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1. Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

Author

Huppenkothen, D., Ntampaka, M., Ho, M., Fouesneau, M., Nord, B., Peek, J. E. G., Walmsley, M., Wu, J. F., Avestruz, C., Buck, T., Brescia, M., Finkbeiner, D. P., Goulding, A. D., Kacprzak, T., Melchior, P., Pasquato, M., Ramachandra, N., Ting, Y. -S., van de Ven, G., Villar, S., Villar, V. A., and Zinger, E.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning
Abstract

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method., Comment: 14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Society

Published
2023

2. The Dark Energy Survey Six-Year Calibration Star Catalog

Author

Rykoff, E. S., Tucker, D. L., Burke, D. L., Allam, S. S., Bechtol, K., Bernstein, G. M., Brout, D., Gruendl, R. A., Lasker, J., Smith, J. A., Wester, W. C., Yanny, B., Abbott, T. M. C., Aguena, M., Alves, O., Andrade-Oliveira, F., Annis, J., Bacon, D., Bertin, E., Brooks, D., Rosell, A. Carnero, Carretero, J., Castander, F. J., Choi, A., da Costa, L. N., Pereira, M. E. S., Davis, T. M., De Vicente, J., Diehl, H. T., Doel, P., Drlica-Wagner, A., Everett, S., Ferrero, I., Frieman, J., García-Bellido, J., Giannini, G., Gruen, D., Gutierrez, G., Hinton, S. R., Hollowood, D. L., James, D. J., Kuehn, K., Lahav, O., Marshall, J. L., Mena-Fernández, J., Menanteau, F., Myles, J., Nord, B. D., Ogando, R. L. C., Palmese, A., Pieres, A., Malagón, A. A. Plazas, Raveri, M., Rodgríguez-Monroy, M., Sanchez, E., Santiago, B., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Suchyta, E., Swanson, M. E. C., Varga, T. N., Vincenzi, M., Walker, A. R., Weaverdyck, N., and Wiseman, P.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

This Technical Note presents a catalog of calibrated reference stars that was generated by the Forward Calibration Method (FGCM) pipeline (arXiv:1706.01542) as part of the FGCM photometric calibration of the full Dark Energy Survey (DES) 6-Year data set (Y6). This catalog provides DES grizY magnitudes for 17 million stars with i-band magnitudes mostly in the range 16 < i < 21 spread over the full DES footprint covering 5000 square degrees over the Southern Galactic Cap at galactic latitudes b < -20 degrees (plus a few outlying fields disconnected from the main survey footprint). These stars are calibrated to a uniformity of better than 1.8 milli-mag (0.18%) RMS over the survey area. The absolute calibration of the catalog is computed with reference to the STISNIC.007 spectrum of the Hubble Space Telescope CalSpec standard star C26202; including systematic errors, the absolute flux system is known at the approximately 1% level. As such, these stars provide a useful reference catalog for calibrating grizY-band or grizY-like band photometry in the Southern Hemisphere, particularly for observations within the DES footprint., Comment: 21 pages, 15 figures, Fermilab Technical Note. Official Data Access Site: https://des.ncsa.illinois.edu/releases/other ; Temporary Data Access Site: https://data.darkenergysurvey.org/public_calib/DES_6yr_CalibStarCat/index.html

Published
2023

3. DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection

Author

Ćiprijanović, A, Lewis, A, Pedro, K, Madireddy, S, Nord, B, Perdue, GN, and Wild, SM

Subjects
Information and Computing Sciences, Machine Learning, domain adaptation, convolutional neural networks, deep learning, model robustness, galaxy morphological classification, sky surveys, Applied computing, Machine learning
Abstract

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to 40 % on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.

Published
2023

4. DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

Author

Ćiprijanović, A., Lewis, A., Pedro, K., Madireddy, S., Nord, B., Perdue, G. N., and Wild, S. M.

Subjects
Astrophysics - Astrophysics of Galaxies, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, \textit{DeepAstroUDA}, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities ($3$-class and $10$-class problems), with anomaly detection: 1) datasets created after different numbers of observing years from a single survey (LSST mock data of $1$ and $10$ years of observations); 2) data from different surveys (SDSS and DECaLS); and 3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of domain adaptation between very discrepant observational datasets. \textit{DeepAstroUDA} is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to $40\%$ on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset., Comment: Accepted in Machine Learning Science and Technology (MLST); 24 pages, 14 figures

Published
2023

5. DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning

Author

Morgan, Robert, Nord, B., Bechtol, K., Möller, A., Hartley, W. G., Birrer, S., González, S. J., Martinez, M., Gruendl, R. A., Buckley-Geer, E. J., Shajib, A. J., Rosell, A. Carnero, Lidman, C., Collett, T., Abbott, T. M. C., Aguena, M., Andrade-Oliveira, F., Annis, J., Bacon, D., Bocquet, S., Brooks, D., Burke, D. L., Kind, M. Carrasco, Carretero, J., Castander, F. J., Conselice, C., da Costa, L. N., Costanzi, M., De Vicente, J., Desai, S., Doel, P., Everett, S., Ferrero, I., Flaugher, B., Friedel, D., Frieman, J., García-Bellido, J., Gaztanaga, E., Gruen, D., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., Kuehn, K., Kuropatkin, N., Lahav, O., Lima, M., Menanteau, F., Miquel, R., Palmese, A., Paz-Chinchón, F., Pereira, M. E. S., Pieres, A., Malagón, A. A. Plazas, Prat, J., Rodriguez-Monroy, M., Romer, A. K., Roodman, A., Sanchez, E., Scarpine, V., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., and Varga, T. N.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain and 5-10 LSNe in total while next-generation experiments are expected to contain several hundreds to a few thousands of these systems. We search for these systems in observed Dark Energy Survey (DES) 5-year SN fields -- 10 3-sq. deg. regions of sky imaged in the $griz$ bands approximately every six nights over five years. To perform the search, we utilize the DeepZipper approach: a multi-branch deep learning architecture trained on image-level simulations of LSNe that simultaneously learns spatial and temporal relationships from time series of images. We find that our method obtains a LSN recall of 61.13% and a false positive rate of 0.02% on the DES SN field data. DeepZipper selected 2,245 candidates from a magnitude-limited ($m_i$ $<$ 22.5) catalog of 3,459,186 systems. We employ human visual inspection to review systems selected by the network and find three candidate LSNe in the DES SN fields., Comment: Accepted by ApJ

Published
2022
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6. DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification

Author

Morgan, Robert, Nord, B., Bechtol, K., González, S. J., Buckley-Geer, E., Möller, A., Park, J. W., Kim, A. G., Birrer, S., Aguena, M., Annis, J., Bocquet, S., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cawthon, R., da Costa, L. N., Davis, T. M., De Vicente, J., Doel, P., Ferrero, I., Friedel, D., Frieman, J., García-Bellido, J., Gatti, M., Gaztanaga, E., Giannini, G., Gruen, D., Gruendl, R. A., Gutierrez, G., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Kuropatkin, N., Maia, M. A. G., Miquel, R., Palmese, A., Paz-Chinchón, F., Pereira, M. E. S., Pieres, A., Malagón, A. A. Plazas, Reil, K., Roodman, A., Sanchez, E., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., and To, C.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey datasets, we designed ZipperNet, a multi-branch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory (LSTM) layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories -- no lens, galaxy-galaxy lens, lensed type Ia supernova, lensed core-collapse supernova -- within high-fidelity simulations of three cosmic survey data sets -- the Dark Energy Survey (DES), Rubin Observatory's Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like dataset, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79\% accuracy, and demonstrates similarly high performance for LSNe 1-2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments., Comment: Published in ApJ

Published
2021
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7. Robustness of deep learning algorithms in astronomy -- galaxy morphology studies

Author

Ćiprijanović, A., Kafkes, D., Perdue, G. N., Pedro, K., Snyder, G., Sánchez, F. J., Madireddy, S., Wild, S. M., and Nord, B.

Subjects
Astrophysics - Astrophysics of Galaxies, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models., Comment: Accepted in: Fourth Workshop on Machine Learning and the Physical Sciences (35th Conference on Neural Information Processing Systems; NeurIPS2021); final version

