Genetics, Learning, Psychology, Biology (General), Molecular Biology, Research Integrity, Ecology, Evolution, Behavior and Systematics, Data Management, Ecology, Cognitive Psychology, Biology and Life Sciences, Software Engineering, Research Assessment, Data science, Reproducibility, Professions, Editorial, Computational Theory and Mathematics, Research Design, Modeling and Simulation, People and Places, Scientists, Cognitive Science, Engineering and Technology, Population Groupings, Workshops, Neuroscience
Published
2021

15. DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains

Author

Sandeep Madireddy, Gregory F. Snyder, K. Downey, A. Ćiprijanović, Brian Nord, Sydney Jenkins, Gabriel Perdue, Diana Kafkes, and Travis Johnston

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, FOS: Physical sciences, Context (language use), 02 engineering and technology, 01 natural sciences, Domain (software engineering), Machine Learning (cs.LG), 0103 physical sciences, Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, 010303 astronomy & astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Physics, Artificial neural network, business.industry, Deep learning, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, Identification (information), Artificial Intelligence (cs.AI), Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), 020201 artificial intelligence & image processing, Artificial intelligence, business, Astrophysics - Instrumentation and Methods for Astrophysics, Algorithm
Abstract

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques$-$ Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)$-$ and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ${\sim}20\%$. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys., Submitted to MNRAS; 21 pages, 9 figures, 9 tables

Published
2021

16. DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning

Author

João Caldeira, Camille Avestruz, Shubhendu Trivedi, W. L. K. Wu, Z. Lin, Brian Nord, and N. Huang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Cosmology and Nongalactic Astrophysics (astro-ph.CO), media_common.quotation_subject, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, FOS: Physical sciences, 01 natural sciences, Convolutional neural network, Machine Learning (cs.LG), Completeness (order theory), 0103 physical sciences, Cluster (physics), 010303 astronomy & astrophysics, Galaxy cluster, media_common, Physics, 010308 nuclear & particles physics, business.industry, Matched filter, Astronomy and Astrophysics, Pattern recognition, Redshift, Space and Planetary Science, Sky, Noise (video), Artificial intelligence, business, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Galaxy clusters identified from the Sunyaev Zel'dovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN). We further implement and show results for a `combined' identifier. We apply the methods to simulated millimeter maps for several observing frequencies for an SPT-3G-like survey. There are some key differences between the methods. The MF method requires image pre-processing to remove point sources and a model for the noise, while the CNN method requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes as input, cutout images of the sky. Specifically, we use the CNN to classify whether or not an 8 arcmin $\times$ 8 arcmin cutout of the sky contains a cluster. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some of which have SNR below the MF detection threshold. However, the CNN tends to mis-classify cutouts whose clusters are located near the edge of the cutout, which can be mitigated with staggered cutouts. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness of the MF alone are both 0.61, assuming a standard detection threshold. The purity and completeness of the CNN alone are 0.59 and 0.61. The combined classification method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many lower signal-to-noise clusters.

Published
2021
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17. A Machine Learning Approach to the Detection of Ghosting and Scattered Light Artifacts in Dark Energy Survey Images

Author

Chihway Chang, Donah Michelle Wang, Alex Drlica-Wagner, Michael H. L. S. Wang, Brian Nord, and Stephen M. Kent

Subjects
Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, FOS: Physical sciences, Machine learning, computer.software_genre, 01 natural sciences, Convolutional neural network, Light scattering, law.invention, Telescope, Observatory, law, 0103 physical sciences, Image sensor, Ghosting, 010303 astronomy & astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), 010308 nuclear & particles physics, business.industry, Astronomy and Astrophysics, Computer Science Applications, Space and Planetary Science, Proof of concept, Dark energy, Artificial intelligence, business, Astrophysics - Instrumentation and Methods for Astrophysics, computer
Abstract

Astronomical images are often plagued by unwanted artifacts that arise from a number of sources including imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and artificial satellites. Spurious reflections (known as "ghosts") and the scattering of light off the surfaces of a camera and/or telescope are particularly difficult to avoid. Detecting ghosts and scattered light efficiently in large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts and scattered light using convolutional neural networks. The model architecture and training procedure is discussed in detail, and the performance on the training and validation set is presented. Testing is performed on data and results are compared with those from a ray-tracing algorithm. As a proof of principle, we have shown that our method is promising for the Rubin Observatory and beyond., Comment: accepted for publication in "Astronomy and Computing"

Published
2021
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18. Domain Adaptation for Cross-Domain Studies of Merging Galaxies

Author

Diana Kafkes, Aleksandra Miodrag Ćiprijanović, Sydney Jenkins, Gabriel Perdue, Brian Nord, K. Downey, Travis Johnston, and Sandeep Madireddy

Subjects
Domain adaptation, Computer science, Galaxy merger, Algorithm, Domain (software engineering)
Published
2021

21. Strong Gravitational Lensing

Author

Martin Makler, Brian Nord, Adam Amara, and E. Buckley-Geer

Subjects
Physics, Strong gravitational lensing, Astrophysics
Published
2020

22. Observation and confirmation of nine strong-lensing systems in Dark Energy Survey Year 1 data

Author

M. Smith, Daniel Gruen, V. Scarpine, A. K. Romer, Felipe Menanteau, M. E. C. Swanson, Eli S. Rykoff, E. Suchyta, Enrique Gaztanaga, August E. Evrard, J. Carretero, Marco A. P. Lima, Juan Garcia-Bellido, Brian Nord, Michael Schubnell, Adriano Agnello, C. Davis, Keith Bechtol, Flavia Sobreira, Santiago Avila, M. Carrasco Kind, A. Carnero Rosell, I. Sevilla-Noarbe, Carlos E. Cunha, Yanxi Zhang, Douglas L. Tucker, Kyler Kuehn, Tim Eifler, G. Gutierrez, G. Tarle, A. A. Plazas, N. Kuropatkin, Enrique Fernández, L. N. da Costa, Huan Lin, Josh Frieman, M. March, K. Honscheid, David J. James, David J. Brooks, R. H. Schindler, Pablo Fosalba, T. M. C. Abbott, D. L. Burke, Ofer Lahav, Peter Doel, E. J. Sanchez, S. Allam, Robert A. Gruendl, Marcelle Soares-Santos, H. T. Diehl, J. Annis, D. L. Hollowood, J. De Vicente, W. G. Hartley, Jennifer L. Marshall, Daniel Thomas, E. Buckley-Geer, Adam Amara, Ramon Miquel, Thomas E. Collett, A. Roodman, B. Flaugher, Ben Hoyle, M. A. G. Maia, Peter Melchior, Department of Energy (US), National Science Foundation (US), Ministerio de Ciencia, Innovación y Universidades (España), Science and Technology Facilities Council (UK), University of Illinois, Kavli Institute for Theoretical Physics, University of Chicago, The Ohio State University, Texas A&M University, Financiadora de Estudos e Projetos (Brasil), Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brasil), Ministério da Ciência, Tecnologia e Inovação (Brasil), and German Research Foundation

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), Higher education, Gravitational lensing: strong, DES [surveys], astro-ph.GA, Library science, Techniques: spectroscopic, FOS: Physical sciences, clusters [gravitational lensing], Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, survey [gravitational lensing], Galaxies: individual, 0103 physical sciences, media_common.cataloged_instance, Astrophysics::Solar and Stellar Astrophysics, European union, general [gravitational lenses], 010303 astronomy & astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Astrophysics::Galaxy Astrophysics, STFC, media_common, Physics, 010308 nuclear & particles physics, business.industry, RCUK, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, gravitational lensing individual, Alliance, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), strong [gravitational lensing], Fundamental physics, astro-ph.CO, Christian ministry, business, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, astro-ph.IM
Abstract

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.

Published
2020

23. HEP ML/Optimization Go Quantum – QuantISED Pilot

Author

James B. Kowalkowski, João Caldeira, Steven H. Adachi, Evan Peters, Stephen Mrenna, A. Tsaris, Brian Nord, Joshua Job, Travis S. Humble, Kathleen E. Hamilton, Stefan Leichenauer, Alexander McCaskey, Gabriel Perdue, and Andrea Delgado

Subjects
Physics, Quantum mechanics, Quantum
Published
2020

24. A Machine Learning Approach to the Detection of Ghosting Artifacts in Dark Energy Survey Images

Author

Michael H. L. S. Wang, Brian Nord, Wang, Donah, Michelle, Chihway Chang, and Alex Drlica-Wagner

Subjects
Computer science, business.industry, Petabyte, Machine learning, computer.software_genre, Convolutional neural network, Task (project management), law.invention, Telescope, Set (abstract data type), law, Dark energy, Artificial intelligence, Image sensor, Ghosting, business, computer
Abstract

Unwanted artifacts that often plague astronomical images arise from a number of sources that include imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and satellites. One major source that is dicult to avoid is known as ghosting, and is caused by the scattering and internal reflections of light o of the telescope’s mechanical and optical components. Detecting ghosting artifacts eciently in the large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts based on convolutional neural networks. The model architecture and training procedure is discussed in detail, and the performance on the training and validation set presented. Testing is performed on data and results are compared with those from a ray-tracing algorithm. A proof-of-principle is demonstrated that shows promise for Stage IV surveys and beyond.

Published
2020

25. Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

Author

Brian Nord, Andrew Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua Peek, Chad Schafer, Erik Tollerud, Camille Avestruz, G. Babu, Simon Birrer, Douglas Burke, Joao Caldeira, Douglas Caldwell, Joleen Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric Feigelson, V. Golkhou, Vinay Kashyap, T. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey Mandel, J. Martinez-Galarza, Adam Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta Sipocz, Arfon Smith, Nhan Tran, Ricardo Vilalta, Lucianne Walkowicz, and John ZuHone

Subjects
White paper, 010308 nuclear & particles physics, Computer science, 0103 physical sciences, Scientific discovery, Petabyte, Statistical model, 010303 astronomy & astrophysics, 01 natural sciences, Data science
Abstract

This white paper offers guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era.

Published
2020

26. Flare Statistics for Young Stars from a Convolutional Neural Network Analysis of $\textit{TESS}$ Data

Author

Benjamin T. Montet, Megan Ansdell, Maximilian N. Günther, Michael Gully-Santiago, Jacob L. Bean, Brian Nord, Joshua E. Schlieder, and Adina D. Feinstein

Subjects
Physics, 010504 meteorology & atmospheric sciences, Stellar rotation, FOS: Physical sciences, Astronomy and Astrophysics, Astrophysics, 01 natural sciences, Convolutional neural network, law.invention, Stars, Astrophysics - Solar and Stellar Astrophysics, Space and Planetary Science, law, 0103 physical sciences, Astrophysics::Solar and Stellar Astrophysics, Astrophysics::Earth and Planetary Astrophysics, Time series, Astrophysics - Instrumentation and Methods for Astrophysics, 010303 astronomy & astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Astrophysics::Galaxy Astrophysics, Solar and Stellar Astrophysics (astro-ph.SR), 0105 earth and related environmental sciences, Flare
Abstract

All-sky photometric time-series missions have allowed for the monitoring of thousands of young ($t_{\rm age} < 800$Myr) to understand the evolution of stellar activity. Here we developed a convolutional neural network (CNN), $\texttt{stella}$, specifically trained to find flares in $\textit{Transiting Exoplanet Survey Satellite}$ ($\textit{TESS}$) short-cadence data. We applied the network to 3200 young stars to evaluate flare rates as a function of age and spectral type. The CNN takes a few seconds to identify flares on a single light curve. We also measured rotation periods for 1500 of our targets and find that flares of all amplitudes are present across all spot phases, suggesting high spot coverage across the entire surface. Additionally, flare rates and amplitudes decrease for stars $t_{\rm age} > 50$Myr across all temperatures $T_{\rm eff} \geq 4000$K, while stars from $2300 \leq T_{\rm eff} < 4000$K show no evolution across 800 Myr. Stars of $T_{\rm eff} \leq 4000$K also show higher flare rates and amplitudes across all ages. We investigate the effects of high flare rates on photoevaporative atmospheric mass loss for young planets. In the presence of flares, planets lose 4-7% more atmosphere over the first 1 Gyr. $\texttt{stella}$ is an open-source Python tool-kit hosted on GitHub and PyPI., 21 pages, 17 figures, 1 table, AJ accepted

Published
2020
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27. Dark energy survey year 1 results: Cosmological constraints from cluster abundances and weak lensing

Author

Alex Drlica-Wagner, Dragan Huterer, J. Annis, I. Sevilla-Noarbe, Daniel Scolnic, N. Kuropatkin, Tesla E. Jeltema, F. Paz-Chinchón, K. D. Eckert, T. N. Varga, Brian Nord, Adam Mantz, L. N. da Costa, M. Costanzi, M. A. G. Maia, Michel Aguena, E. Suchyta, August E. Evrard, Brian Yanny, Shantanu Desai, A. Alarcon, M. W. G. Johnson, A. K. Romer, J. Carretero, Paul Martini, V. Scarpine, I. Ferrero, Matt J. Jarvis, Tamara M. Davis, Yanxi Zhang, Tim Eifler, Arya Farahi, David James, Z. Zhang, D. W. Gerdes, Joshua A. Frieman, D. Gruen, R. Cawthon, Antonella Palmese, Pablo Fosalba, Carlos Solans Sanchez, D. H. Brooks, A. R. Walker, B. Flaugher, Risa H. Wechsler, P. Rooney, Keith Bechtol, M. Sako, C. Lidman, A. von der Linden, Erin Sheldon, D. L. Hollowood, M. E. C. Swanson, Huan Lin, Sebastian Bocquet, Julian A. Mayers, Steve Kent, J. De Vicente, Martin Crocce, R. D. Wilkinson, D. L. Burke, Daniel Thomas, E. Buckley-Geer, M. Carrasco Kind, S. Everett, Robert C. Nichol, S. Allam, Robert A. Gruendl, R. L. C. Ogando, J. P. Dietrich, Juan Garcia-Bellido, E. Bertin, Marcos Lima, Michael Troxel, Eduardo Rozo, Jack Elvin-Poole, Enrique Gaztanaga, Peter Doel, Peter Melchior, Ofer Lahav, M. Smith, H. T. Diehl, Douglas L. Tucker, Kyler Kuehn, J. Allyn Smith, Paul Giles, David Bacon, Niall MacCrann, Ami Choi, Ben Hoyle, A. Roodman, Markus Rau, Tommaso Giannantonio, J. Gschwend, Gary Bernstein, Hao-Yi Wu, Tenglin Li, Scott Dodelson, S. W. Allen, Santiago Avila, Chihway Chang, M. D. Johnson, Ramon Miquel, A. Bermeo, Elisabeth Krause, Sebastian Grandis, J. Mena-Fernández, Joe Zuntz, Jochen Weller, F. J. Castander, T. M. C. Abbott, Chun-Hao To, E. J. Sanchez, K. Honscheid, Eli S. Rykoff, Richard G. Kron, A. Carnero Rosell, Felipe Menanteau, S. Samuroff, Gregory Tarle, Santiago Serrano, J. DeRose, D. J. Brout, J. Prat, Samuel Hinton, G. Gutierrez, Xi Chen, A. A. Plazas, Sunayana Bhargava, Joseph J. Mohr, A. Saro, Jennifer L. Marshall, W. G. Hartley, M. Gatti, Juan Estrada, Michael Schubnell, Ministerio de Economía y Competitividad (España), Generalitat de Catalunya, European Commission, European Research Council, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), DES, UAM. Departamento de Física Teórica, Abbott, T. M. C., Aguena, M., Alarcon, A., Allam, S., Allen, S., Annis, J., Avila, S., Bacon, D., Bechtol, K., Bermeo, A., Bernstein, G. M., Bertin, E., Bhargava, S., Bocquet, S., Brooks, D., Brout, D., Buckley-Geer, E., Burke, D. L., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Castander, F. J., Cawthon, R., Chang, C., Chen, X., Choi, A., Costanzi, M., Crocce, M., Da Costa, L. N., Davis, T. M., De Vicente, J., Derose, J., Desai, S., Diehl, H. T., Dietrich, J. P., Dodelson, S., Doel, P., Drlica-Wagner, A., Eckert, K., Eifler, T. F., Elvin-Poole, J., Estrada, J., Everett, S., Evrard, A. E., Farahi, A., Ferrero, I., Flaugher, B., Fosalba, P., Frieman, J., Garcia-Bellido, J., Gatti, M., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Giles, P., Grandis, S., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hinton, S. R., Hollowood, D. L., Honscheid, K., Hoyle, B., Huterer, D., James, D. J., Jarvis, M., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Kent, S., Krause, E., Kron, R., Kuehn, K., Kuropatkin, N., Lahav, O., Li, T. S., Lidman, C., Lima, M., Lin, H., Maccrann, N., Maia, M. A. G., Mantz, A., Marshall, J. L., Martini, P., Mayers, J., Melchior, P., Mena-Fernandez, J., Menanteau, F., Miquel, R., Mohr, J. J., Nichol, R. C., Nord, B., Ogando, R. L. C., Palmese, A., Paz-Chinchon, F., Plazas, A. A., Prat, J., Rau, M. M., Romer, A. K., Roodman, A., Rooney, P., Rozo, E., Rykoff, E. S., Sako, M., Samuroff, S., Sanchez, C., Sanchez, E., Saro, A., Scarpine, V., Schubnell, M., Scolnic, D., Serrano, S., Sevilla-Noarbe, I., Sheldon, E., Smith, J. A., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., To, C., Troxel, M. A., Tucker, D. L., Varga, T. N., Von Der Linden, A., Walker, A. R., Wechsler, R. H., Weller, J., Wilkinson, R. D., Wu, H., Yanny, B., Zhang, Y., Zhang, Z., and Zuntz, J.

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), Software_OPERATINGSYSTEMS, ComputingMethodologies_SIMULATIONANDMODELING, Cosmic microwave background, FOS: Physical sciences, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, Data_CODINGANDINFORMATIONTHEORY, 01 natural sciences, XMM-Newton Telescope, symbols.namesake, 0103 physical sciences, Hardware_INTEGRATEDCIRCUITS, Planck, 010303 astronomy & astrophysics, Weak gravitational lensing, Galaxy cluster, Physics, 010308 nuclear & particles physics, Física, ComputerSystemsOrganization_PROCESSORARCHITECTURES, Galaxies, Cosmos, Redshift, Galaxy, Cosmology, Dark energy, symbols, Baryon acoustic oscillations, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

DES Collaboration: et al., We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset. Our analysis uses the same shear and source photometric redshifts estimates as were used in the DES combined probes analysis. Our analysis results in surprisingly low values for S8=σ8(Ωm/0.3)0.5=0.65±0.04, driven by a low matter density parameter, Ωm=0.179+0.031−0.038, with σ8−Ωm posteriors in 2.4σ tension with the DES Y1 3x2pt results, and in 5.6σ with the Planck CMB analysis. These results include the impact of post-unblinding changes to the analysis, which did not improve the level of consistency with other data sets compared to the results obtained at the unblinding. The fact that multiple cosmological probes (supernovae, baryon acoustic oscillations, cosmic shear, galaxy clustering and CMB anisotropies), and other galaxy cluster analyses all favor significantly higher matter densities suggests the presence of systematic errors in the data or an incomplete modeling of the relevant physics. Cross checks with x-ray and microwave data, as well as independent constraints on the observable-mass relation from Sunyaev-Zeldovich selected clusters, suggest that the discrepancy resides in our modeling of the weak lensing signal rather than the cluster abundance. Repeating our analysis using a higher richness threshold (λ≥30) significantly reduces the tension with other probes, and points to one or more richness-dependent effects not captured by our model., 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. SEV2016-0588, No. SEV-2016-0597, 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 Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through Project No. CE110001020. This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DEAC02-07CH11359 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, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. M. C. and A. S. are supported by the ERC-StG “ClustersXCosmo” Grant agreement No. 716762. A. S. is supported by the FAREMIUR grant “ClustersXEuclid”. E. R. was supported by the DOE Grant No. DE-SC0015975, by the Sloan Foundation, Grant No. FG-2016-6443, and the Cottrell Scholar program of the Research Corporation for Science Advancement. This research used simulations that were performed resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DEAC02-05CH11231.

Published
2020

28. The STRong lensing Insights into the Dark Energy Survey (STRIDES) 2016 follow-up campaign – I. Overview and classification of candidates selected by two techniques

Author

R. C. Smith, David J. James, Pablo Fosalba, V. Scarpine, Christopher D. Fassnacht, E. Suchyta, Filipe B. Abdalla, K. Rojas, Michael Schubnell, P. R. Sivakumar, Geoff C. F. Chen, J. Carretero, Daniel A. Goldstein, Y. J. Kim, P. Williams, Paul L. Schechter, Richard G. McMahon, David Brooks, M. E. C. Swanson, K. Honscheid, M. Carrasco Kind, Flavia Sobreira, C. Lemon, C. B. D'Andrea, J. H.H. Chan, Brian Nord, Juan Garcia-Bellido, Adriano Agnello, Alistair R. Walker, F. Ostrovski, Santiago Avila, Philip J. Marshall, Matthew W. Auger, Louis E. Abramson, Marcelle Soares-Santos, A. Carnero Rosell, Peter Doel, Timo Anguita, J. Annis, Tim Eifler, Cristian E. Rusu, G. Meylan, Ramon Miquel, Felipe Menanteau, B. Flaugher, R. H. Schindler, J. De Vicente, Thomas E. Collett, G. Gutierrez, Gregory Tarle, Yordanka Apostolovski, M. Baumer, Marcio A. G. Maia, F. J. Castander, Nikolay Kuropatkin, Daniel Thomas, E. Buckley-Geer, M. Smith, Daniel Gruen, A. K. Romer, Simon Birrer, Paul Martini, T. M. C. Abbott, Veronica Motta, W. G. Hartley, E. J. Sanchez, Huan Lin, Frederic Courbin, Sherry H. Suyu, A. A. Plazas, D. L. Hollowood, S. Allam, Robert A. Gruendl, Marcos Lima, J. W. Hsueh, M. Banerji, Joshua A. Frieman, I. Sevilla-Noarbe, Tommaso Treu, Douglas L. Tucker, Kyler Kuehn, L. N. da Costa, UAM. Departamento de Física Teórica, Lemon, Cameron [0000-0003-2456-9317], McMahon, Richard [0000-0001-8447-8869], Banerji, Manda [0000-0002-0639-5141], and Apollo - University of Cambridge Repository

Subjects
Time delays, Cosmology and Nongalactic Astrophysics (astro-ph.CO), gravitationally lensed quasars, astro-ph.GA, statistical [methods], Dark matter, time-delay cosmography, internal structure, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Astronomy & Astrophysics, 01 natural sciences, matter substructure, law.invention, hubble constant, Affordable and Clean Energy, law, 0103 physical sciences, he 0435-1223, digital-sky-survey, early-type galaxies, luminosity function, Astronomical And Space Sciences, 010303 astronomy & astrophysics, STFC, catalogues, QB, Physics, methods: statistical, 010308 nuclear & particles physics, pg 1115+080, RCUK, Física, gravitational lensing: strong, Astronomy, Astronomy and Astrophysics, Quasar, Astrophysics - Astrophysics of Galaxies, Galaxy, Data set, Lens (optics), Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), strong [gravitational lensing], Outlier, astro-ph.CO, Dark energy, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The primary goals of the STRong lensing Insights into the Dark Energy Survey (STRIDES) collaboration are to measure the dark energy equation of state parameter and the free streaming length of dark matter. To this aim, STRIDES is discovering strongly lensed quasars in the imaging data of the Dark Energy Survey and following them up to measure time delays, high resolution imaging, and spectroscopy sufficient to construct accurate lens models. In this paper, we first present forecasts for STRIDES. Then, we describe the STRIDES classification scheme, and give an overview of the Fall 2016 follow-up campaign. We continue by detailing the results of two selection methods, the outlier selection technique and a morphological algorithm, and presenting lens models of a system that could possibly be a lensed quasar in an unusual configuration. We conclude with the summary statistics of the Fall 2016 campaign. Including searches presented in companion papers (Anguita et al.; Ostrovski et al.), STRIDES followed up 117 targets identifying 7 new strongly lensed systems, and 7 nearly identical quasars, which could be confirmed as lenses by the detection of the lens galaxy. 76 candidates were rejected and 27 remain otherwise inconclusive, for a success rate in the range of 6-35 per cent. This rate is comparable to that of previous searches like SDSS Quasar Lens Search even though the parent data set of STRIDES is purely photometric and our selection of candidates cannot rely on spectroscopic information., TT and VM acknowledge support by the David and Lucille Packard Foundation through a Packard Research Fellowship to TT. TT acknowledges support by the National Science Foundation through grants AST- 1450141 and AST-1714953. CDF and GCFC acknowledge support from the National Science Foundation through grants AST-1312329 and AST-1715611. TA and YA acknowledge support by proyecto FONDECYT 11130630 and by the Ministry for the Economy, Development, and Tourism’s Programa Inicativa Científica Milenio through grant IC 12009, awarded to The Millennium Institute of Astrophysics (MAS). FC, VB, and JC acknowledge support from the Swiss National Science Foundation. SHS thanks the Max Planck Society for support through the Max Planck Research Group