Published
2021

8. The DES Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5,000 Sq. Deg. Footprint

Author

O'Donnell, J. H., Wilkinson, R. D., Diehl, H. T., Aros-Bunster, C., Bechtol, K., Birrer, S., Buckley-Geer, E. J., Rosell, A. Carnero, Kind, M. Carrasco, da Costa, L. N., Lozano, S. J. Gonzalez, Gruendl, R. A., Hilton, M., Lin, H., Lindgren, K. A., Martin, J., Pieres, A., Rykoff, E. S., Sevilla-Noarbe, I., Sheldon, E., Sifón, C., Tucker, D. L., Yanny, B., Abbott, T. M. C., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bertin, E., Brooks, D., Burke, D. L., Carretero, J., Costanzi, M., De Vicente, J., Desai, S., Dietrich, J. P., Eckert, K., Everett, S., Ferrero, I., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gschwend, J., Gill, M. S. S., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Jeltema, T., Kuehn, K., Lahav, O., Lima, M., Maia, M. A. G., Marshall, J. L., Melchior, P., Menanteau, F., Miquel, R., Morgan, R., Nord, B., Ogando, R. L. C., Paz-Chinchón, F., Pereira, M. E. S., Malagón, A. A. Plazas, Rodriguez-Monroy, M., Romer, A. K., Roodman, A., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., To, C., and Varga, T. N.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We report the combined results of eight searches for strong gravitational lens systems in the full 5,000 sq. deg. of Dark Energy Survey (DES) observations. The observations accumulated by the end of the third observing season fully covered the DES footprint in 5 filters (grizY), with an $i-$band limiting magnitude (at $10\sigma$) of 23.44. In four searches, a list of potential candidates was identified using a color and magnitude selection from the object catalogs created from the first three observing seasons. Three other searches were conducted at the locations of previously identified galaxy clusters. Cutout images of potential candidates were then visually scanned using an object viewer. An additional set of candidates came from a data-quality check of a subset of the color-coadd "tiles" created from the full DES six-season data set. A short list of the most promising strong lens candidates was then numerically ranked according to whether or not we judged them to be bona fide strong gravitational lens systems. These searches discovered a diverse set of 247 strong lens candidate systems, of which 81 are identified for the first time. We provide the coordinates, magnitudes, and photometric properties of the lens and source objects, and an estimate of the Einstein radius for 81 new systems and 166 previously reported. This catalog will be of use for selecting interesting systems for detailed follow-up, studies of galaxy cluster and group mass profiles, as well as a training/validation set for automated strong lens searches., Comment: 38 pages, 17 figures, 4 tables, accepted by ApJS

Published
2021
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9. Finding quadruply imaged quasars with machine learning. I. Methods

Author

Akhazhanov, A., More, A., Amini, A., Hazlett, C., Treu, T., Birrer, S., Shajib, A., Schechter, P., Lemon, C., Nord, B., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Choi, A., Conselice, C., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Dietrich, J. P., Doel, P., Everett, S., Ferrero, I., Finley, D. A., Flaugher, B., Frieman, J., García-Bellido, J., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kim, A. G., Kuehn, K., Kuropatkin, N., Lahav, O., Lima, M., Lin, H., Maia, M. A. G., March, M., Menanteau, F., Miquel, R., Morgan, R., Palmese, A., Paz-Chinchón, F., Pieres, A., Malagón, A. A. Plazas, Sanchez, E., Scarpine, V., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Varga, T. N., and Weller, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky -- only a few tens are known to date -- and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic "needle in a haystack" problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range 0.86 to 0.89. Recall is close to 100% down to total magnitude i~21 indicating high completeness, while precision declines from 85% to 70% in the range i~17-21. The methods are extremely fast: training on 2 million samples takes 20 hours on a GPU machine, and 10^8 multi-band cutouts can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads., Comment: 17 pages, 14 figures, submitted to MNRAS

Published
2021
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10. DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification

Author

Morgan, R, Nord, B, Bechtol, K, González, SJ, Buckley-Geer, E, Möller, A, Park, JW, Kim, AG, Birrer, S, Aguena, M, Annis, J, Bocquet, S, Brooks, D, Rosell, A Carnero, Kind, M Carrasco, Carretero, J, Cawthon, R, da Costa, LN, Davis, TM, De Vicente, J, Doel, P, Ferrero, I, Friedel, D, Frieman, J, García-Bellido, J, Gatti, M, Gaztanaga, E, Giannini, G, Gruen, D, Gruendl, RA, Gutierrez, G, Hollowood, DL, Honscheid, K, James, DJ, Kuehn, K, Kuropatkin, N, Maia, MAG, Miquel, R, Palmese, A, Paz-Chinchón, F, Pereira, MES, Pieres, A, Malagón, AA Plazas, Reil, K, Roodman, A, Sanchez, E, Smith, M, Suchyta, E, Swanson, MEC, Tarle, G, and To, C

Subjects
Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Physical Chemistry (incl. Structural), Astronomy & Astrophysics
Abstract

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey data sets, we designed ZipperNet, a multibranch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories - no lens, galaxy-galaxy lens, lensed Type-Ia supernova, lensed core-collapse supernova - within high-fidelity simulations of three cosmic survey data sets: the Dark Energy Survey, Rubin Observatory's Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like data set, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1-2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments.

Published
2022

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

Author

Ćiprijanović, A., Kafkes, D., Downey, K., Jenkins, S., Perdue, G. N., Madireddy, S., Johnston, T., Snyder, G. F., and Nord, B.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
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., Comment: Submitted to MNRAS; 21 pages, 9 figures, 9 tables

Published
2021
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12. Domain adaptation techniques for improved cross-domain study of galaxy mergers

Author

Ćiprijanović, A., Kafkes, D., Jenkins, S., Downey, K., Perdue, G. N., Madireddy, S., Johnston, T., and Nord, B.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on images from one domain does not guarantee satisfactory performance on new images from a different domain. The ability to share cross-domain knowledge is the main advantage of modern deep domain adaptation techniques. Here we demonstrate the use of two techniques - Maximum Mean Discrepancy (MMD) and adversarial training with Domain Adversarial Neural Networks (DANN) - for the classification of distant galaxy mergers from the Illustris-1 simulation, where the two domains presented differ only due to inclusion of observational noise. We show how the addition of either MMD or adversarial training greatly improves the performance of the classifier on the target domain when compared to conventional machine learning algorithms, thereby demonstrating great promise for their use in astronomy., Comment: Accepted in: Machine Learning and the Physical Sciences - Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS); final version

Published
2020

13. DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks

Author

Ćiprijanović, A., Snyder, G. F., Nord, B., and Peek, J. E. G.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computer Vision and Pattern Recognition
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., Comment: 17 pages, 8 figures, submitted to Astronomy & Computing

Published
2020

14. Observation and Confirmation of Nine Strong Lensing Systems in Dark Energy Survey Year 1 Data

Author

Nord, B., Buckley-Geer, E., Lin, H., Kuropatkin, N., Collett, T., Tucker, D. L., Diehl, H. T., Agnello, A., Amara, A., Abbott, T. M. C., Allam, S., Annis, J., Avila, S., Bechtol, K., Brooks, D., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., da Costa, L. N., Davis, C., De Vicente, J., Doel, P., Eifler, T. F., Evrard, A. E., Fernandez, E., Flaugher, B., Fosalba, P., Frieman, J., Bellido, J. Garcıa, Gaztanaga, E., Gruen, D., Gruendl, R. A., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Lahav, O., Lima, M., Maia, M. A. G., March, M., Marshall, J. L., Melchior, P., Menanteau, F., Miquel, R., Plazas, A. A., Romer, A. K., Roodman, A., Rykoff, E. S., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., and Zhang, Y.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We describe the observation and confirmation of \nbconfirmtext\ new strong gravitational lenses discovered in Year 1 data from the Dark Energy Survey (DES). We created candidate lists based on a) galaxy group and cluster samples and b) 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 spectroscopic follow-up of 21 of these candidates. Through analysis of this spectroscopic follow-up data, we confirmed nine new lensing systems and rejected 2 candidates, but the analysis was inconclusive on 10 candidates. For each of the confirmed systems, we report measured spectroscopic properties, estimated \einsteinradiussub, and estimated enclosed masses. The sources that we targeted have an i-band surface brightness range of iSB ~ 22 - 24 mag arcsec^2 and a spectroscopic redshift range of zspec ~0.8 - 2.6. The lens galaxies have a photometric redshift range of zlens ~ 0.3 - 0.7. The lensing systems range in image-lens separation 2 - 9 arcsec and in enclosed mass 10^12 - 10^13 Msol.

Published
2019
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15. Response to NITRD, NCO, NSF Request for Information on 'Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan'

Author

Amundson, J., Annis, J., Avestruz, C., Bowring, D., Caldeira, J., Cerati, G., Chang, C., Dodelson, S., Elvira, D., Farahi, A., Genser, K., Gray, L., Gutsche, O., Harris, P., Kinney, J., Kowalkowski, J. B., Kutschke, R., Mrenna, S., Nord, B., Para, A., Pedro, K., Perdue, G. N., Scheinker, A., Spentzouris, P., John, J. St., Tran, N., Trivedi, S., Trouille, L., Wu, W. L. K., and Bom, C. R.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, Physics - Physics and Society
Abstract

We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.

Published
2019

16. Petabytes to Science

Author

Bauer, Amanda E., Bellm, Eric C., Bolton, Adam S., Chaudhuri, Surajit, Connolly, A. J., Cruz, Kelle L., Desai, Vandana, Drlica-Wagner, Alex, Economou, Frossie, Gaffney, Niall, Kavelaars, J., Kinney, J., Li, Ting S., Lundgren, B., Margutti, R., Narayan, G., Nord, B., Norman, Dara J., O'Mullane, W., Padhi, S., Peek, J. E. G., Schafer, C., Schwamb, Megan E., Smith, Arfon M., Tollerud, Erik J., Weijmans, Anne-Marie, and Szalay, Alexander S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

A Kavli foundation sponsored workshop on the theme \emph{Petabytes to Science} was held 12$^{th}$ to 14$^{th}$ of February 2019 in Las Vegas. The aim of the this workshop was to discuss important trends and technologies which may support astronomy. We also tackled how to better shape the workforce for the new trends and how we should approach education and public outreach. This document was coauthored during the workshop and edited in the weeks after. It comprises the discussions and highlights many recommendations which came out of the workshop. We shall distill parts of this document and formulate potential white papers for the decadal survey., Comment: 70 pages 2 figures - this contains a few fixes

Published
2019

17. A Search for Optical Emission from Binary-Black-Hole Merger GW170814 with the Dark Energy Camera

Author

Doctor, Z., Kessler, R., Herner, K., Palmese, A., Soares-Santos, M., Annis, J., Brout, D., Holz, D. E., Sako, M., Rest, A., Cowperthwaite, P., Berger, E., Foley, R. J., Conselice, C. J., Gill, M. S. S., Allam, S., Balbinot, E., Butler, R. E., Chen, H. -Y., Chornock, R., Cook, E., Diehl, H. T., Farr, B., Fong, W., Frieman, J., Fryer, C., García-Bellido, J., Margutti, R., Marshall, J. L., Matheson, T., Metzger, B. D., Nicholl, M., Paz-Chinchón, F., Salim, S., Sauseda, M., Secco, L. F., Smith, N., Smith, R. C., Vivas, A. K., Tucker, D. L., Abbott, T. M. C., Avila, S., Bechtol, K., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., D'Andrea, C. B., da Costa, L. N., De Vicente, J., Desai, S., Doel, P., Flaugher, B., Fosalba, P., Gaztanaga, E., Gerdes, D. W., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Jeltema, T., Kent, S., Kuehn, K., Kuropatkin, N., Lahav, O., Lima, M., Maia, M. A. G., March, M., Menanteau, F., Miller, C. J., Miquel, R., Neilsen, E., Nord, B., Ogando, R. L. C., Plazas, A. A., Roodman, A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Walker, A. R., and Wester, W.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Binary black hole (BBH) mergers found by the LIGO and Virgo detectors are of immense scientific interest to the astrophysics community, but are considered unlikely to be sources of electromagnetic emission. To test whether they have rapidly fading optical counterparts, we used the Dark Energy Camera to perform an $i$-band search for the BBH merger GW170814, the first gravitational wave detected by three interferometers. The 87-deg$^2$ localization region (at 90\% confidence) centered in the Dark Energy Survey (DES) footprint enabled us to image 86\% of the probable sky area to a depth of $i\sim 23$ mag and provide the most comprehensive dataset to search for EM emission from BBH mergers. To identify candidates, we perform difference imaging with our search images and with templates from pre-existing DES images. The analysis strategy and selection requirements were designed to remove supernovae and to identify transients that decline in the first two epochs. We find two candidates, each of which is spatially coincident with a star or a high-redshift galaxy in the DES catalogs, and they are thus unlikely to be associated with GW170814. Our search finds no candidates associated with GW170814, disfavoring rapidly declining optical emission from BBH mergers brighter than $i\sim 23$ mag ($L_{\rm optical} \sim 5\times10^{41}$ erg/s) 1-2 days after coalescence. In terms of GW sky map coverage, this is the most complete search for optical counterparts to BBH mergers to date, Comment: 11 pages, 3 figures

Published
2018
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18. Finding high-redshift strong lenses in DES using convolutional neural networks

Author

Jacobs, C., Collett, T., Glazebrook, K., McCarthy, C., Qin, A. K., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Bechtol, K., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Diehl, H. T., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., Bellido, J. García, Gaztanaga, E., Gerdes, D. W., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Li, T. S., Lima, M., Lin, H., Maia, M. A. G., Martini, P., Miller, C. J., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Vikram, V., Walker, A. R., Zhang, Y., and Zuntz, J.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
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., Comment: Accepted for publication in MNRAS

Published
2018
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19. First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Photometric Pipeline and Light Curve Data Release

Author

Brout, D., Sako, M., Scolnic, D., Kessler, R., D'Andrea, C. B., Davis, T. M., Hinton, S. R., Kim, A. G., Lasker, J., Macaulay, E., Möller, A., Nichol, R. C., Smith, M., Sullivan, M., Wolf, R. C., Allam, S., Bassett, B. A., Brown, P., Castander, F. J., Childress, M., Foley, R. J., Galbany, L., Herner, K., Kasai, E., March, M., Morganson, E., Nugent, P., Pan, Y. -C., Thomas, R. C., Tucker, B. E., Wester, W., Abbott, T. M. C., Annis, J., Avila, S., Bertin, E., Brooks, D., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Crocce, M., Cunha, C. E., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Diehl, H. T., Doel, P., Eifler, T. F., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Li, T. S., Lima, M., Marshall, J. L., Martini, P., Miquel, R., Nord, B., Plazas, A. A., Roodman, A., Rykoff, E. S., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, D. L., Walker, A. R., Yanny, B., and Zhang, Y.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
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
2018
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20. First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters

Author

Abbott, T. M. C., Allam, S., Andersen, P., Angus, C., Asorey, J., Avelino, A., Avila, S., Bassett, B. A., Bechtol, K., Bernstein, G. M., Bertin, E., Brooks, D., Brout, D., Brown, P., Burke, D. L., Calcino, J., Rosell, A. Carnero, Carollo, D., Kind, M. Carrasco, Carretero, J., Casas, R., Castander, F. J., Cawthon, R., Challis, P., Childress, M., Clocchiatti, A., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., Davis, T. M., De Vicente, J., DePoy, D. L., Desai, S., Diehl, H. T., Doel, P., Drlica-Wagner, A., Eifler, T. F., Evrard, A. E., Fernandez, E., Filippenko, A. V., Finley, D. A., Flaugher, B., Foley, R. J., Fosalba, P., Frieman, J., Galbany, L., Garcia-Bellido, J., Gaztanaga, E., Giannantonio, T., Glazebrook, K., Goldstein, D. A., Gonzalez-Gaitan, S., Gruen, D., Gruendl, R. A., Gschwend, J., Gupta, R. R., Gutierrez, G., Hartley, W. G., Hinton, S. R., Hollowood, D. L., Honscheid, K., Hoormann, J. K., Hoyle, B., James, D. J., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Kasai, E., Kent, S., Kessler, R., Kim, A. G., Kirshner, R. P., Kovacs, E., Krause, E., Kron, R., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Lahav, O., Lasker, J., Lewis, G. F., Li, T. S., Lidman, C., Lima, M., Lin, H., Macaulay, E., Maia, M. A. G., Mandel, K. S., March, M., Marriner, J., Marshall, J. L., Martini, P., Menanteau, F., Miller, C. J., Miquel, R., Miranda, V., Mohr, J. J., Morganson, E., Muthukrishna, D., Möller, A., Neilsen, E., Nichol, R. C., Nord, B., Nugent, P., Ogando, R. L. C., Palmese, A., Pan, Y. -C., Plazas, A. A., Pursiainen, M., Romer, A. K., Roodman, A., Rozo, E., Rykoff, E. S., Sako, M., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Scolnic, D., Serrano, S., Sevilla-Noarbe, I., Sharp, R., Smith, M., Soares-Santos, M., Sobreira, F., Sommer, N. E., Spinka, H., Suchyta, E., Sullivan, M., Swann, E., Tarle, G., Thomas, D., Thomas, R. C., Troxel, M. A., Tucker, B. E., Uddin, S. A., Walker, A. R., Wester, W., Wiseman, P., Wolf, R. C., Yanny, B., Zhang, B., and Zhang, Y.

Subjects
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 Omega_m = 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 Omega_m = 0.321 +_ 0.018. For a flat w0waCDM 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
2018
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21. First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases

Author

Kessler, R., Brout, D., D'Andrea, C. B., Davis, T. M., Hinton, S. R., Kim, A. G., Lasker, J., Lidman, C., Macaulay, E., Möller, A., Sako, M., Scolnic, D., Smith, M., Sullivan, M., Zhang, B., Andersen, P., Asorey, J., Avelino, A., Calcino, J., Carollo, D., Challis, P., Childress, M., Clocchiatti, A., Crawford, S., Filippenko, A. V., Foley, R. J., Glazebrook, K., Hoormann, J. K., Kasai, E., Kirshner, R. P., Lewis, G. F., Mandel, K. S., March, M., Morganson, E., Muthukrishna, D., Nugent, P., Pan, Y. -C., Sommer, N. E., Swann, E., Thomas, R. C., Tucker, B. E., Uddin, S. A., Abbott, T. M. C., Allam, S., Annis, J., Avila, S., Banerji, M., Bechtol, K., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Crocce, M., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Diehl, H. T., Doel, P., Eifler, T. F., Flaugher, B., Fosalba, P., Frieman, J., Garcia-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., James, D. J., Johnson, M. W. G., Johnson, M. D., Krause, E., Kuehn, K., Kuropatkin, N., Lahav, O., Li, T. S., Lima, M., Marshall, J. L., Martini, P., Menanteau, F., Miller, C. J., Miquel, R., Nord, B., Plazas, A. A., Roodman, A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Soares-Santos, M., Sobreira, F., Suchyta, E., Tarle, G., Thomas, D., Walker, A. R., and Zhang, Y.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We describe catalog-level simulations of Type Ia supernova (SN~Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light curve analysis, and to determine bias corrections for SN~Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves, the simulation uses a detailed SN~Ia model, incorporates information from observations (PSF, sky noise, zero point), and uses summary information (e.g., detection efficiency vs. signal to noise ratio) based on 10,000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05~mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue color, distance biases can reach 0.4~mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.