Published
2018

29. Dark Energy Survey Year 1 Results: redshift distributions of the weak-lensing source galaxies

Author

A. Benoit-Lévy, Tenglin Li, Chihway Chang, Filipe B. Abdalla, A. L. King, R. C. Smith, Matthew R. Becker, Flavia Sobreira, Peter Doel, R. C. Wolf, M. D. Johnson, D. L. Burke, A. Roodman, Tommaso Giannantonio, D. Mudd, Edward Macaulay, C. Davis, Enrique Fernández, T. N. Varga, H. T. Diehl, Jochen Weller, F. J. Castander, A. A. Plazas, M. T. Busha, T. M. C. Abbott, E. J. Sanchez, Matt J. Jarvis, K. Honscheid, Martin Crocce, E. Suchyta, S. Allam, Robert A. Gruendl, August E. Evrard, S. A. Uddin, Diego Capozzi, Niall MacCrann, M. March, Basilio X. Santiago, Geraint F. Lewis, M. A. G. Maia, C. Bonnett, Alex Drlica-Wagner, P. Vielzeuf, Ofer Lahav, Tim Eifler, Daniela Carollo, Daniel Gruen, B. Flaugher, Donnacha Kirk, M. E. C. Swanson, M. Gatti, J. K. Hoormann, Erin Sheldon, David Brooks, A. Carnero Rosell, J. Gschwend, Gary Bernstein, Risa H. Wechsler, Michael Troxel, Huan Lin, W. C. Wester, David J. James, I. Sevilla-Noarbe, Felipe Menanteau, J. Annis, J. DeRose, D. W. Gerdes, Alistair R. Walker, A. K. Romer, J. Prat, Samuel Hinton, Douglas L. Tucker, N. Kuropatkin, Gregory Tarle, M. Childress, Kyler Kuehn, Paul Martini, J. Carretero, Ashley J. Ross, Tesla E. Jeltema, Eli S. Rykoff, A. G. Kim, Pablo Fosalba, Tamara M. Davis, G. Gutierrez, Marcelle Soares-Santos, Daniel A. Goldstein, Jacobo Asorey, J. De Vicente, Darren L. DePoy, L. N. da Costa, Daniel B. Thomas, Jennifer L. Marshall, Brad E. Tucker, R. P. Rollins, Elisabeth Krause, Anais Möller, E. Bertin, C. B. D'Andrea, Masao Sako, Rafe Schindler, Rob Sharp, S. E. Kuhlmann, Markus Michael Rau, W. G. Hartley, Bonnie Zhang, Karl Glazebrook, M. S. Schubnell, Keith Bechtol, Peter Melchior, Fang Yuan, Brian Nord, N. E. Sommer, Brian Yanny, Shantanu Desai, C. Lidman, Joshua A. Frieman, R. Cawthon, Juan Garcia-Bellido, Eduardo Rozo, Ramon Miquel, S. Samuroff, M. Carrasco Kind, Mathew Smith, Robert C. Nichol, C. Sánchez, Marcos Lima, E. Buckley-Geer, A. Alarcon, Ricardo L. C. Ogando, Juan Estrada, Enrique Gaztanaga, Ben Hoyle, Marvin Johnson, C. R. O'Neill, J. Zuntz, Vinu Vikram, V. Scarpine, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), DES, Institut d'Astrophysique de Paris ( IAP ), and Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), [ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph], Dark matter, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics, 01 natural sciences, Photometry (optics), surveys, 0103 physical sciences, 010303 astronomy & astrophysics, STFC, Astrophysics::Galaxy Astrophysics, catalogues, Weak gravitational lensing, QB, Physics, 010308 nuclear & particles physics, RCUK, Astronomy, Astronomy and Astrophysics, Redshift survey, methods: data analysis, Redshift, Galaxy, Gravitational lens, Space and Planetary Science, Dark energy, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We describe the derivation and validation of redshift distribution estimates and their uncertainties for the galaxies used as weak lensing sources in the Dark Energy Survey (DES) Year 1 cosmological analyses. The Bayesian Photometric Redshift (BPZ) code is used to assign galaxies to four redshift bins between z=0.2 and 1.3, and to produce initial estimates of the lensing-weighted redshift distributions $n^i_{PZ}(z)$ for bin i. Accurate determination of cosmological parameters depends critically on knowledge of $n^i$ but is insensitive to bin assignments or redshift errors for individual galaxies. The cosmological analyses allow for shifts $n^i(z)=n^i_{PZ}(z-\Delta z^i)$ to correct the mean redshift of $n^i(z)$ for biases in $n^i_{\rm PZ}$. The $\Delta z^i$ are constrained by comparison of independently estimated 30-band photometric redshifts of galaxies in the COSMOS field to BPZ estimates made from the DES griz fluxes, for a sample matched in fluxes, pre-seeing size, and lensing weight to the DES weak-lensing sources. In companion papers, the $\Delta z^i$ are further constrained by the angular clustering of the source galaxies around red galaxies with secure photometric redshifts at 0.15, Comment: MNRAS accepted; 20 pages, 8 figures

Published
2018

30. UV-luminous, star-forming hosts of z ∼ 2 reddened quasars in the Dark Energy Survey

Author

David J. James, Pablo Fosalba, V. Scarpine, A. Benoit-Lévy, R. C. Smith, J. Gschwend, J. Carretero, M. Carrasco-Kind, Flavia Sobreira, G. Gutierrez, Shantanu Desai, S. L. Reed, Paul C. Hewett, Richard G. McMahon, David Brooks, Daniel Gruen, Brian Nord, M. Banerji, Joshua A. Frieman, Peter Doel, A. K. Romer, Juan Garcia-Bellido, Jennifer L. Marshall, E. J. Sanchez, Robert A. Gruendl, E. Buckley-Geer, Paul Martini, E. Suchyta, F. B. Abdalla, D. W. Gerdes, B. Flaugher, Alistair R. Walker, Ramon Miquel, Marcelle Soares-Santos, Carlos E. Cunha, Robert C. Nichol, Rafe Schindler, Kyler Kuehn, C. F. Wethers, Tesla E. Jeltema, Matthew Smith, Marcos Lima, I. Sevilla-Noarbe, N. Kuropatkin, L. N. da Costa, M. A. G. Maia, A. A. Plazas, K. Honscheid, C. Lemon, C. B. D'Andrea, Diego Capozzi, S. E. Kuhlmann, Michael Schubnell, Darren L. DePoy, A. Carnero Rosell, Yue Shen, Felipe Menanteau, Gregory Tarle, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), DES, Banerji, Manda [0000-0002-0639-5141], Hewett, Paul [0000-0002-6528-1937], Lemon, Cameron [0000-0003-2456-9317], McMahon, Richard [0000-0001-8447-8869], and Apollo - University of Cambridge Repository

Subjects
Astrophysics::High Energy Astrophysical Phenomena, galaxies: active, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics, 01 natural sciences, Photometry (optics), galaxies: high-redshift, quasars: general, 0103 physical sciences, Galaxy formation and evolution, Astrophysics::Solar and Stellar Astrophysics, 010303 astronomy & astrophysics, STFC, Astrophysics::Galaxy Astrophysics, QB, Physics, 010308 nuclear & particles physics, Star formation, RCUK, Astronomy and Astrophysics, Quasar, Astrophysics - Astrophysics of Galaxies, Galaxy, Wavelength, 13. Climate action, Space and Planetary Science, galaxies: star formation, Astrophysics of Galaxies (astro-ph.GA), ultraviolet: galaxies, Dark energy, Spectral energy distribution, Astrophysics::Earth and Planetary Astrophysics, galaxies: evolution, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Abstract

We present the first rest-frame UV population study of 17 heavily reddened, high-luminosity (E(B-V)$_{\rm{QSO}}\gtrsim$ 0.5; L$_{\rm{bol}}>$ 10$^{46}$ergs$^{-1}$) broad-line quasars at $1.5 < z < 2.7$. We combine the first year of deep, optical, ground-based observations from the Dark Energy Survey (DES) with the near infrared VISTA Hemisphere Survey (VHS) and UKIDSS Large Area Survey (ULAS) data, from which the reddened quasars were initially identified. We demonstrate that the significant dust reddening towards the quasar in our sample allows host galaxy emission to be detected at the rest-frame UV wavelengths probed by the DES photometry. By exploiting this reddening effect, we disentangle the quasar emission from that of the host galaxy via spectral energy distribution (SED) fitting. We find evidence for a relatively unobscured, star-forming host galaxy in at least ten quasars, with a further three quasars exhibiting emission consistent with either star formation or scattered light. From the rest-frame UV emission, we derive instantaneous, dust-corrected star formation rates (SFRs) in the range 25 < SFR$_{\rm{UV}}$ < 365 M$_{\odot}$yr$^{-1}$, with an average SFR$_{\rm{UV}}$ = 130 $\pm$ 95 M$_{\odot}$yr$^{-1}$. We find a broad correlation between SFR$_{\rm{UV}}$ and the bolometric quasar luminosity. Overall, our results show evidence for coeval star formation and black hole accretion occurring in luminous, reddened quasars at the peak epoch of galaxy formation., 21 pages, 8 figures, MNRAS Accepted

Published
2018

31. A Need for Dedicated Outreach Expertise and Online Programming: Astro2020 Science White Paper

Author

Dara Norman, Lauren Corlies, Megan E. Schwamb, Britt Lundgren, Brian Nord, Amanda E. Bauer, and William O'Mullane

Subjects
Outreach, Engineering, Engineering management, White paper, Web development, Scale (ratio), business.industry, ComputingMilieux_COMPUTERSANDEDUCATION, business
Abstract

Maximizing the public impact of astronomy projects in the next decade requires NSF-funded centers to support the development of online, mobile-friendly outreach and education activities. EPO teams with astronomy, education, and web development expertise should be in place to build accessible programs at scale and support astronomers doing outreach.

Published
2019

33. Astro2020 APC White Paper: Elevating the Role of Software as a Product of the Research Enterprise

Author

Erik Tollerud, Iva Laginja, Ivelina Momcheva, Vandana Desai, Derek Buzasi, Britton D. Smith, Benjamin J. Weiner, Lior Shamir, Matthew J. Turk, Molly S. Peeples, Jennifer Sobeck, Joel R. Brownstein, Jacqueline K. Faherty, Bruce Berriman, Maryam Modjaz, Gautham Narayan, André Leon Sampaio Gradvohl, Adam Ginsburg, Andrew J. Connolly, Arfon M. Smith, Heloise F. Stevance, Eric C. Bellm, Frossie Economou, Adrian M. Price-Whelan, Kelle L. Cruz, Britt Lundgren, Brian Nord, Chi-kwan Chan, Chuanfei Dong, Joseph Harrington, V. Zach Golkhou, Jeyhan S. Kartaltepe, Chad M. Schafer, Peter Teuben, Wolfgang Kerzendorf, Manodeep Sinha, Dara Norman, David R. Rodriguez, Robert J. Nemiroff, Brian Cherinka, Bruno Merín, Cameron Hummels, Brigitta Sipőcz, and T. E. Pickering

Subjects
White paper, Software, business.industry, Product (category theory), business, Manufacturing engineering
Published
2019