Published
2018
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22. Cosmological Constraints from Multiple Probes in the Dark Energy Survey

Author

DES Collaboration, Abbott, T. M. C., Alarcon, A., Allam, S., Andersen, P., Andrade-Oliveira, F., Annis, J., Asorey, J., Avelino, A., Avila, S., Bacon, D., Banik, N., Bassett, B. A., Baxter, E., Bechtol, K., Becker, M. R., Bernstein, G. M., Bertin, E., Blazek, J., Bridle, S. L., Brooks, D., Brout, D., Burke, D. L., Calcino, J., Camacho, H., Campos, A., Rosell, A. Carnero, Carollo, D., Kind, M. Carrasco, Carretero, J., Castander, F. J., Cawthon, R., Challis, P., Chan, K. C., Chang, C., Childress, M., Clocchiatti, A., Crocce, M., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., Davis, T. M., 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., Elvin-Poole, J., Estrada, J., Evrard, A. E., Fernandez, E., Filippenko, A. V., Flaugher, B., Foley, R. J., Fosalba, P., Frieman, J., Galbany, L., García-Bellido, J., Gatti, M., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Glazebrook, K., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hinton, S. R., Hollowood, D. L., Honscheid, K., Hoormann, J. K., Hoyle, B., Huterer, D., Jain, B., James, D. J., Jarvis, M., Jeltema, T., Kasai, E., Kent, S., Kessler, R., Kim, A. G., Kirshner, R. P., Kokron, N., Krause, E., Kron, R., Kuehn, K., Kuropatkin, N., Lahav, O., Lasker, J., Lemos, P., Lewis, G. F., Li, T. S., Lidman, C., Lima, M., Lin, H., Macaulay, E., MacCrann, N., Maia, M. A. G., Mandel, K. S., March, M., Marriner, J., Marshall, J. L., Martini, P., McMahon, R. G., Melchior, P., Menanteau, F., Miquel, R., Mohr, J. J., Morganson, E., Muir, J., Möller, A., Neilsen, E., Nichol, R. C., Nord, B., Ogando, R. L. C., Palmese, A., Pan, Y. -C., Peiris, H. V., Percival, W. J., Plazas, A. A., Porredon, A., Prat, J., Romer, A. K., Roodman, A., Rosenfeld, R., Ross, A. J., Rykoff, E. S., Samuroff, S., Sánchez, C., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Scolnic, D., Secco, L. F., Serrano, S., Sevilla-Noarbe, I., Sharp, R., Sheldon, E., Smith, M., Soares-Santos, M., Sobreira, F., Sommer, N. E., Swann, E., Swanson, M. E. C., Tarle, G., Thomas, D., Thomas, R. C., Troxel, M. A., Tucker, B. E., Uddin, S. A., Vielzeuf, P., Walker, A. R., Wang, M., Weaverdyck, N., Wechsler, R. H., Weller, J., Yanny, B., Zhang, B., Zhang, Y., and Zuntz, J.

Subjects
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
2018
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23. Dynamical Analysis of Three Distant Trans-Neptunian Objects with Similar Orbits

Author

Khain, T., Becker, J. C., Adams, F. C., Gerdes, D. W., Hamilton, S. J., Franson, K., Zullo, L., Sako, M., Napier, K., Lin, Hsing Wen, Markwardt, L., Bernardinelli, P., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Bertin, E., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Diehl, H. T., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., Garcia-Bellido, J., Gruen, D., Gruendl, R. A., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., James, D. J., Krause, E., Kuehn, K., Kuropatkin, N., Lahav, O., Maia, M. A. G., Menanteau, F., Miquel, R., Nord, B., Ogando, R. L. C., Plazas, A. A., Romer, A. K., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Vikram, V., Walker, A. R., Wester, W., and Zhang, Y.

Subjects
Astrophysics - Earth and Planetary Astrophysics
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
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24. Dark Energy Survey Year 1 Results: Constraints on Extended Cosmological Models from Galaxy Clustering and Weak Lensing

Author

DES Collaboration, Abbott, T. M. C., Abdalla, F. B., Avila, S., Banerji, M., Baxter, E., Bechtol, K., Becker, M. R., Bertin, E., Blazek, J., Bridle, S. L., Brooks, D., Brout, D., Burke, D. L., Campos, A., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Cawthon, R., Chang, C., Chen, A., Crocce, M., Cunha, C. E., da Costa, L. N., Davis, C., De Vicente, J., DeRose, J., Desai, S., Di Valentino, E., Diehl, H. T., Dietrich, J. P., Dodelson, S., Doel, P., Drlica-Wagner, A., Eifler, T. F., Elvin-Poole, J., Evrard, A. E., Fernandez, E., Ferté, A., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., Huterer, D., Jain, B., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Kim, A. G., Krause, E., Kuehn, K., Kuropatkin, N., Lahav, O., Lee, S., Lemos, P., Leonard, C. D., Li, T. S., Liddle, A. R., Lima, M., Lin, H., Maia, M. A. G., Marshall, J. L., Martini, P., Menanteau, F., Miller, C. J., Miquel, R., Miranda, V., Mohr, J. J., Muir, J., Nichol, R. C., Nord, B., Ogando, R. L. C., Plazas, A. A., Raveri, M., Rollins, R. P., Romer, A. K., Roodman, A., Rosenfeld, R., Samuroff, S., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Scolnic, D., Secco, L. F., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Troxel, M. A., Vikram, V., Walker, A. R., Weaverdyck, N., Wechsler, R. H., Weller, J., Yanny, B., Zhang, Y., and Zuntz, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract

We present constraints on extensions of the minimal cosmological models dominated by dark matter and dark energy, $\Lambda$CDM and $w$CDM, by using a combined analysis of galaxy clustering and weak gravitational lensing from the first-year data of the Dark Energy Survey (DES Y1) in combination with external data. We consider four extensions of the minimal dark energy-dominated scenarios: 1) nonzero curvature $\Omega_k$, 2) number of relativistic species $N_{\rm eff}$ different from the standard value of 3.046, 3) time-varying equation-of-state of dark energy described by the parameters $w_0$ and $w_a$ (alternatively quoted by the values at the pivot redshift, $w_p$, and $w_a$), and 4) modified gravity described by the parameters $\mu_0$ and $\Sigma_0$ that modify the metric potentials. We also consider external information from Planck CMB measurements; BAO measurements from SDSS, 6dF, and BOSS; RSD measurements from BOSS; and SNIa information from the Pantheon compilation. Constraints on curvature and the number of relativistic species are dominated by the external data; when these are combined with DES Y1, we find $\Omega_k=0.0020^{+0.0037}_{-0.0032}$ at the 68% confidence level, and $N_{\rm eff}<3.28\, (3.55)$ at 68% (95%) confidence. For the time-varying equation-of-state, we find the pivot value $(w_p, w_a)=(-0.91^{+0.19}_{-0.23}, -0.57^{+0.93}_{-1.11})$ at pivot redshift $z_p=0.27$ from DES alone, and $(w_p, w_a)=(-1.01^{+0.04}_{-0.04}, -0.28^{+0.37}_{-0.48})$ at $z_p=0.20$ from DES Y1 combined with external data; in either case we find no evidence for the temporal variation of the equation of state. For modified gravity, we find the present-day value of the relevant parameters to be $\Sigma_0= 0.43^{+0.28}_{-0.29}$ from DES Y1 alone, and $(\Sigma_0, \mu_0)=(0.06^{+0.08}_{-0.07}, -0.11^{+0.42}_{-0.46})$ from DES Y1 combined with external data, consistent with predictions from GR., Comment: 22 pages, 7 figures, matches the published version

Published
2018
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25. Dark Energy Survey Year 1 Results: Joint Analysis of Galaxy Clustering, Galaxy Lensing, and CMB Lensing Two-point Functions