34. Cosmological Constraints from Multiple Probes in the Dark Energy Survey

Author

Eric H. Neilsen, Martin Crocce, A. Carnero Rosell, J. Carretero, Tamara M. Davis, Martin Becker, J. P. Dietrich, J. Gschwend, Richard G. McMahon, J. DeRose, L. F. Secco, D. J. Brout, Darren L. DePoy, W. G. Hartley, F. J. Castander, Richard G. Kron, P. Andersen, Lluís Galbany, A. Roodman, Tommaso Giannantonio, C. Davis, Gary Bernstein, Daniel Gruen, S. A. Uddin, Ofer Lahav, J. Lasker, Elisabeth Krause, J. Muir, Enrique Gaztanaga, N. E. Sommer, Rob Sharp, D. L. Hollowood, Risa H. Wechsler, D. W. Gerdes, A. G. Kim, V. Scarpine, S. Serrano, Richard Kessler, A. Campos, Eli S. Rykoff, Erin Sheldon, David Brooks, D. L. Burke, Geraint F. Lewis, David Goldstein, Carlos Solans Sanchez, Bonnie Zhang, Alex Drlica-Wagner, Marcos Lima, I. Sevilla-Noarbe, H. T. Diehl, P. Martini, M. Gatti, David Bacon, J. Calcino, David J. James, Joseph J. Mohr, J. Prat, Samuel Hinton, Bruce A. Bassett, J. P. Marriner, M. Carrasco Kind, Eric J. Baxter, G. Gutierrez, Dragan Huterer, Niall MacCrann, M. H.L.S. Wang, Bhuvnesh Jain, B. Flaugher, F. Andrade-Oliveira, Alistair R. Walker, Robert C. Nichol, Joe Zuntz, S. Kent, Pablo Fosalba, S. Allam, Ryan J. Foley, Robert A. Gruendl, Anais Möller, N. Kokron, E. Bertin, R. Cawthon, Ramon Miquel, Ashley J. Ross, Juan Estrada, M. S. Schubnell, P. Doel, Marcelle Soares-Santos, J. Annis, H. Camacho, Will J. Percival, N. Weaverdyck, A. Porredon, Karl Glazebrook, Enrique Fernández, Juan Garcia-Bellido, P. Challis, Huan Lin, Daniela Carollo, Jennifer L. Marshall, Brad E. Tucker, K. Honscheid, Brian Yanny, Shantanu Desai, N. Kuropatkin, E. Kasai, Rafe Schindler, Mathew Smith, A. Alarcon, M. Childress, C. Lidman, Felipe Menanteau, Daniel Scolnic, Gregory Tarle, L. N. da Costa, Tesla E. Jeltema, August E. Evrard, E. Swann, K. C. Chan, J. De Vicente, P. Lemos, Daniel Thomas, Brian Nord, Flavia Sobreira, Michael Troxel, Joshua A. Frieman, M. March, M. E. C. Swanson, Matt J. Jarvis, Kyler Kuehn, P. Vielzeuf, Yanxi Zhang, Tim Eifler, Keith Bechtol, Antonella Palmese, Rogerio Rosenfeld, M. A. G. Maia, J. K. Hoormann, Ricardo L. C. Ogando, Ben Hoyle, A. A. Plazas, A. K. Romer, C. B. D'Andrea, Edward Macaulay, Jack Elvin-Poole, S. Samuroff, R. C. Thomas, Eric Morganson, Jacobo Asorey, Yen-Chen Pan, Peter Melchior, N. Banik, Jonathan Blazek, Tenglin Li, Sarah Bridle, Scott Dodelson, Santiago Avila, Chihway Chang, Carlos E. Cunha, Jochen Weller, T. M. C. Abbott, Hiranya V. Peiris, E. J. Sanchez, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), DES, National Optical Astronomy Observatory, Institut d'Estudis Espacials de Catalunya (IEEC), Institute of Space Sciences (ICE CSIC), Fermi National Accelerator Laboratory, University of Queensland, Dark Cosmology Centre, Universidade Estadual Paulista (UNESP), Laboratório Interinstitucional de E-Astronomia - LIneA, Korea Astronomy and Space Science Institute, University of Portsmouth, African Institute for Mathematical Sciences, South African Astronomical Observatory, University of Pennsylvania, LSST, University of Wisconsin-Madison, Argonne National Laboratory, Institut d'Astrophysique de Paris, Ohio State University, Observatoire de Sauverny, University of Manchester, University College London, Stanford University, SLAC National Accelerator Laboratory, Universidade de São Paulo (USP), Carnegie Mellon University, Medioambientales y Tecnológicas (CIEMAT), Astrophysical Observatory of Turin, University of Illinois at Urbana-Champaign, National Center for Supercomputing Applications, Barcelona Institute of Science and Technology, Harvard-Smithsonian Center for Astrophysics, University of Chicago, University of Southampton, Observatório Nacional, Texas A and M University, 382 Via Pueblo Mall, IIT Hyderabad, Excellence Cluster Universe, Ludwig-Maximilians-Universität, Steward Observatory, California Institute of Technology, University of Michigan, Santa Cruz Institute for Particle Physics, University of Pittsburgh, Universidad Autonoma de Madrid, University of Cambridge, Ludwig-Maximilians Universität München, Swinburne University of Technology, ETH Zurich, Max Planck Institute for Extraterrestrial Physics, University of Namibia, Lawrence Berkeley National Laboratory, Macquarie University, University of Sydney, Australian National University, Peyton Hall, Institució Catalana de Recerca i Estudis Avançats, ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), National Astronomical Observatory of Japan, Academia Sinica, University of Waterloo, Perimeter Institute for Theoretical Physics, University of Sussex, Brookhaven National Laboratory, Physics Department, Universidade Estadual de Campinas (UNICAMP), Duke University, Observatories of the Carnegie Institution for Science, University of Edinburgh, Natl Opt Astron Observ, IEEC, CSIC, Fermilab Natl Accelerator Lab, Univ Queensland, Univ Copenhagen, Universidade Estadual Paulista (Unesp), Lab Interinst E Astron LIneA, Korea Astron & Space Sci Inst, Univ Portsmouth, African Inst Math Sci, South African Astron Observ, Univ Penn, Univ Wisconsin, Argonne Natl Lab, CNRS, UPMC Univ Paris 06, Ohio State Univ, Ecole Polytech Fed Lausanne, Univ Manchester, UCL, Stanford Univ, SLAC Natl Accelerator Lab, Carnegie Mellon Univ, Ctr Invest Energet Medioambientales & Tecnol CIEM, Astrophys Observ Turin, Univ Illinois, Natl Ctr Supercomp Applicat, Barcelona Inst Sci & Technol, Harvard Smithsonian Ctr Astrophys, Univ Chicago, Univ Southampton, Observ Nacl, Texas A&M Univ, Ludwig Maximilians Univ Munchen, Steward Observ, CALTECH, Univ Michigan, Santa Cruz Inst Particle Phys, Univ Pittsburgh, Univ Autonoma Madrid, Univ Cambridge, Swinburne Univ Technol, Swiss Fed Inst Technol, Max Planck Inst Extraterr Phys, Univ Namibia, Lawrence Berkeley Natl Lab, Macquarie Univ, Univ Sydney, Australian Natl Univ, Princeton Univ, Inst Catalana Recerca & Estudis Avancats, ARC Ctr Excellence All Sky Astrophys CAASTRO, Natl Astron Observ Japan, Acad Sinica, Univ Waterloo, Perimeter Inst Theoret Phys, Univ Sussex, Brookhaven Natl Lab, Brandeis Univ, Duke Univ, Observ Carnegie Inst Sci, and Univ Edinburgh

Subjects
bao, Cosmology and Nongalactic Astrophysics (astro-ph.CO), MATÉRIA ESCURA, media_common.quotation_subject, Cosmic microwave background, Dark matter, FOS: Physical sciences, General Physics and Astronomy, Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics, des, Gravitation and Astrophysics, 7. Clean energy, 01 natural sciences, Cosmology, supernova, galaxies, 0103 physical sciences, redshift distributions, 010306 general physics, QC, STFC, Weak gravitational lensing, media_common, Physics, Equation of state (cosmology), RCUK, Shape of the universe, Universe, 13. Climate action, astro-ph.CO, Dark energy, baryon acoustic-oscillations, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], sdss, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The combination of multiple observational probes has long been advocated as a powerful technique to constrain cosmological parameters, in particular dark energy. The Dark Energy Survey has measured 207 spectroscopically--confirmed Type Ia supernova lightcurves; the baryon acoustic oscillation feature; weak gravitational lensing; and galaxy clustering. Here we present combined results from these probes, deriving constraints on the equation of state, $w$, of dark energy and its energy density in the Universe. Independently of other experiments, such as those that measure the cosmic microwave background, the probes from this single photometric survey rule out a Universe with no dark energy, finding $w=-0.80^{+0.09}_{-0.11}$. The geometry is shown to be consistent with a spatially flat Universe, and we obtain a constraint on the baryon density of $\Omega_b=0.069^{+0.009}_{-0.012}$ that is independent of early Universe measurements. These results demonstrate the potential power of large multi-probe photometric surveys and pave the way for order of magnitude advances in our constraints on properties of dark energy and cosmology over the next decade., Comment: 8 pages, 2 figures; v3 matches version accepted by PRL

Published
2019

35. Challenges and Approaches for Mining Astronomical Data and Complex Models

Author

M.J. Marquez and Brian Nord

Subjects
Knowledge extraction, Computer science, business.industry, Big data, Goal pursuit, Set (psychology), business, Data science, Big data mining, Field (computer science)
Abstract

This paper describes the main challenges encountered when implementing new approaches for handling very large data sets in the area of Deep Cosmological Fields.The ultimate goal pursuit is the knowledge discovery in the area of galaxies surface brightness and by that the improvement of our current knowledge regarding galaxies and their role in the Universe. The specific problem addressed here is the de-blending of very faint and blurred galaxies in crowded images.We introduce a set of artificial intelligence methodologies for the effective implementation of a proof-of-concept, which can then be imported into Big Data Computing platforms, such as XSEDE. This paper is structured as follows: chapter 1 - Introduction, describing the current state-of-the-art and motivation related to the field of Big Data Mining, focussing on astronomical data; chapter 2 - Description of the difficulty of de-blending blurred and faint galaxies, one of the main challenges in the area of complex computing and very large data sets; chapter 3 - New approach, detailing the methodologies used for the problem of de blending blurred and overlapped sources; chapter 4 - Results and Discussion presenting preliminary results, advantages, disadvantages and future lines of work in this area.

Published
2019

36. Finding high-redshift strong lenses in DES using convolutional neural networks

Author

J. Carretero, Daniel A. Goldstein, Huan Lin, Paul Martini, J. De Vicente, T. M. C. Abbott, E. Suchyta, Flavia Sobreira, M. Carrasco Kind, E. J. Sanchez, G. Gutierrez, Scarpine, Michael Schubnell, G. Tarle, Tenglin Li, J. Annis, Kyler Kuehn, Santiago Avila, Chris McCarthy, E. Bertin, D. L. Burke, Enrique Gaztanaga, A. A. Plazas, C. Davis, Brian Nord, J. Gschwend, Robert A. Gruendl, Joe Zuntz, Peter Doel, H. T. Diehl, M. E. C. Swanson, Yanxi Zhang, Tim Eifler, Ben Hoyle, D. L. Hollowood, Alistair R. Walker, S. Serrano, M. A. G. Maia, Shantanu Desai, Marcos Lima, Marcelle Soares-Santos, Mathew Smith, A. K. Qin, W. G. Hartley, David J. James, David Brooks, Karl Glazebrook, Joshua A. Frieman, K. Bechtol, I. Sevilla-Noarbe, N. Kuropatkin, L. N. da Costa, A. Carnero Rosell, E. Buckley-Geer, K. Honscheid, Ofer Lahav, Ramon Miquel, Filipe B. Abdalla, B. Flaugher, Thomas E. Collett, Vikram, Juan Garcia-Bellido, Colin Jacobs, Daniel Gruen, D. W. Gerdes, C. J. Miller, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and DES

Subjects
astro-ph.GA, AST-1138766, statistical [methods], FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Astronomy & Astrophysics, 01 natural sciences, Imaging data, Convolutional neural network, law.invention, Methods statistical, Combinatorics, law, 0103 physical sciences, LENTES GRAVITACIONAIS, AST-1536171, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Instrumentation and Methods for Astrophysics (astro-ph.IM), 010303 astronomy & astrophysics, STFC, Physics, methods: statistical, 010308 nuclear & particles physics, RCUK, gravitational lensing: strong, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, Redshift, Lens (optics), Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), strong [gravitational lensing], Astrophysics - Instrumentation and Methods for Astrophysics, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astronomical and Space Sciences, astro-ph.IM
Abstract

We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250,000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g - i < 5, 0.6 < g -r < 3, r_mag > 19, g_mag > 20 and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7,301 galaxies. During visual inspection we rate 84 as "probably" or "definitely" lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9,428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range., Accepted for publication in MNRAS

Published
2019

37. First Cosmology Results Using Type Ia Supernovae from the Dark Energy Survey: Photometric Pipeline and Light-curve Data Release

Author

Anais Möller, J. Carretero, Tamara M. Davis, Daniel A. Goldstein, M. S. Schubnell, Richard Kessler, Douglas L. Tucker, Peter Nugent, Martin Crocce, H. T. Diehl, C. B. D'Andrea, Alex G. Kim, W. G. Hartley, S. Serrano, Ken Herner, David J. James, Flavia Sobreira, Bruce A. Bassett, Lluís Galbany, N. P. Kuropatkin, Devon L. Hollowood, T. M. C. Abbott, Daniel Gruen, Enrique Gaztanaga, Pablo Fosalba, E. Suchyta, J. Frieman, E. J. Sanchez, Yanxi Zhang, Tim Eifler, Paul Martini, A. Roodman, M. March, D. W. Gerdes, Brian Yanny, Yen-Chen Pan, C. Davis, A. A. Plazas, L. N. da Costa, Shantanu Desai, Mark Sullivan, M. Carrasco Kind, Robert C. Nichol, Peter de Nully Brown, E. Kasai, Mathew Smith, S. Allam, Robert A. Gruendl, David Brooks, M. E. C. Swanson, Francisco J. Castander, Daniel Scolnic, A. Carnero Rosell, J. De Vicente, Carlos Cunha, Ofer Lahav, Marco A. P. Lima, Brian Nord, Juan Garcia-Bellido, J. Lasker, Samuel Hinton, Tenglin Li, Santiago Avila, R. C. Thomas, J. Gschwend, G. Gutierrez, D. J. Brout, Eric Morganson, Masao Sako, William Wester, Ryan J. Foley, Edward Macaulay, Kyler Kuehn, V. Scarpine, Daniel Thomas, Emmanuel Bertin, Alistair R. Walker, Marcelle Soares-Santos, Rafe Schindler, Jennifer L. Marshall, Brad E. Tucker, Gregory Tarle, K. Honscheid, Eli S. Rykoff, D. L. Burke, I. Sevilla-Noarbe, R. Wolf, B. Flaugher, James Annis, M. Childress, Ramon Miquel, P. Doel, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and DES

Subjects
010504 meteorology & atmospheric sciences, Pipeline (computing), Astrophysics::High Energy Astrophysical Phenomena, FOS: Physical sciences, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Cosmology, techniques: photometric, supernovae: general, 0103 physical sciences, Astrophysics::Solar and Stellar Astrophysics, Surface brightness, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], 010303 astronomy & astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), STFC, Astrophysics::Galaxy Astrophysics, 0105 earth and related environmental sciences, Physics, Astrophysics::Instrumentation and Methods for Astrophysics, RCUK, Astronomy and Astrophysics, Function (mathematics), Light curve, Galaxy, Supernova, Space and Planetary Science, cosmology: observations, Dark energy, SUPERNOVAS, Astrophysics - Instrumentation and Methods for Astrophysics, astro-ph.IM
Abstract

We present griz light curves of 251 Type Ia Supernovae (SNe Ia) from the first 3 years of the Dark Energy Survey Supernova Program's (DES-SN) spectroscopically classified sample. The photometric pipeline described in this paper produces the calibrated fluxes and associated uncertainties used in the cosmological parameter analysis (Brout et al. 2018-SYS, DES Collaboration et al. 2018) by employing a scene modeling approach that simultaneously forward models a variable transient flux and temporally constant host galaxy. We inject artificial point sources onto DECam images to test the accuracy of our photometric method. Upon comparison of input and measured artificial supernova fluxes, we find flux biases peak at 3 mmag. We require corrections to our photometric uncertainties as a function of host galaxy surface brightness at the transient location, similar to that seen by the DES Difference Imaging Pipeline used to discover transients. The public release of the light curves can be found at https://des.ncsa.illinois.edu/releases/sn., Comment: 12 Pages, 8 Figures, Submitted to ApJ, Comments welcome

Published
2019

38. Probing the Fundamental Nature of Dark Matter with the Large Synoptic Survey Telescope

Author

Alex Drlica-Wagner, Yao-Yuan Mao, Susmita Adhikari, Robert Armstrong, Arka Banerjee, Nilanjan Banik, Keith Bechtol, Simeon Bird, Kimberly Boddy, Ana Bonaca, Jo Bovy, Buckley, Matthew R., Esra Bulbul, Chihway Chang, George Chapline, Johann Cohen-Tanugi, Alessandro Cuoco, Francis-Yan Cyr-Racine, Dawson, William A., Ana Díaz Rivero, Cora Dvorkin, Denis Erkal, Fassnacht, Christopher D., Juan García-Bellido, Maurizio Giannotti, Vera Gluscevic, Nathan Golovich, David Hendel, Hezaveh, Yashar D., Shunsaku Horiuchi, James Jee, M., Manoj Kaplinghat, Keeton, Charles R., Koposov, Sergey E., Li, Ting S., Rachel Mandelbaum, Mcdermott, Samuel D., Mitch Mcnanna, Michael Medford, Manuel Meyer, Moniez Marc, Simona Murgia, Nadler, Ethan O., Lina Necib, Eric Nuss, Pace, Andrew B., Peter, Annika H. G., Polin, Daniel A., Chanda Prescod-Weinstein, Read, Justin I., Rogerio Rosenfeld, Nora Shipp, Simon, Joshua D., Slatyer, Tracy R., Oscar Straniero, Strigari, Louis E., Erik Tollerud, Anthony Tyson, J., Mei-Yu Wang, Wechsler, Risa H., David Wittman, Hai-Bo Yu, Gabrijela Zaharijas, Yacine Ali-Haïmoud, James Annis, Simon Birrer, Rahul Biswas, Jonathan Blazek, Brooks, Alyson M., Elizabeth Buckley-Geer, Regina Caputo, Eric Charles, Seth Digel, Scott Dodelson, Brenna Flaugher, Joshua Frieman, Eric Gawiser, Hearin, Andrew P., Renee Hložek, Bhuvnesh Jain, Jeltema, Tesla E., Koushiappas, Savvas M., Mariangela Lisanti, Marilena Loverde, Siddharth Mishra-Sharma, Newman, Jeffrey A., Brian Nord, Erfan Nourbakhsh, Steven Ritz, Robertson, Brant E., Sánchez-Conde, Miguel A., Anže Slosar, Tait, Tim M. P., Aprajita Verma, Ricardo Vilalta, Walter, Christopher W., Brian Yanny, Zentner, Andrew R., Marine Sciences Research Center (MSRC), Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY), Laboratoire Univers et Particules de Montpellier (LUPM), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy-le-Vieux de Physique Théorique (LAPTH), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Ohio State University [Columbus] (OSU), Abdus Salam International Centre for Theoretical Physics [Trieste] (ICTP), SLAC National Accelerator Laboratory (SLAC), Stanford University, Department of Physics and Astronomy [Pittsburgh], University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), California Institute of Technology (CALTECH), LSST Dark Energy Collaboration, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Montpellier 2 - Sciences et Techniques (UM2)

Subjects
[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO], Astrophysics::Instrumentation and Methods for Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics
Abstract

94 pages, 22 figures, 1 table; Astrophysical and cosmological observations currently provide the only robust, empirical measurements of dark matter. Future observations with Large Synoptic Survey Telescope (LSST) will provide necessary guidance for the experimental dark matter program. This white paper represents a community effort to summarize the science case for studying the fundamental physics of dark matter with LSST. We discuss how LSST will inform our understanding of the fundamental properties of dark matter, such as particle mass, self-interaction strength, non-gravitational couplings to the Standard Model, and compact object abundances. Additionally, we discuss the ways that LSST will complement other experiments to strengthen our understanding of the fundamental characteristics of dark matter. More information on the LSST dark matter effort can be found at https://lsstdarkmatter.github.io/ .

Published
2019

39. Transfer learning for galaxy morphology from one survey to another

Author

V. Scarpine, Michael Schubnell, Mariangela Bernardi, E. Suchyta, August E. Evrard, T. M. C. Abbott, N. Kuropatkin, L. N. da Costa, A. Carnero Rosell, M. E. C. Swanson, E. J. Sanchez, Peter Doel, Kyler Kuehn, R. H. Schindler, Ben Hoyle, Santiago Avila, Sugata Kaviraj, Carlos E. Cunha, K. Honscheid, Ofer Lahav, Joshua A. Frieman, G. Tarle, Joe Zuntz, J. Annis, Peter Melchior, Brian Nord, David J. Brooks, David J. James, Felipe Menanteau, Pablo Fosalba, Marc Huertas-Company, M. Carrasco Kind, Alistair R. Walker, H. Doḿinguez Sanchez, Marcelle Soares-Santos, Filipe B. Abdalla, Ramon Miquel, M. A. G. Maia, A. A. Plazas, G. Gutierrez, D. L. Hollowood, C. Davis, C. B. D'Andrea, J. Gschwend, Enrique Gaztanaga, W. G. Hartley, Flavia Sobreira, Robert A. Gruendl, M. Smith, D. W. Gerdes, J. L. Fischer, Daniel Gruen, M. March, Daniel Thomas, E. Buckley-Geer, J. De Vicente, Juan Garcia-Bellido, J. Carretero, R. C. Smith, Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7), DES, Observatoire de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)

Subjects
structure [Galaxies], astro-ph.GA, FOS: Physical sciences, Morphology (biology), photometric [Methods], Astrophysics::Cosmology and Extragalactic Astrophysics, Surveys, 01 natural sciences, surveys, methods: photometric, 0103 physical sciences, observational [Methods], 010303 astronomy & astrophysics, Physics, 010308 nuclear & particles physics, Astronomy, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, Methods observational, Galaxy, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), galaxies: structure, methods: observational, Transfer of learning, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Abstract

Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study., Accepted for publication in MNRAS

Published
2019

40. First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters

Author

M. A. G. Maia, Brian Yanny, S. Serrano, Shantanu Desai, T. M. C. Abbott, Lluís Galbany, E. Kasai, Mathew Smith, Kaisey S. Mandel, R. J. Foley, Alexei V. Filippenko, P. Martini, N. Kuropatkin, Daniel Scolnic, Edward Macaulay, E. J. Sanchez, Per Kragh Andersen, Peter de Nully Brown, Brian Nord, N. E. Sommer, Tenglin Li, J. Carretero, H. Spinka, Tamara M. Davis, Robert P. Kirshner, Huan Lin, Carlos Cunha, J. Calcino, David J. James, Santiago Avila, M. Pursiainen, Bruce A. Bassett, Antonella Palmese, Eric H. Neilsen, Daniel A. Goldstein, H. T. Diehl, Ofer Lahav, Pablo Fosalba, E. Swann, R. Cawthon, J. Lasker, V. Scarpine, Peter Nugent, Enrique Fernández, Geraint F. Lewis, Stephen M. Kent, Juan Garcia-Bellido, Eduardo Rozo, A. Roodman, Tommaso Giannantonio, C. Davis, Alex Drlica-Wagner, Daniel Thomas, Karl Glazebrook, Emmanuel Bertin, Alejandro Clocchiatti, D. Brout, Yanxi Zhang, Flavia Sobreira, J. K. Hoormann, W. G. Hartley, Michael D. Johnson, Ricardo L. C. Ogando, Syed Uddin, Daniela Carollo, Tim Eifler, Ben Hoyle, Elisabeth Krause, M. W. G. Johnson, I. Sevilla-Noarbe, A. K. Romer, Mark Sullivan, F. J. Castander, Anais Möller, Alistair R. Walker, Marcelle Soares-Santos, A. Carnero Rosell, David Brooks, Rafe Schindler, M. March, J. Gschwend, M. S. Schubnell, B. Flaugher, Steve Kuhlmann, Yen-Chen Pan, D. A. Finley, Gary Bernstein, R. C. Wolf, Jennifer L. Marshall, Keith Bechtol, Ramon Miquel, Ravi R. Gupta, P. Doel, J. De Vicente, Peter M. Challis, Brad E. Tucker, A. A. Plazas, A. E. Evrard, Daniel Gruen, Eli S. Rykoff, V. Miranda, D. L. Burke, Felipe Menanteau, Gregory Tarle, Daniel Muthukrishna, L. N. da Costa, K. Honscheid, Ricard Casas, Christopher J. Miller, M. Carrasco Kind, Robert C. Nichol, Arturo Avelino, Tesla E. Jeltema, Joshua A. Frieman, Eve Kovacs, R. C. Thomas, Eric Morganson, Jacobo Asorey, C. B. D'Andrea, Alex G. Kim, Bin-Bin Zhang, Santiago González-Gaitán, Enrique Gaztanaga, William Wester, Kyler Kuehn, Michael Troxel, S. Allam, Robert A. Gruendl, Marcos Lima, M. Childress, C. Lidman, D. L. DePoy, Joseph J. Mohr, Rob Sharp, C. Angus, D. L. Hollowood, Samuel Hinton, G. Gutierrez, Richard Kessler, J. Marriner, P. Wiseman, Richard G. Kron, E. Suchyta, Masao Sako, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and DES

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), 010504 meteorology & atmospheric sciences, MATÉRIA ESCURA, Cosmic microwave background, FOS: Physical sciences, Astrophysics, Cosmological constant, Astrophysics::Cosmology and Extragalactic Astrophysics, Astronomy & Astrophysics, 01 natural sciences, Omega, Cosmology, dark matter, Photometry (optics), 0103 physical sciences, Astrophysics::Solar and Stellar Astrophysics, Astronomical And Space Sciences, dark energy, 010303 astronomy & astrophysics, QC, STFC, Astrophysics::Galaxy Astrophysics, 0105 earth and related environmental sciences, Physics, RCUK, Astronomy and Astrophysics, Supernova, 13. Climate action, Space and Planetary Science, Dark energy, astro-ph.CO, Baryon acoustic oscillations, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat LCDM model we find a matter density Ωm = 0.331 +_ 0.038. For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state w = -0.978 +_ 0.059, and Ωm = 0.321 +_ 0.018. For a flat w0 w aCDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find w0 = -0.885 +_ 0.114 and wa = -0.387 +_ 0.430. These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA).