Author

Abbott, T. M. C., Abdalla, F. B., Alarcon, A., Allam, S., Annis, J., Avila, S., Aylor, K., Banerji, M., Banik, N., Baxter, E. J., Bechtol, K., Becker, M. R., Benson, B. A., Bernstein, G. M., Bertin, E., Bianchini, F., Blazek, J., Bleem, L., Bleem, L. E., Bridle, S. L., Brooks, D., Buckley-Geer, E., Burke, D. L., Carlstrom, J. E., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Cawthon, R., Chang, C., Chang, C. L., Cho, H-M., Choi, A., Chown, R., Crawford, T. M., Crites, A. T., Crocce, M., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., de Haan, T., DeRose, J., Desai, S., De Vicente, J., Diehl, H. T., Dietrich, J. P., Dobbs, M. A., Dodelson, S., Doel, P., Drlica-Wagner, A., Eifler, T. F., Elvin-Poole, J., Everett, W. B., Flaugher, B., Fosalba, P., Friedrich, O., Frieman, J., García-Bellido, J., Gatti, M., Gaztanaga, E., George, E. M., Gerdes, D. W., Giannantonio, T., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Halverson, N. W., Harrington, N. L., Hartley, W. G., Holder, G. P., Hollowood, D. L., Holzapfel, W. L., Honscheid, K., Hou, Z., Hoyle, B., Hrubes, J. D., Huterer, D., Jain, B., James, D. J., Jarvis, M., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Kent, S., Kirk, D., Knox, L., Kokron, N., Krause, E., Kuehn, K., Lahav, O., Lee, A. T., Leitch, E. M., Li, T. S., Lima, M., Lin, H., Luong-Van, D., MacCrann, N., Maia, M. A. G., Manzotti, A., Marrone, D. P., Marshall, J. L., Martini, P., McMahon, J. J., Menanteau, F., Meyer, S. S., Miquel, R., Mocanu, L. M., Mohr, J. J., Muir, J., Natoli, T., Nicola, A., Nord, B., Omori, Y., Padin, S., Pandey, S., Plazas, A. A., Porredon, A., Prat, J., Pryke, C., Rau, M. M., Reichardt, C. L., Rollins, R. P., Romer, A. K., Roodman, A., Ross, A. J., Rozo, E., Ruhl, J. E., Rykoff, E. S., Samuroff, S., Sánchez, C., Sanchez, E., Sayre, J. T., Scarpine, V., Schaffer, K. K., Secco, L. F., Serrano, S., Sevilla-Noarbe, I., Sheldon, E., Shirokoff, E., Simard, G., Smith, M., Soares-Santos, M., Sobreira, F., Staniszewski, Z., Stark, A. A., Story, K. T., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Troxel, M. A., Tucker, D. L., Vanderlinde, K., Vieira, J. D., Vielzeuf, P., Vikram, V., Walker, A. R., Wechsler, R. H., Weller, J., Williamson, R., Wu, W. L. K., Yanny, B., Zahn, O., Zhang, Y., and Zuntz, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We perform a joint analysis of the auto and cross-correlations between three cosmic fields: the galaxy density field, the galaxy weak lensing shear field, and the cosmic microwave background (CMB) weak lensing convergence field. These three fields are measured using roughly 1300 sq. deg. of overlapping optical imaging data from first year observations of the Dark Energy Survey and millimeter-wave observations of the CMB from both the South Pole Telescope Sunyaev-Zel'dovich survey and Planck. We present cosmological constraints from the joint analysis of the two-point correlation functions between galaxy density and galaxy shear with CMB lensing. We test for consistency between these measurements and the DES-only two-point function measurements, finding no evidence for inconsistency in the context of flat $\Lambda$CDM cosmological models. Performing a joint analysis of five of the possible correlation functions between these fields (excluding only the CMB lensing autospectrum) yields $S_{8}\equiv \sigma_8\sqrt{\Omega_{\rm m}/0.3} = 0.782^{+0.019}_{-0.025}$ and $\Omega_{\rm m}=0.260^{+0.029}_{-0.019}$. We test for consistency between these five correlation function measurements and the Planck-only measurement of the CMB lensing autospectrum, again finding no evidence for inconsistency in the context of flat $\Lambda$CDM models. Combining constraints from all six two-point functions yields $S_{8}=0.776^{+0.014}_{-0.021}$ and $\Omega_{\rm m}= 0.271^{+0.022}_{-0.016}$. These results provide a powerful test and confirmation of the results from the first year DES joint-probes analysis., Comment: 20 pages, 7 figures

Published
2018
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26. 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

Treu, T., Agnello, A., Baumer, M. A., Birrer, S., Buckley-Geer, E. J., Courbin, F., Kim, Y. J., Lim, H., Marshall, P. J., Nord, B., Schechter, P. L., Sivakumar, P. R., Abramson, L. E., Anguita, T., Apostolovski, Y., Auger, M. W., Chan, J. H. H., Chen, G. C. F., Collett, T. E., Fassnacht, C. D., Hsueh, J. -W., Lemon, C., McMahon, R. G., Motta, V., Ostrovski, F., Rojas, K., Rusu, C. E., Williams, P., Frieman, J., Meylan, G., Suyu, S. H., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Avila, S., Banerji, M., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., D'Andrea, C. B., da Costa, L. N., De Vicente, J., Doel, P., Eifler, T. F., Flaugher, B., Fosalba, P., García-Bellido, J., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gutierrez, G., Hartley, W. G., Hollowood, D., Honscheid, K., James, D. J., Kuehn, K., Kuropatkin, N., Lima, M., Maia, M. A. G., Martini, P., Menanteau, F., Miquel, R., Plazas, A. A., Romer, A. K., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, D. L., and Walker, A. R.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
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, which 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 (NIQs), 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 6-35\%. This rate is comparable to that of previous searches like SQLS even though the parent dataset of STRIDES is purely photometric and our selection of candidates cannot rely on spectroscopic information., Comment: (MNRAS in press; color scheme of Figures 1-4 improved with respect to v1)

Published
2018
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27. Transfer learning for galaxy morphology from one survey to another

Author

Sánchez, H. Domínguez, Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J. L., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Brooks, D., Buckley-Geer, E., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Doel, P., Evrard, A. E., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Maia, M. A. G., March, M., Melchior, P., Menanteau, F., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Walker, A. R., and Zuntz, J.

Subjects
Astrophysics - Astrophysics of Galaxies
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., Comment: Accepted for publication in MNRAS

Published
2018
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28. Star-galaxy classification in the Dark Energy Survey Y1 dataset

Author

Sevilla-Noarbe, I., Hoyle, B., Marchã, M. J., Soumagnac, M. T., Bechtol, K., Drlica-Wagner, A., Abdalla, F., Aleksić, J., Avestruz, C., Balbinot, E., Banerji, M., Bertin, E., Bonnett, C., Brunner, R., Carrasco-Kind, M., Choi, A., Giannantonio, T., Kim, E., Lahav, O., Moraes, B., Nord, B., Ross, A. J., Rykoff, E. S., Santiago, B., Sheldon, E., Wei, K., Wester, W., Yanny, B., Abbott, T., Allam, S., Brooks, D., Carnero-Rosell, A., Carretero, J., Cunha, C., da Costa, L., Davis, C., de Vicente, J., Desai, S., Doel, P., Fernandez, E., Flaugher, B., Frieman, J., Garcia-Bellido, J., Gaztanaga, E., Gruen, D., Gruendl, R., Gschwend, J., Gutierrez, G., Hollowood, D. L., Honscheid, K., James, D., Jeltema, T., Kirk, D., Krause, E., Kuehn, K., Li, T. S., Lima, M., Maia, M. A. G., March, M., McMahon, R. G., Menanteau, F., Miquel, R., Ogando, R. L. C., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, D. L., and Walker, A. R.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We perform a comparison of different approaches to star-galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. This is done by performing a wide range of tests with and without external `truth' information, which can be ported to other similar datasets. We make a broad evaluation of the performance of the classifiers in two science cases with DES data that are most affected by this systematic effect: large-scale structure and Milky Way studies. In general, even though the default morphological classifiers used for DES Y1 cosmology studies are sufficient to maintain a low level of systematic contamination from stellar mis-classification, contamination can be reduced to the O(1%) level by using multi-epoch and infrared information from external datasets. For Milky Way studies the stellar sample can be augmented by ~20% for a given flux limit. Reference catalogs used in this work will be made available upon publication., Comment: Reference catalogs used in this work will be made available upon publication

Published
2018
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29. Dark Energy Survey Year 1 Results: Weak Lensing Mass Calibration of redMaPPer Galaxy Clusters

Author

McClintock, T., Varga, T. N., Gruen, D., Rozo, E., Rykoff, E. S., Shin, T., Melchior, P., DeRose, J., Seitz, S., Dietrich, J. P., Sheldon, E., Zhang, Y., von der Linden, A., Jeltema, T., Mantz, A., Romer, A. K., Allen, S., Becker, M. R., Bermeo, A., Bhargava, S., Costanzi, M., Everett, S., Farahi, A., Hamaus, N., Hartley, W. G., Hollowood, D. L., Hoyle, B., Israel, H., Li, P., MacCrann, N., Morris, G., Palmese, A., Plazas, A. A., Pollina, G., Rau, M. M., Simet, M., Soares-Santos, M., Troxel, M. A., Cervantes, C. Vergara, Wechsler, R. H., Zuntz, J., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Avila, S., Bridle, S. L., Brooks, D., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Crocce, M., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Diehl, H. T., Doel, P., Drlica-Wagner, A., Evrard, A. E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Gruendl, R. A., Gutierrez, G., Honscheid, K., James, D. J., Kirk, D., Krause, E., Kuehn, K., Lahav, O., Li, T. S., Lima, M., March, M., Marshall, J. L., Menanteau, F., Miquel, R., Mohr, J. J., Nord, B., Ogando, R. L. C., Roodman, A., Sanchez, E., Scarpine, V., Schindler, R., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Tucker, D. L., Vikram, V., Walker, A. R., and Weller, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We constrain the mass--richness scaling relation of redMaPPer galaxy clusters identified in the Dark Energy Survey Year 1 data using weak gravitational lensing. We split clusters into $4\times3$ bins of richness $\lambda$ and redshift $z$ for $\lambda\geq20$ and $0.2 \leq z \leq 0.65$ and measure the mean masses of these bins using their stacked weak lensing signal. By modeling the scaling relation as $\langle M_{\rm 200m}|\lambda,z\rangle = M_0 (\lambda/40)^F ((1+z)/1.35)^G$, we constrain the normalization of the scaling relation at the 5.0 per cent level as $M_0 = [3.081 \pm 0.075 ({\rm stat}) \pm 0.133 ({\rm sys})] \cdot 10^{14}\ {\rm M}_\odot$ at $\lambda=40$ and $z=0.35$. The richness scaling index is constrained to be $F=1.356 \pm 0.051\ ({\rm stat})\pm 0.008\ ({\rm sys})$ and the redshift scaling index $G=-0.30\pm 0.30\ ({\rm stat})\pm 0.06\ ({\rm sys})$. These are the tightest measurements of the normalization and richness scaling index made to date. We use a semi-analytic covariance matrix to characterize the statistical errors in the recovered weak lensing profiles. Our analysis accounts for the following sources of systematic error: shear and photometric redshift errors, cluster miscentering, cluster member dilution of the source sample, systematic uncertainties in the modeling of the halo--mass correlation function, halo triaxiality, and projection effects. We discuss prospects for reducing this systematic error budget, which dominates the uncertainty on $M_0$. Our result is in excellent agreement with, but has significantly smaller uncertainties than, previous measurements in the literature, and augurs well for the power of the DES cluster survey as a tool for precision cosmology and upcoming galaxy surveys such as LSST, Euclid and WFIRST., Comment: 28 pages, 15 figures; matches MNRAS referee response version

Published
2018
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30. New Science, New Media: An Assessment of the Online Education and Public Outreach Initiatives of The Dark Energy Survey

Author

Wolf, R. C., Romer, A. K., Nord, B., Avila, S., Bechtol, K., Biron, L., Cawthon, R., Chang, C., Das, R., Ferte, A., Gill, M. S. S., Gupta, R. R., Hamilton, S., Hislop, J. M., Jennings, E., Krawiec, C., Kremin, A., Li, T. S., Lingard, T., Moller, A., Muir, J., Nagasawa, D. Q., Ogando, R. L. C., Plazas, A. A., Sevilla-Noarbe, I., Suchyta, E., Zhang, Y., and Zuntz, J.