Published
2019

41. deeplenstronomy: A dataset simulation package for strong gravitational lensing

Author

J. Poh, Robert Morgan, Brian Nord, Joshua Yao-Yu Lin, and Simon Birrer

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), Computer science, Strong gravitational lensing, FOS: Physical sciences, General Relativity and Quantum Cosmology (gr-qc), computer.software_genre, 01 natural sciences, General Relativity and Quantum Cosmology, 010305 fluids & plasmas, Optical imaging, 0103 physical sciences, Data mining, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), 010303 astronomy & astrophysics, computer, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ ., Published in the Journal of Open Source Software

Published
2021

42. SPOKES: An end-to-end simulation facility for spectroscopic cosmological surveys

Author

La. Gamper, Jaime E. Forero-Romero, Lukas Gamper, Donnacha Kirk, Adam Amara, Brian Nord, Chihway Chang, Carlos E. Cunha, B. Hambrecht, A. H. Bauer, Stephanie Jouvel, Michael T. Busha, Alexandre Refregier, Andrina Nicola, O. Coles, Will Saunders, Risa H. Wechsler, and Santiago Serrano

Subjects
Flexibility (engineering), Computer science, Group method of data handling, business.industry, Astronomy and Astrophysics, Usability, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Pipeline (software), Computer Science Applications, 010309 optics, Data access, Space and Planetary Science, Component (UML), 0103 physical sciences, Systems engineering, Dark energy, Benchmark (computing), business, 010303 astronomy & astrophysics
Abstract

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.

Published
2016

43. DeepMerge: Classifying high-redshift merging galaxies with deep neural networks

Author

Gregory F. Snyder, A. Ćiprijanović, Brian Nord, and Joshua E. G. Peek

Subjects
010308 nuclear & particles physics, Computer science, business.industry, Deep learning, Astronomy and Astrophysics, Pattern recognition, Astrophysics::Cosmology and Extragalactic Astrophysics, Galaxy merger, 01 natural sciences, Convolutional neural network, Galaxy, Redshift, Computer Science Applications, Random forest, Space and Planetary Science, Test set, 0103 physical sciences, Artificial intelligence, business, 010303 astronomy & astrophysics, Classifier (UML)
Abstract

We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e., z = 2 ). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a “pristine” data set and that with noise form a “noisy” data set. The test set classification accuracy of the CNN is 79% for pristine and 76% for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, M 20 statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.

Published
2020

44. Dark Energy Survey year 1 results: Galaxy-galaxy lensing

Author

Keith Bechtol, B. Flaugher, David Thomas, H. T. Diehl, A. K. Romer, David Bacon, Paul Martini, Elisabeth Krause, Eli S. Rykoff, J. Prat, Joe Zuntz, Niall MacCrann, Marcos Lima, Masao Sako, W. G. Hartley, G. Gutierrez, C. Davis, A. Roodman, J. Gschwend, R. P. Rollins, M. D. Johnson, Gary Bernstein, R. H. Schindler, Tommaso Giannantonio, R. C. Smith, L. F. Secco, R. Cawthon, G. Tarle, Ramon Miquel, A. Carnero Rosell, Tesla E. Jeltema, F. J. Castander, I. Sevilla-Noarbe, E. Rozo, C. Sánchez, Douglas L. Tucker, Kyler Kuehn, Brian Yanny, Enrique Fernández, Michael Schubnell, Daniel Gruen, Shantanu Desai, A. A. Plazas, T. M. C. Abbott, J. Carretero, E. Buckley-Geer, Alex Drlica-Wagner, Daniel A. Goldstein, Juan Garcia-Bellido, Y. Fang, A. Benoit-Lévy, E. J. Sanchez, T. N. Varga, E. M. Huff, E. Bertin, Jonathan Blazek, L. N. da Costa, D. W. Gerdes, Mathew Smith, Oliver Friedrich, J. P. Dietrich, Donnacha Kirk, Tenglin Li, Scott Dodelson, D. L. Burke, Chihway Chang, Alistair R. Walker, A. Alarcon, Carlos E. Cunha, M. Gatti, Marcelle Soares-Santos, C. B. D'Andrea, S. E. Kuhlmann, Erin Sheldon, Felipe Menanteau, Joseph J. Mohr, Michael Troxel, David J. James, M. E. C. Swanson, S. Allam, Robert A. Gruendl, Martin Crocce, Jennifer L. Marshall, Pablo Fosalba, J. Annis, Ashley J. Ross, K. Honscheid, Joshua A. Frieman, Brian Nord, Risa H. Wechsler, J. De Vicente, Flavia Sobreira, S. Samuroff, M. W. G. Johnson, M. March, Markus Michael Rau, Matt J. Jarvis, P. Vielzeuf, Yanxi Zhang, Tim Eifler, M. Carrasco Kind, Robert C. Nichol, Ofer Lahav, Enrique Gaztanaga, Vinu Vikram, Filipe B. Abdalla, V. Scarpine, David J. Brooks, Bhuvnesh Jain, Jack Elvin-Poole, Peter Melchior, E. Suchyta, Ben Hoyle, N. Kokron, M. A. G. Maia, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and DES

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), Physics and Astronomy (miscellaneous), FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Cosmology, cosmic rays, Consistency (statistics), 0103 physical sciences, Experiments in gravity, 010303 astronomy & astrophysics, STFC, QC, Astrophysics::Galaxy Astrophysics, Photometric redshift, Physics, COSMIC cancer database, 010308 nuclear & particles physics, RCUK, Astronomy, Redshift, Galaxy, Dark energy, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], cosmology, Jackknife resampling, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present galaxy-galaxy lensing measurements from 1321 sq. deg. of the Dark Energy Survey (DES) Year 1 (Y1) data. The lens sample consists of a selection of 660,000 red galaxies with high-precision photometric redshifts, known as redMaGiC, split into five tomographic bins in the redshift range $0.15 < z < 0.9$. We use two different source samples, obtained from the Metacalibration (26 million galaxies) and Im3shape (18 million galaxies) shear estimation codes, which are split into four photometric redshift bins in the range $0.2 < z < 1.3$. We perform extensive testing of potential systematic effects that can bias the galaxy-galaxy lensing signal, including those from shear estimation, photometric redshifts, and observational properties. Covariances are obtained from jackknife subsamples of the data and validated with a suite of log-normal simulations. We use the shear-ratio geometric test to obtain independent constraints on the mean of the source redshift distributions, providing validation of those obtained from other photo-$z$ studies with the same data. We find consistency between the galaxy bias estimates obtained from our galaxy-galaxy lensing measurements and from galaxy clustering, therefore showing the galaxy-matter cross-correlation coefficient $r$ to be consistent with one, measured over the scales used for the cosmological analysis. The results in this work present one of the three two-point correlation functions, along with galaxy clustering and cosmic shear, used in the DES cosmological analysis of Y1 data, and hence the methodology and the systematics tests presented here provide a critical input for that study as well as for future cosmological analyses in DES and other photometric galaxy surveys., 26 pages, 19 figures. Matches the version accepted by PRD

Published
2018

45. The Dark Energy Survey Data Release 1

Author

M. W. G. Johnson, M. Baumer, Tesla E. Jeltema, Eric H. Neilsen, Peter Nugent, Martin Crocce, S. Hamilton, M. Smith, Antonella Palmese, Nora Shipp, Eli S. Rykoff, F. Nogueira, L. Baruah, Daniel Gruen, Daniel Scolnic, Brian Nord, M. A. G. Maia, Brian Yanny, Matthias Klein, J. Song, R. R. Gupta, Yanxi Zhang, Arya Farahi, J. Carretero, M. March, Basilio X. Santiago, Tamara M. Davis, Shantanu Desai, Gary S. Da Costa, Jochen Weller, T. F. Eifler, Daniel A. Goldstein, J. M. Hislop, Joseph J. Mohr, R. C. Thomas, Erin Sheldon, David Brooks, M. E. C. Swanson, A. Porredon, A. Carnero Rosell, A. Saro, D. W. Gerdes, Xi Chen, Attila Kovács, Eric Morganson, R. C. Wolf, J. P. Dietrich, A. Kremin, T. M. C. Abbott, Richard G. McMahon, Jeremy Mould, J. D. Maloney, Jacobo Asorey, A. Benoit-Lévy, Hiranya V. Peiris, Ofer Lahav, Vinu Vikram, J. Lasker, E. J. Sanchez, B. Flaugher, S. Juneau, Risa H. Wechsler, Ricardo L. C. Ogando, Alistair R. Walker, A. A. Plazas, Ryan J. Foley, M. Carrasco Kind, W. C. Wester, Jennifer L. Marshall, D. L. Burke, Adam Amara, A. Scott, M.L. Sánchez, Jonathan Blazek, C. B. D'Andrea, Marcelle Soares-Santos, R. C. Smith, S. E. Kuhlmann, Ashley J. Ross, Robert C. Nichol, Ben Hoyle, G. Daues, M. Gower, C. J. Miller, M. D. Johnson, Wayne A. Barkhouse, Samuel Hinton, Felipe Menanteau, Kevin Reil, L. Nunes, F. Paz-Chinchón, David J. James, Tenglin Li, Scott Dodelson, Santiago Avila, Ann Elliott, Chihway Chang, T. Kacprzak, G. Tarle, Knut Olsen, R. Das, Ramon Miquel, Lyndsay Old, Juan Garcia-Bellido, E. Bertin, A. Roodman, Tommaso Giannantonio, Carlos E. Cunha, J. Poh, Pablo Fosalba, Enrique Gaztanaga, G. Gutierrez, J. DeRose, J. J. Thaler, Enrique Fernández, Will J. Percival, S. Allam, Paul M. Ricker, A. Pujol, Robert A. Gruendl, V. Scarpine, Andrew B. Pace, R. P. Rollins, K. Honscheid, Timothy A. McKay, Darren L. DePoy, G. Khullar, R. T. Li, Joe Zuntz, Alex Drlica-Wagner, R. Nikutta, Francisco J. Castander, C. Pond, Douglas L. Tucker, Don Petravick, W. G. Hartley, A. K. Vivas, R. Cawthon, Riccardo Campisano, D. A. Finley, D. Brout, Karl Glazebrook, Dragan Huterer, Peter Melchior, Elisabeth Krause, Mark Sullivan, Kyler Kuehn, V. C. Busti, P. Rooney, C. J. Conselice, Huan Lin, Marc Manera, J. Annis, Sebastian Bocquet, A. M. G. Koziol, M. L. Silveira, M. Fitzpatrick, Andrew R. Liddle, Alfredo Zenteno, O. Ballester, Steve Kent, Daniel Thomas, E. Buckley-Geer, Michael Troxel, A. K. Romer, Paul Martini, A. Fausti Neto, Keith Bechtol, C. Davis, J. Gschwend, Gary Bernstein, Hao-Yi Wu, Peter Doel, H. T. Diehl, J. De Vicente, J. P. Marriner, M. S. S. Gill, E. Suchyta, Niall MacCrann, August E. Evrard, Alexandre Refregier, Douglas N. Friedel, Albert Stebbins, M. Banerji, Joshua A. Frieman, I. Sevilla-Noarbe, J. A. Smith, T. McClintock, N. Kuropatkin, L. N. da Costa, Laia Cardiel-Sas, M. Sako, D. L. Hollowood, S. Serrano, David L. Nidever, Marcos Lima, Richard G. Kron, P. Lopez-Reyes, Filipe B. Abdalla, Bhuvnesh Jain, Matthew R. Becker, Flavia Sobreira, Institut d'Astrophysique de Paris ( IAP ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), DES, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), NOAO Data Lab, Abbott, T. M. C., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Asorey, J., Avila, S., Ballester, O., Banerji, M., Barkhouse, W., Baruah, L., Baumer, M., Bechtol, K., Becker, M. R., Benoit-Lévy, A., Bernstein, G. M., Bertin, E., Blazek, J., Bocquet, S., Brooks, D., Brout, Buckley-Geer, E., Burke, D. L., Busti, V., Campisano, R., Cardiel-Sas, L., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Castander, F. J., Cawthon, R., Chang, C., Chen, X., Conselice, C., Costa, G., Crocce, M., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Das, R., Daues, G., Davis, T. M., Davis, C., De Vicente, J., Depoy, D. L., Derose, J., Desai, S., Diehl, H. T., Dietrich, J. P., Dodelson, S., Doel, P., Drlica-Wagner, A., Eifler, T. F., Elliott, A. E., Evrard, A. E., Farahi, A., Fausti Neto, A., Fernandez, E., Finley, D. A., Flaugher, B., Foley, R. J., Fosalba, P., Friedel, D. N., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Gill, M. S. S., Glazebrook, K., Goldstein, D. A., Gower, M., Gruen, D., Gruendl, R. A., Gschwend, J., Gupta, R. R., Gutierrez, G., Hamilton, S., Hartley, W. G., Hinton, S. R., Hislop, J. M., Hollowood, D., Honscheid, K., Hoyle, B., Huterer, D., Jain, B., James, D. J., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Kacprzak, T., Kent, S., Khullar, G., Klein, M., Kovacs, A., Koziol, A. M. G., Krause, E., Kremin, A., Kron, R., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Lahav, O., Lasker, J., Li, T. S., Li, R. T., Liddle, A. R., Lima, M., Lin, H., López-Reyes, P., Maccrann, N., Maia, M. A. G., Maloney, J. D., Manera, M., March, M., Marriner, J., Marshall, J. L., Martini, P., Mcclintock, T., Mckay, T., Mcmahon, R. G., Melchior, P., Menanteau, F., Miller, C. J., Miquel, R., Mohr, J. J., Morganson, E., Mould, J., Neilsen, E., Nichol, R. C., Nogueira, F., Nord, B., Nugent, P., Nunes, L., Ogando, R. L. C., Old, L., Pace, A. B., Palmese, A., Paz-Chinchón, F., Peiris, H. V., Percival, W. J., Petravick, D., Plazas, A. A., Poh, J., Pond, C., Porredon, A., Pujol, A., Refregier, A., Reil, K., Ricker, P. M., Rollins, R. P., Romer, A. K., Roodman, A., Rooney, P., Ross, A. J., Rykoff, E. S., Sako, M., Sanchez, M. L., Sanchez, E., Santiago, B., Saro, A., Scarpine, V., Scolnic, D., Serrano, S., Sevilla-Noarbe, I., Sheldon, E., Shipp, N., Silveira, M. L., Smith, M., Smith, R. C., Smith, J. A., Soares-Santos, M., Sobreira, F., Song, J., Stebbins, A., Suchyta, E., Sullivan, M., Swanson, M. E. C., Tarle, G., Thaler, J., Thomas, D., Thomas, R. C., Troxel, M. A., Tucker, D. L., Vikram, V., Vivas, A. K., Walker, A. R., Wechsler, R. H., Weller, J., Wester, W., Wolf, R. C., Wu, H., Yanny, B., Zenteno, A., Zhang, Y., Zuntz, J., Des, Collaboration, Juneau, S., Fitzpatrick, M., Nikutta, R., Nidever, D., Olsen, K., Scott, A., and NOAO Data, Lab

Subjects
Astronomical Objects, catalog, [ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph], techniques: image processing, Astrophysics, astronomical databases: miscellaneous, 01 natural sciences, law.invention, photometric [techniques], techniques: photometric, law, Observatory, Astrophysics - Cosmology and Nongalactic Astrophysic, 010303 astronomy & astrophysics, [ PHYS.PHYS.PHYS-INS-DET ] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Physics, image processing [techniques], observations [cosmology], sky survey, Astrophysics - Solar and Stellar Astrophysics, astro-ph.CO, Astrophysics - Instrumentation and Methods for Astrophysics, sextractor, Data release, observation [cosmology], Astrophysics - Cosmology and Nongalactic Astrophysics, astro-ph.SR, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Aperture, astro-ph.GA, FOS: Physical sciences, Astronomy & Astrophysics, miscellaneou [astronomical databases], Telescope, surveys, Astrophysics - Astrophysics of Galaxie, 0103 physical sciences, survey, Astronomical And Space Sciences, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Instrumentation and Methods for Astrophysics (astro-ph.IM), Solar and Stellar Astrophysics (astro-ph.SR), Physical Chemistry (Incl. Structural), 010308 nuclear & particles physics, Organic Chemistry, representations, Astronomy and Astrophysics, miscellaneous [astronomical databases], angular masks, Astrophysics - Astrophysics of Galaxies, Galaxy, Data set, catalogs, cosmology: observations, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), Dark energy, Astrophysics - Instrumentation and Methods for Astrophysic, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], astro-ph.IM
Abstract

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

Published
2018

46. Dynamical Analysis of Three Distant Trans-Neptunian Objects with Similar Orbits

Author

H. T. Diehl, J. Annis, Vinu Vikram, Ramon Miquel, L. Zullo, Robert A. Gruendl, K. Honscheid, T. F. Eifler, Kevin Napier, E. Bertin, Juan Garcia-Bellido, T. M. C. Abbott, Pedro H. Bernardinelli, Tali Khain, I. Sevilla-Noarbe, V. Scarpine, W. G. Hartley, N. Kuropatkin, Kyler Kuehn, Michael Schubnell, E. J. Sanchez, L. N. da Costa, J. Carretero, E. Suchyta, Marcelle Soares-Santos, R. H. Schindler, Elisabeth Krause, David J. James, B. Flaugher, P. Doel, Filipe B. Abdalla, Felipe Menanteau, Juliette C. Becker, M. E. C. Swanson, O. Lahav, M. N. K. Smith, M. Sako, Kyle Franson, Fred C. Adams, Carlos E. Cunha, W. C. Wester, Larissa Markwardt, D. L. Hollowood, S. Desai, S. Hamilton, Joshua A. Frieman, G. Gutierrez, G. Tarle, A. A. Plazas, A. K. Romer, C. L. Davis, Salcedo Romero de Ávila, A. Carnero Rosell, M. Carrasco Kind, Yanxi Zhang, Brian Nord, Daniel Gruen, D. W. Gerdes, Flavia Sobreira, M. A. G. Maia, A. R. Walker, J. De Vicente, Hsing Wen Lin, Ricardo L. C. Ogando, D. Brooks, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and DES

Subjects
Earth and Planetary Astrophysics (astro-ph.EP), Physics, Solar System, 010504 meteorology & atmospheric sciences, FOS: Physical sciences, Astronomy and Astrophysics, Astrophysics, Phase plane, Orbital mechanics, 01 natural sciences, Omega, 13. Climate action, Space and Planetary Science, Planet, Neptune, 0103 physical sciences, Astrophysics::Earth and Planetary Astrophysics, Trans-Neptunian object, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], 010303 astronomy & astrophysics, Order of magnitude, QC, Astrophysics - Earth and Planetary Astrophysics, 0105 earth and related environmental sciences
Abstract

This paper reports the discovery and orbital characterization of two extreme trans-Neptunian objects (ETNOs), 2016 QV$_{89}$ and 2016 QU$_{89}$, which have orbits that appear similar to that of a previously known object, 2013 UH$_{15}$. All three ETNOs have semi-major axes $a\approx 172$ AU and eccentricities $e\approx0.77$. The angular elements $(i,\omega,\Omega)$ vary by 6, 15, and 49 deg, respectively between the three objects. The two new objects add to the small number of TNOs currently known to have semi-major axes between 150 and 250 AU, and serve as an interesting dynamical laboratory to study the outer realm of our Solar System. Using a large ensemble of numerical integrations, we find that the orbits are expected to reside in close proximity in the $(a,e)$ phase plane for roughly 100 Myr before diffusing to more separated values. We then explore other scenarios that could influence their orbits. With aphelion distances over 300 AU, the orbits of these ETNOs extend far beyond the classical Kuiper Belt, and an order of magnitude beyond Neptune. As a result, their orbital dynamics can be affected by the proposed new Solar System member, referred to as Planet Nine in this work. With perihelion distances of 35-40 AU, these orbits are also influenced by resonant interactions with Neptune. A full assessment of any possible, new Solar System planets must thus take into account this emerging class of TNOs., Comment: accepted to AJ

Published
2018

47. DES meets Gaia: discovery of strongly lensed quasars from a multiplet search

Author

David J. James, B. Flaugher, V. Scarpine, Adriano Agnello, N. D. Morgan, Karl Glazebrook, A. Carnero Rosell, T. M. C. Abbott, Veronica Motta, Matthew W. Auger, Nikolay Kuropatkin, R. H. Schindler, Michael Schubnell, Enrique Gaztanaga, E. Buckley-Geer, Shantanu Desai, E. J. Sanchez, Tommaso Treu, J. Gschwend, David Brooks, M. E. C. Swanson, Ofer Lahav, Paul L. Schechter, D. L. Burke, Marcos Lima, R. A. Bernstein, Flavia Sobreira, Timo Anguita, G. Gutierrez, I. Sevilla-Noarbe, Ricardo L. C. Ogando, Anupreeta More, E. Bertin, L. N. da Costa, A. A. Plazas, Philip J. Marshall, Brian Nord, K. Honscheid, Juan Garcia-Bellido, Alex Drlica-Wagner, Ramon Miquel, Christopher D. Fassnacht, C. B. D'Andrea, E. Suchyta, Marcelle Soares-Santos, Risa H. Wechsler, Douglas L. Tucker, Kyler Kuehn, Joshua A. Frieman, Robert A. Gruendl, M. March, J. Annis, Sherry H. Suyu, Filipe B. Abdalla, Felipe Menanteau, Gregory Tarle, Adam Amara, Carlos E. Cunha, Tim Eifler, Frederic Courbin, Takahiro Morishita, Huan Lin, K. Benoit-Lévy, K. Bechtol, K. Rojas, Georges Meylan, J. Carretero, Richard G. McMahon, M. Smith, Daniel Gruen, D. W. Gerdes, Marcio A. G. Maia, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and DES

Subjects
statistical [Methods], Cosmology and Nongalactic Astrophysics (astro-ph.CO), Stellar mass, FOS: Physical sciences, techniques: image processing, Astrophysics, Surveys, strong [Gravitational lensing], 01 natural sciences, law.invention, Einstein radius, surveys, law, 0103 physical sciences, image processing [Techniques], 010303 astronomy & astrophysics, Multiplet, Physics, methods: statistical, Simple lens, 010308 nuclear & particles physics, Astronomy, gravitational lensing: strong, Astronomy and Astrophysics, Quasar, Radius, Astrophysics - Astrophysics of Galaxies, Redshift, Lens (optics), Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We report the discovery, spectroscopic confirmation and first lens models of the first two, strongly lensed quasars from a combined search in WISE and Gaia over the DES footprint. The four-image lensWGD2038-4008 (r.a.=20:38:02.65, dec.=-40:08:14.64) has source- and lens-redshifts $z_{s}=0.777 \pm 0.001$ and $z_l = 0.230 \pm 0.002$ respectively. Its deflector has effective radius $R_{\rm eff} \approx 3.4^{\prime\prime}$, stellar mass $\log(M_{\star}/M_{\odot}) = 11.64^{+0.20}_{-0.43}$, and shows extended isophotal shape variation. Simple lens models yield Einstein radii $R_{\rm E}=(1.30\pm0.04)^{\prime\prime},$ axis ratio $q=0.75\pm0.1$ (compatible with that of the starlight) and considerable shear-ellipticity degeneracies. The two-image lensWGD2021-4115 (r.a.=20:21:39.45, dec.=--41:15:57.11) has $z_{s}=1.390\pm0.001$ and $z_l = 0.335 \pm 0.002$, and Einstein radius $R_{\rm E} = (1.1\pm0.1)^{\prime\prime},$ but higher-resolution imaging is needed to accurately separate the deflector and faint quasar image. We also show high-rank candidate doubles selected this way, some of which have been independently identified with different techniques, and discuss a DES+WISE quasar multiplet selection., Comment: MNRAS subm. 21/10/2017, awaiting reviewer selection. This paper has been approved by DES Collaboration-Wide Review