Subjects
Physics - Physics Education, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

As large-scale international collaborations become the standard for astronomy research, a wealth of opportunities have emerged to create innovative education and public outreach (EPO) programming. In the past two decades, large collaborations have focused EPO strategies around published data products. Newer collaborations have begun to explore other avenues of public engagement before and after data are made available. We present a case study of the online EPO program of The Dark Energy Survey, currently one of the largest international astronomy collaborations actively taking data. DES EPO is unique at this scale in astronomy, as far as we are aware, as it evolved organically from scientists' passion for EPO and is entirely organized and implemented by the volunteer efforts of collaboration scientists. We summarize the strategy and implementation of eight EPO initiatives. For content distributed via social media, we present reach and user statistics over the 2016 calendar year. DES EPO online products reached ~2,500 users per post, and 94% of these users indicate a predisposition to science-related interests. We find no obvious correlation between post type and post reach, with the most popular posts featuring the intersections of science and art and/or popular culture. We conclude that one key issue of the online DES EPO program was designing material which would inspire new interest in science. The greatest difficulty of the online DES EPO program was sustaining scientist participation and collaboration support; the most successful programs are those which capitalized on the hobbies of participating scientists. We present statistics and recommendations, along with observations from individual experience, as a potentially instructive resource for scientists or EPO professionals interested in organizing EPO programs and partnerships for large science collaborations or organizations., Comment: 50 pages, 3 appendices, 15 total figures

Published
2018

31. The Dark Energy Survey Data Release 1

Author

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, D., Buckley-Geer, E., Burke, D. L., Busti, V., Campisano, R., Cardiel-Sas, L., Rosell, A. C arnero, Kind, M. Carrasco, 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., Neto, A. Fausti, Fernandez, E., Finley, D. A., Fitzpatrick, M., Flaugher, B., Foley, R. J., Fosalba, P., Friedel, D. N., Frieman, J., García-Bellido, J., tanaga, E. Gaz, 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., Juneau, S., 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., Nidever, D., Nikutta, R., Nogueira, F., Nord, B., Nugent, P., Nunes, L., Ogando, R. L. C., Old, L., Olsen, K., Pace, A. B., Palmese, A., Paz-Chinchón, F., Peiris, H. V., Percival, W. J., Petravick, D., Plazas, A. A., Poh, J., Pond, C., redon, A. Por, 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, E., Sanchez, M. L., Santiago, B., Saro, A., Scarpine, V., Scolnic, D., Scott, A., Serrano, S., Sevilla-Noarbe, I., Sheldon, E., Shipp, N., Silveira, M. L., Smith, R. C., Smith, J. A., Smith, M., Soares-Santos, M., ira, F. Sobre, 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., ker, A. R. Wal, Wechsler, R. H., Weller, J., Wester, W., Wolf, R. C., Wu, H., Yanny, B., Zenteno, A., Zhang, Y., and Zuntz, J.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
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., Comment: 30 pages, 20 Figures. Release page found at this url https://des.ncsa.illinois.edu/releases/dr1

Published
2018
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32. UV-Luminous, Star-Forming Hosts of z~2 Reddened Quasars in the Dark Energy Survey

Author

Wethers, C. F., Banerji, M., Hewett, P. C., Lemon, C. A., McMahon, R. G., Reed, S. L., Shen, Y., Abdalla, F. B., Benoit-Lévy, A., Brooks, D., Buckley-Geer, E., Capozzi, D., Rosell, A. Carnero, CarrascoKind, M., Carretero, J., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., DePoy, D. L., Desai, S., Doel, P., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Honscheid, K., James, D. J., Jeltema, T., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Lima, M., Maia, M. A. G., Marshall, J. L., Martini, P., Menanteau, F., Miquel, R., Nichol, R. C., Nord, B., Plazas, A. A., Romer, A. K., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Tarle, G., and Walker, A. R.

Subjects
Astrophysics - Astrophysics of Galaxies
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., Comment: 21 pages, 8 figures, MNRAS Accepted

Published
2018
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33. Dark Energy Survey year 1 results: Constraints on extended cosmological models from galaxy clustering and weak lensing

Author

Abbott, TMC, Abdalla, FB, Avila, S, Banerji, M, Baxter, E, Bechtol, K, Becker, MR, Bertin, E, Blazek, J, Bridle, SL, Brooks, D, Brout, D, Burke, DL, Campos, A, Rosell, A Carnero, Kind, M Carrasco, Carretero, J, Castander, FJ, Cawthon, R, Chang, C, Chen, A, Crocce, M, Cunha, CE, da Costa, LN, Davis, C, De Vicente, J, DeRose, J, Desai, S, Di Valentino, E, Diehl, HT, Dietrich, JP, Dodelson, S, Doel, P, Drlica-Wagner, A, Eifler, TF, Elvin-Poole, J, Evrard, AE, Fernandez, E, Ferté, A, Flaugher, B, Fosalba, P, Frieman, J, García-Bellido, J, Gaztanaga, E, Gerdes, DW, Giannantonio, T, Gruen, D, Gruendl, RA, Gschwend, J, Gutierrez, G, Hartley, WG, Hollowood, DL, Honscheid, K, Hoyle, B, Huterer, D, Jain, B, Jeltema, T, Johnson, MWG, Johnson, MD, Kim, AG, Krause, E, Kuehn, K, Kuropatkin, N, Lahav, O, Lee, S, Lemos, P, Leonard, CD, Li, TS, Liddle, AR, Lima, M, Lin, H, Maia, MAG, Marshall, JL, Martini, P, Menanteau, F, Miller, CJ, Miquel, R, Miranda, V, Mohr, JJ, Muir, J, Nichol, RC, Nord, B, Ogando, RLC, Plazas, AA, Raveri, M, Rollins, RP, Romer, AK, Roodman, A, Rosenfeld, R, Samuroff, S, Sanchez, E, Scarpine, V, Schindler, R, Schubnell, M, Scolnic, D, Secco, LF, Serrano, S, Sevilla-Noarbe, I, Smith, M, and Soares-Santos, M

Subjects
Particle and High Energy Physics, Physical Sciences, astro-ph.CO, gr-qc, hep-ph, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Quantum Physics, Nuclear & Particles Physics, Mathematical physics, Astronomical sciences, Particle and high energy physics
Abstract

We present constraints on extensions of the minimal cosmological models dominated by dark matter and dark energy, ΛCDM and wCDM, by using a combined analysis of galaxy clustering and weak gravitational lensing from the first-year data of the Dark Energy Survey (DES Y1) in combination with external data. We consider four extensions of the minimal dark energy-dominated scenarios: (1) nonzero curvature ωk, (2) number of relativistic species Neff different from the standard value of 3.046, (3) time-varying equation-of-state of dark energy described by the parameters w0 and wa (alternatively quoted by the values at the pivot redshift, wp, and wa), and (4) modified gravity described by the parameters μ0 and ς0 that modify the metric potentials. We also consider external information from Planck cosmic microwave background measurements; baryon acoustic oscillation measurements from SDSS, 6dF, and BOSS; redshift-space distortion measurements from BOSS; and type Ia supernova information from the Pantheon compilation of datasets. Constraints on curvature and the number of relativistic species are dominated by the external data; when these are combined with DES Y1, we find ωk=0.0020-0.0032+0.0037 at the 68% confidence level, and the upper limit Neff3.0. For the time-varying equation-of-state, we find the pivot value (wp,wa)=(-0.91-0.23+0.19,-0.57-1.11+0.93) at pivot redshift zp=0.27 from DES alone, and (wp,wa)=(-1.01-0.04+0.04,-0.28-0.48+0.37) at zp=0.20 from DES Y1 combined with external data; in either case we find no evidence for the temporal variation of the equation of state. For modified gravity, we find the present-day value of the relevant parameters to be ς0=0.43-0.29+0.28 from DES Y1 alone, and (ς0,μ0)=(0.06-0.07+0.08,-0.11-0.46+0.42) from DES Y1 combined with external data. These modified-gravity constraints are consistent with predictions from general relativity.