Published
2018

48. Baryon Content in a Sample of 91 Galaxy Clusters Selected by the South Pole Telescope at 0.2 < z < 1.25

Author

R. C. Smith, N. Kuropatkin, J. Carretero, L. N. da Costa, Michael McDonald, Elisabeth Krause, J. P. Dietrich, H. Israel, C. B. D'Andrea, S. E. Kuhlmann, David J. James, G. Tarle, Brian Nord, G. Gutierrez, Vinu Vikram, Carlos E. Cunha, Joseph J. Mohr, A. Saro, F. B. Abdalla, Esra Bulbul, Juan Garcia-Bellido, B. Flaugher, Felipe Menanteau, S. S. Murray, Sebastian Bocquet, J. Hlavacek-L., Matthias Klein, V. Strazzullo, Jochen Weller, F. J. Castander, E. Buckley-Geer, Flavia Sobreira, R. Capasso, T. M. C. Abbott, V. Scarpine, D. Rapetti, Shantanu Desai, A. A. Plazas, Nikhel Gupta, Ramon Miquel, B. Stalder, Mark Brodwin, S. Allam, Robert A. Gruendl, M. A. G. Maia, M. L. N. Ashby, E. J. Sanchez, K. Honscheid, Jennifer L. Marshall, Anthony H. Gonzalez, Gordon P. Garmire, Enrique Gaztanaga, Tesla E. Jeltema, John E. Carlstrom, R. H. Schindler, Matthew B. Bayliss, A. Stanford, M. E. C. Swanson, Michael Schubnell, I-Non Chiu, Alistair R. Walker, Bradford Benson, Lindsey Bleem, Yanxi Zhang, Marcelle Soares-Santos, Keren Sharon, Christian L. Reichardt, Tim Eifler, M. Smith, A. Carnero Rosell, Daniel Gruen, Peter Melchior, C. Stern, Alex Drlica-Wagner, D. W. Gerdes, David J. Brooks, J. Annis, E. Suchyta, August E. Evrard, Ofer Lahav, R. L. C. Ogando, A. Benoit-Lévy, A. K. Romer, Ralph P. Kraft, Marcos Lima, A. Roodman, Peter Doel, H. T. Diehl, E. Bertin, C. Davis, J. Gschwend, Kyler Kuehn, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), DES, SPT, Chiu, I., Mohr, J. J., Mcdonald, M., Bocquet, S., Desai, S., Klein, M., Israel, H., Ashby, M. L. N., Stanford, A., Benson, B. A., Brodwin, M., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Bayliss, M., Benoit-Lévy, A., Bertin, E., Bleem, L., Brooks, D., Buckley-Geer, Bulbul, E., Capasso, R., Carlstrom, J. E., Rosell, A. Carnero, Carretero, J., Castander, F. J., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., Diehl, H. T., Dietrich, J. P., Doel, P., Drlica-Wagner, A., Eifler, T. F., Evrard, A. E., Flaugher, B., García-Bellido, J., Garmire, G., Gaztanaga, E., Gerdes, D. W., Gonzalez, A., Gruen, D., Gruendl, R. A., Gschwend, J., Gupta, N., Gutierrez, G., Hlavacek-L, J., Honscheid, K., James, D. J., Jeltema, T., Kraft, R., Krause, E., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Lahav, O., Lima, M., Maia, M. A. G., Marshall, J. L., Melchior, P., Menanteau, F., Miquel, R., Murray, S., Nord, B., Ogando, R. L. C., Plazas, A. A., Rapetti, D., Reichardt, C. L., Romer, A. K., Roodman, A., Sanchez, E., Saro, A., Scarpine, V., Schindler, R., Schubnell, M., Sharon, K., Smith, R. C., Smith, M., Soares-Santos, M., Sobreira, F., Stalder, B., Stern, C., Strazzullo, V., Suchyta, E., Swanson, M. E. C., Tarle, G., Vikram, V., Walker, A. R., Weller, J., and Zhang, Y.

Subjects
galaxies: clusters: individual, galaxies: clusters: intracluster medium, Stellar mass, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Virial theorem, Intracluster medium, 0103 physical sciences, galaxies: clusters: general, Astrophysics - Cosmology and Nongalactic Astrophysics, clusters: general [galaxies], 010303 astronomy & astrophysics, Galaxy cluster, Astrophysics::Galaxy Astrophysics, QB, Physics, clusters: individual [galaxies], 010308 nuclear & particles physics, Astronomy, Astronomy and Astrophysics, Redshift, Baryon, South Pole Telescope, clusters: intracluster medium [galaxies], 13. Climate action, Space and Planetary Science, Spectral energy distribution, Astrophysics::Earth and Planetary Astrophysics, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Abstract

We estimate total mass ($M_{500}$), intracluster medium (ICM) mass ($M_{\mathrm{ICM}}$) and stellar mass ($M_{\star}$) in a Sunyaev-Zel'dovich effect (SZE) selected sample of 91 galaxy clusters with masses $M_{500}\gtrsim2.5\times10^{14}M_{\odot}$ and redshift $0.2 < z < 1.25$ from the 2500 deg$^2$ South Pole Telescope SPT-SZ survey. The total masses $M_{500}$ are estimated from the SZE observable, the ICM masses $M_{\mathrm{ICM}}$ are obtained from the analysis of $Chandra$ X-ray observations, and the stellar masses $M_{\star}$ are derived by fitting spectral energy distribution templates to Dark Energy Survey (DES) $griz$ optical photometry and $WISE$ or $Spitzer$ near-infrared photometry. We study trends in the stellar mass, the ICM mass, the total baryonic mass and the cold baryonic fraction with cluster mass and redshift. We find significant departures from self-similarity in the mass scaling for all quantities, while the redshift trends are all statistically consistent with zero, indicating that the baryon content of clusters at fixed mass has changed remarkably little over the past $\approx9$ Gyr. We compare our results to the mean baryon fraction (and the stellar mass fraction) in the field, finding that these values lie above (below) those in cluster virial regions in all but the most massive clusters at low redshift. Using a simple model of the matter assembly of clusters from infalling groups with lower masses and from infalling material from the low density environment or field surrounding the parent halos, we show that the measured mass trends without strong redshift trends in the stellar mass scaling relation could be explained by a mass and redshift dependent fractional contribution from field material. Similar analyses of the ICM and baryon mass scaling relations provide evidence for the so-called "missing baryons" outside cluster virial regions., Comment: Accepted by MNRAS with further clarification and discussion of the mass calibration added

Published
2018

49. Exploiting cross correlations and joint analyses

Author

R. de Putter, K. Honscheid, Tzu-Ching Chang, Jeffrey A. Newman, Eli S. Rykoff, Steven W. Allen, Brice Ménard, Bradford Benson, A. Vallinotto, Jason Rhodes, Eric V. Linder, Brian Nord, Scott Dodelson, Olivier Doré, Eduardo Rozo, and David H. Weinberg

Subjects
Physics, Theoretical physics, Field (physics), General relativity, Physics beyond the Standard Model, Dark energy, Astronomy and Astrophysics, Lambda-CDM model, Covariance, Cosmology, Metric expansion of space
Abstract

The nature of the dark energy thought to be causing the accelerating expansion of the Universe is one of the most compelling questions in all of science. Any of the explanations for the accelerated expansion, whether a new field, a negative pressure fluid, or a modification to General Relativity will signal new physics and have a profound effect on our understanding of the Universe. The current observational constraints on dark energy and modifications to gravity still allow for a large range of models and theoretical explanations. Given the importance of dark energy, we must attack the problem from a variety of angles, taking advantage of cross-correlations between and joint analyses of different probes, missions, wavelengths, and surveys, to enable the most stringent cosmological constraints.

Published
2015

50. OzDES multifibre spectroscopy for the Dark Energy Survey: 3-yr results and first data release

Author

D. Mudd, V. Scarpine, M. E. C. Swanson, R. R. Gupta, John Marriner, Tim Eifler, F. J. Castander, Peter Doel, Mark Sullivan, Edward Macaulay, A. G. Kim, Eve Kovacs, B. Flaugher, M. Smith, I. Sevilla-Noarbe, N. E. Sommer, Daniel Gruen, C. Lidman, Ramon Miquel, T. M. C. Abbott, Jeremy Mould, Geraint F. Lewis, Michael Schubnell, Samuel Hinton, S. A. Uddin, R. L. C. Ogando, A. Benoit-Lévy, N. Kuropatkin, Rob Sharp, M. Sako, G. Gutierrez, M. Childress, David J. James, Stephanie R. Bernard, Juan Garcia-Bellido, L. N. da Costa, David Brooks, Pablo Fosalba, M. A. G. Maia, E. Buckley-Geer, Fang Yuan, Flavia Sobreira, M. Carrasco Kind, Robert C. Nichol, Richard Kessler, J. Annis, Karl Glazebrook, A. A. Plazas, Alistair R. Walker, Ryan J. Foley, Nick Seymour, F. Ostrovski, M. Banerji, Joshua A. Frieman, Marcelle Soares-Santos, M. March, Huan Lin, Jennifer L. Marshall, Brad E. Tucker, E. Suchyta, August E. Evrard, G. Tarle, Rafe Schindler, Anais Möller, E. Bertin, Kevin Reil, Kyler Kuehn, W. C. Wester, S. L. Reed, Tianjun Li, Marcos Lima, D. L. Burke, Daniel Scolnic, Daniela Carollo, David Parkinson, H. M. Spinka, Eric Morganson, S. Allam, Robert A. Gruendl, J. Gschwend, Brian Nord, D. Lagattuta, Jacobo Asorey, J. K. Hoormann, C. B. D'Andrea, A. K. Romer, S. E. Kuhlmann, Paul Martini, F. B. Abdalla, Bonnie Zhang, J. Carretero, Tamara M. Davis, Daniel A. Goldstein, Richard G. McMahon, Eli S. Rykoff, E. J. Sanchez, A. L. King, Carlos E. Cunha, A. Carnero Rosell, Felipe Menanteau, Daniel Muthukrishna, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche Astrophysique de Lyon (CRAL), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), DES, École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Institut d'Astrophysique de Paris ( IAP ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Physique Nucléaire de Lyon ( IPNL ), Université Claude Bernard Lyon 1 ( UCBL ), and Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
[ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph], AST-1138766, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, law.invention, Telescope, law, 0103 physical sciences, Astrophysics::Solar and Stellar Astrophysics, dark energy, 010303 astronomy & astrophysics, Spectrograph, STFC, Astrophysics::Galaxy Astrophysics, Physics, 010308 nuclear & particles physics, RCUK, Astronomy, Astronomy and Astrophysics, Quasar, supernovae: general – dark energy, Redshift, Galaxy, Supernova, 13. Climate action, Space and Planetary Science, Magnitude (astronomy), Dark energy, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], general [supernovae], uploaded-in-3-months-elsewhere
Abstract

International audience; We present results for the first three years of OzDES, a six year programme to obtain redshifts for objects in the Dark Energy Survey (DES) supernova fields using the 2dF fibre positioner and AAOmega spectrograph on the Anglo-Australian Telescope. OzDES is a multi-object spectroscopic survey targeting multiple types of targets at multiple epochs over a multiyear baseline and is one of the first multi-object spectroscopic surveys to dynamically include transients into the target list soon after their discovery. At the end of three years, OzDES has spectroscopically confirmed almost 100 supernovae, and has measured redshifts for 17 000 objects, including the redshifts of 2566 supernova hosts. We examine how our ability to measure redshifts for targets of various types depends on signal-to-noise ratio (S/N), magnitude and exposure time, finding that our redshift success rate increases significantly at a S/N of 2–3 per 1-Å bin. We also find that the change in S/N with exposure time closely matches the Poisson limit for stacked exposures as long as 10 h. We use these results to predict the redshift yield of the full OzDES survey, as well as the potential yields of future surveys on other facilities such as (i.e. the 4-m Multi-Object Spectroscopic Telescope, the Subaru Prime Focus Spectrograph and the Maunakea Spectroscopic Explorer). This work marks the first OzDES data release, comprising 14 693 redshifts. OzDES is on target to obtain over 30 000 redshifts over the 6-yr duration of the survey, including a yield of approximately 5700 supernova host-galaxy redshifts.

Published
2017

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