Published
2019

34. First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases

Author

Kessler, R, Brout, D, D’Andrea, CB, Davis, TM, Hinton, SR, Kim, AG, Lasker, J, Lidman, C, Macaulay, E, Möller, A, Sako, M, Scolnic, D, Smith, M, Sullivan, M, Zhang, B, Andersen, P, Asorey, J, Avelino, A, Calcino, J, Carollo, D, Challis, P, Childress, M, Clocchiatti, A, Crawford, S, Filippenko, AV, Foley, RJ, Glazebrook, K, Hoormann, JK, Kasai, E, Kirshner, RP, Lewis, GF, Mandel, KS, March, M, Morganson, E, Muthukrishna, D, Nugent, P, Pan, Y-C, Sommer, NE, Swann, E, Thomas, RC, Tucker, BE, Uddin, SA, Abbott, TMC, Allam, S, Annis, J, Avila, S, Banerji, M, Bechtol, K, Bertin, E, Brooks, D, Buckley-Geer, E, Burke, DL, Rosell, A Carnero, Kind, M Carrasco, Carretero, J, Castander, FJ, Crocce, M, da Costa, LN, Davis, C, De Vicente, J, Desai, S, Diehl, HT, Doel, P, Eifler, TF, Flaugher, B, Fosalba, P, Frieman, J, García-Bellido, J, Gaztanaga, E, Gerdes, DW, Gruen, D, Gruendl, RA, Gutierrez, G, Hartley, WG, Hollowood, DL, Honscheid, K, James, DJ, Johnson, MWG, Johnson, MD, Krause, E, Kuehn, K, Kuropatkin, N, Lahav, O, Li, TS, Lima, M, Marshall, JL, Martini, P, Menanteau, F, Miller, CJ, Miquel, R, Nord, B, Plazas, AA, Roodman, A, Sanchez, E, Scarpine, V, Schindler, R, Schubnell, M, Serrano, S, Sevilla-Noarbe, I, and Soares-Santos, M

Subjects
Space Sciences, Physical Sciences, Affordable and Clean Energy, techniques, cosmology, supernovae, (cosmology:) dark energy, astro-ph.CO, astro-ph.IM, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract

We describe catalogue-level simulations of Type Ia supernova (SN Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN) and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light-curve analysis and to determine bias corrections for SN Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves, the simulation uses a detailed SN Ia model, incorporates information from observations (point spread function, sky noise, zero-point), and uses summary information (e.g. detection efficiency versus signal-to-noise ratio) based on 10 000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05 mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue colour, distance biases can reach 0.4 mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.

Published
2019

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

Author

Jacobs, C, Collett, T, Glazebrook, K, McCarthy, C, Qin, AK, Abbott, TMC, Abdalla, FB, Annis, J, Avila, S, Bechtol, K, Bertin, E, Brooks, D, Buckley-Geer, E, Burke, DL, Rosell, A Carnero, Kind, M Carrasco, Carretero, J, da Costa, LN, Davis, C, De Vicente, J, Desai, S, Diehl, HT, Doel, P, Eifler, TF, Flaugher, B, Frieman, J, García-Bellido, J, Gaztanaga, E, Gerdes, DW, Goldstein, DA, Gruen, D, Gruendl, RA, Gschwend, J, Gutierrez, G, Hartley, WG, Hollowood, DL, Honscheid, K, Hoyle, B, James, DJ, Kuehn, K, Kuropatkin, N, Lahav, O, Li, TS, Lima, M, Lin, H, Maia, MAG, Martini, P, Miller, CJ, Miquel, R, Nord, B, Plazas, AA, Sanchez, E, Scarpine, V, Schubnell, M, Serrano, S, Sevilla-Noarbe, I, Smith, M, Soares-Santos, M, Sobreira, F, Suchyta, E, Swanson, MEC, Tarle, G, Vikram, V, Walker, AR, Zhang, Y, Zuntz, J, and Collaboration, DES

Subjects
Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, gravitational lensing: strong, methods: statistical, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
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 7301 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 9428 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.

Published
2019

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

Author

Brout, D, Sako, M, Scolnic, D, Kessler, R, D’Andrea, CB, Davis, TM, Hinton, SR, Kim, AG, Lasker, J, Macaulay, E, Möller, A, Nichol, RC, Smith, M, Sullivan, M, Wolf, RC, Allam, S, Bassett, BA, Brown, P, Castander, FJ, Childress, M, Foley, RJ, Galbany, L, Herner, K, Kasai, E, March, M, Morganson, E, Nugent, P, Pan, Y-C, Thomas, RC, Tucker, BE, Wester, W, Abbott, TMC, Annis, J, Avila, S, Bertin, E, Brooks, D, Burke, DL, Rosell, A Carnero, Kind, M Carrasco, Carretero, J, Crocce, M, Cunha, CE, da Costa, LN, Davis, C, De Vicente, J, Desai, S, Diehl, HT, Doel, P, Eifler, TF, Flaugher, B, Fosalba, P, Frieman, J, García-Bellido, J, Gaztanaga, E, Gerdes, DW, Goldstein, DA, Gruen, D, Gruendl, RA, Gschwend, J, Gutierrez, G, Hartley, WG, Hollowood, DL, Honscheid, K, James, DJ, Kuehn, K, Kuropatkin, N, Lahav, O, Li, TS, Lima, M, Marshall, JL, Martini, P, Miquel, R, Nord, B, Plazas, AA, Roodman, A, Rykoff, ES, Sanchez, E, Scarpine, V, Schindler, R, Schubnell, M, Serrano, S, Sevilla-Noarbe, I, Soares-Santos, M, Sobreira, F, Suchyta, E, Swanson, MEC, Tarle, G, Thomas, D, Tucker, DL, Walker, AR, Yanny, B, and Zhang, Y

Subjects
Space Sciences, Physical Sciences, Affordable and Clean Energy, cosmology: observations, supernovae: general, techniques: photometric, astro-ph.IM, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Physical Chemistry (incl. Structural), Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract

We present griz light curves of 251 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 by employing a scene modeling approach that simultaneously 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 that 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.

Published
2019

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

Author

Agnello, A., Lin, H., Kuropatkin, N., Buckley-Geer, E., Anguita, T., Schechter, P. L., Morishita, T., Motta, V., Rojas, K., Treu, T., Amara, A., Auger, M. W., Courbin, F., Fassnacht, C. D., Frieman, J., More, A., Marshall, P. J., McMahon, R. G., Meylan, G., Suyu, S. H., Glazebrook, K., Morgan, N., Nord, B., Abbott, T. M. C., Abdalla, F. B., Annis, J., Bechtol, K., Benoit-Lévy, A., Bertin, E., Bernstein, R. A., Brooks, D., Burke, D. L., Rosell, A. Carnero, Carretero, J., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Desai, S., Drlica-Wagner, A., Eifler, T. F., Flaughe, B., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Honscheid, K., James, D. J., Kuehn, K., Lahav, O., Lima, M., Maia, M. A. G., March, M., Menanteau, F., Miquel, R., Ogando, R. L. C., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnel, M., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Tucker, D., and Wechsler, R.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
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
2017
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40. Baryon Content in a Sample of 91 Galaxy Clusters Selected by the South Pole Telescope at 0.2 < z < 1.25

Author

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, E., 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, M., Smith, R. C., 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
Astrophysics - Cosmology and Nongalactic Astrophysics
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
2017
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41. The Splashback Feature around DES Galaxy Clusters: Galaxy Density and Weak Lensing Profiles

Author

Chang, C., Baxter, E., Jain, B., Sánchez, C., Adhikari, S., Varga, T. N., Fang, Y., Rozo, E., Rykoff, E. S., Kravtsov, A., Gruen, D., Huff, E. M., Jarvis, M., Kim, A. G., Prat, J., MacCrann, N., McClintock, T., Palmese, A., Rapetti, D., Rollins, R. P., Samuroff, S., Sheldon, E., Troxel, M. A., Wechsler, R. H., Zhang, Y., Zuntz, J., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Bechtol, K., Benoit-Lévy, A., Bernstein, G. M., Brooks, D., Buckley-Geer, E., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., D'Andrea, C. B., da, L. N., Davis, C., Desai, S., Diehl, H. T., Dietrich, J. P., Drlica-Wagner, A., Eifler, T. F., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruendl, R. A., Gschwend, J., Gutierrez, G., Honscheid, K., James, D. J., Jeltema, T., Krause, E., Kuehn, K., Lahav, O., Lima, M., March, M., Marshall, J. L., Martini, P., Melchior, P., Menanteau, F., Miquel, R., Mohr, J. J., Nord, B., Ogando, R. L. C., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Sevilla-Noarbe, I., Smith, R. C., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., and Weller, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Splashback refers to the process of matter that is accreting onto a dark matter halo reaching its first orbital apocenter and turning around in its orbit. The cluster-centric radius at which this process occurs, r_sp, defines a halo boundary that is connected to the dynamics of the cluster. A rapid decline in the halo profile is expected near r_sp. We measure the galaxy number density and weak lensing mass profiles around redMaPPer galaxy clusters in the first year Dark Energy Survey (DES) data. For a cluster sample with mean M_200m mass ~2.5 x 10^14 M_sun, we find strong evidence of a splashback-like steepening of the galaxy density profile and measure r_sp=1.13 +/- 0.07 Mpc/h, consistent with earlier SDSS measurements of More et al. (2016) and Baxter et al. (2017). Moreover, our weak lensing measurement demonstrates for the first time the existence of a splashback-like steepening of the matter profile of galaxy clusters. We measure r_sp=1.34 +/- 0.21 Mpc/h from the weak lensing data, in good agreement with our galaxy density measurements. For different cluster and galaxy samples, we find that consistent with LCDM simulations, r_sp scales with R_200m and does not evolve with redshift over the redshift range of 0.3--0.6. We also find that potential systematic effects associated with the redMaPPer algorithm may impact the location of r_sp. We discuss progress needed to understand the systematic uncertainties and fully exploit forthcoming data from DES and future surveys, emphasizing the importance of more realistic mock catalogs and independent cluster samples., Comment: 25 pages, 15 figures; update to journal accepted version

Published
2017
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42. A gravitational-wave standard siren measurement of the Hubble constant

Author

Abbott, B. P., Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adams, T., Addesso, P., Adhikari, R. X., Adya, V. B., Affeldt, C., Afrough, M., Agarwal, B., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, B., Allen, G., Allocca, A., Altin, P. A., Amato, A., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arnaud, N., Arun, K. G., Ascenzi, S., Ashton, G., Ast, M., Aston, S. M., Astone, P., Atallah, D. V., Aufmuth, P., Aulbert, C., AultONeal, K., Austin, C., Avila-Alvarez, A., Babak, S., Bacon, P., Bader, M. K. M., Bae, S., Baker, P. T., Baldaccini, F., Ballardin, G., Ballmer, S. W., Banagiri, S., Barayoga, J. C., Barclay, S. E., Barish, B. C., Barker, D., Barkett, K., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Batch, J. C., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Beer, C., Bejger, M., Belahcene, I., Bell, A. S., Berger, B. K., Bergmann, G., Bero, J. J., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhagwat, S., Bhandare, R., Bilenko, I. A., Billingsley, G., Billman, C. R., Birch, J., Birney, R., Birnholtz, O., Biscans, S., Biscoveanu, S., Bisht, A., Bitossi, M., Biwer, C., Bizouard, M. A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bloemen, S., Bock, O., Bode, N., Boer, M., Bogaert, G., Bohe, A., Bondu, F., Bonilla, E., Bonnand, R., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bossie, K., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Branchesi, M., Brau, J. E., Briant, T., Brillet, A., Brinkmann, M., Brisson, V., Brockill, P., Broida, J. E., Brooks, A. F., Brown, D. A., Brown, D. D., Brunett, S., Buchanan, C. C., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Buy, C., Byer, R. L., Cabero, M., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Canizares, P., Cannon, K. C., Cao, H., Cao, J., Capano, C. D., Capocasa, E., Carbognani, F., Caride, S., Carney, M. F., Diaz, J. Casanueva, Casentini, C., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cepeda, C. B., Cerdá-Durán, P., Cerretani, G., Cesarini, E., Chamberlin, S. J., Chan, M., Chao, S., Charlton, P., Chase, E., Chassande-Mottin, E., Chatterjee, D., Chatziioannou, K., Cheeseboro, B. D., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Chia, H., Chincarini, A., Chiummo, A., Chmiel, T., Cho, H. S., Cho, M., Chow, J. H., Christensen, N., Chu, Q., Chua, A. J. K., Chua, S., Chung, A. K. W., Chung, S., Ciani, G., Ciolfi, R., Cirelli, C. E., Cirone, A., Clara, F., Clark, J. A., Clearwater, P., Cleva, F., Cocchieri, C., Coccia, E., Cohadon, P. -F., Cohen, D., Colla, A., Collette, C. G., Cominsky, L. R., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corley, K. R., Cornish, N., Corsi, A., Cortese, S., Costa, C. A., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Couvares, P., Covas, P. B., Cowan, E. E., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Cripe, J., Crowder, S. G., Cullen, T. J., Cumming, A., Cunningham, L., Cuoco, E., Canton, T. Dal, Dálya, G., Danilishin, S. L., D'Antonio, S., Danzmann, K., Dasgupta, A., Costa, C. F. Da Silva, Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davis, D., Daw, E. J., Day, B., De, S., DeBra, D., Degallaix, J., De Laurentis, M., Deléglise, S., Del Pozzo, W., Demos, N., Denker, T., Dent, T., De Pietri, R., Dergachev, V., De Rosa, R., DeRosa, R. T., De Rossi, C., DeSalvo, R., de Varona, O., Devenson, J., Dhurandhar, S., Díaz, M. C., Di Fiore, L., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Di Pace, S., Di Palma, I., Di Renzo, F., Doctor, Z., Dolique, V., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Douglas, R., Álvarez, M. Dovale, Downes, T. P., Drago, M., Dreissigacker, C., Driggers, J. C., Du, Z., Ducrot, M., Dupej, P., Dwyer, S. E., Edo, T. B., Edwards, M. C., Effler, A., Eggenstein, H. -B., Ehrens, P., Eichholz, J., Eikenberry, S. S., Eisenstein, R. A., Essick, R. C., Estevez, D., Etienne, Z. B., Etzel, T., Evans, M., Evans, T. M., Factourovich, M., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fee, C., Fehrmann, H., Feicht, J., Fejer, M. M., Fernandez-Galiana, A., Ferrante, I., Ferreira, E. C., Ferrini, F., Fidecaro, F., Finstad, D., Fiori, I., Fiorucci, D., Fishbach, M., Fisher, R. P., Fitz-Axen, M., Flaminio, R., Fletcher, M., Fong, H., Font, J. A., Forsyth, P. W. F., Forsyth, S. S., Fournier, J. -D., Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fries, E. M., Fritschel, P., Frolov, V. V., Fulda, P., Fyffe, M., Gabbard, H., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gammaitoni, L., Ganija, M. R., Gaonkar, S. G., Garcia-Quiros, C., Garufi, F., Gateley, B., Gaudio, S., Gaur, G., Gayathri, V., Gehrels, N., Gemme, G., Genin, E., Gennai, A., George, D., George, J., Gergely, L., Germain, V., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, S., Giaime, J. A., Giardina, K. D., Giazotto, A., Gill, K., Glover, L., Goetz, E., Goetz, R., Gomes, S., Goncharov, B., González, G., Castro, J. M. Gonzalez, Gopakumar, A., Gorodetsky, M. L., Gossan, S. E., Gosselin, M., Gouaty, R., Grado, A., Graef, C., Granata, M., Grant, A., Gras, S., Gray, C., Greco, G., Green, A. C., Gretarsson, E. M., Groot, P., Grote, H., Grunewald, S., Gruning, P., Guidi, G. M., Guo, X., Gupta, A., Gupta, M. K., Gushwa, K. E., Gustafson, E. K., Gustafson, R., Halim, O., Hall, B. R., Hall, E. D., Hamilton, E. Z., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanson, J., Hardwick, T., Harms, J., Harry, G. M., Harry, I. W., Hart, M. J., Haster, C. -J., Haughian, K., Healy, J., Heidmann, A., Heintze, M. C., Heitmann, H., Hello, P., Hemming, G., Hendry, M., Heng, I. S., Hennig, J., Heptonstall, A. W., Heurs, M., Hild, S., Hinderer, T., Hoak, D., Hofman, D., Holt, K., Holz, D. E., Hopkins, P., Horst, C., Hough, J., Houston, E. A., Howell, E. J., Hreibi, A., Hu, Y. M., Huerta, E. A., Huet, D., Hughey, B., Husa, S., Huttner, S. H., Huynh-Dinh, T., Indik, N., Inta, R., Intini, G., Isa, H. N., Isac, J. -M., Isi, M., Iyer, B. R., Izumi, K., Jacqmin, T., Jani, K., Jaranowski, P., Jawahar, S., Jiménez-Forteza, F., Johnson, W. W., Jones, D. I., Jones, R., Jonker, R. J. G., Ju, L., Junker, J., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Karki, S., Karvinen, K. S., Kasprzack, M., Katolik, M., Katsavounidis, E., Katzman, W., Kaufer, S., Kawabe, K., Kéfélian, F., Keitel, D., Kemball, A. J., Kennedy, R., Kent, C., Key, J. S., Khalili, F. Y., Khan, I., Khan, S., Khan, Z., Khazanov, E. A., Kijbunchoo, N., Kim, Chunglee, Kim, J. C., Kim, K., Kim, W., Kim, W. S., Kim, Y. -M., Kimbrell, S. J., King, E. J., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knowles, T. D., Koch, P., Koehlenbeck, S. M., Koley, S., Kondrashov, V., Kontos, A., Korobko, M., Korth, W. Z., Kowalska, I., Kozak, D. B., Krämer, C., Kringel, V., Krishnan, B., Królak, A., Kuehn, G., Kumar, P., Kumar, R., Kumar, S., Kuo, L., Kutynia, A., Kwang, S., Lackey, B. D., Lai, K. H., Landry, M., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lee, C. H., Lee, H. K., Lee, H. M., Lee, H. W., Lee, K., Lehmann, J., Lenon, A., Leonardi, M., Leroy, N., Letendre, N., Levin, Y., Li, T. G. F., Linker, S. D., Littenberg, T. B., Liu, J., Liu, X., Lo, R. K. L., Lockerbie, N. A., London, L. T., Lord, J. E., Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lough, J. D., Lousto, C. O., Lovelace, G., Lück, H., Lumaca, D., Lundgren, A. P., Lynch, R., Ma, Y., Macas, R., Macfoy, S., Machenschalk, B., MacInnis, M., Macleod, D. M., Hernandez, I. Magaña, Magaña-Sandoval, F., Zertuche, L. Magaña, Magee, R. M., Majorana, E., Maksimovic, I., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Marchesoni, F., Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Martelli, F., Martellini, L., Martin, I. W., Martin, R. M., Martynov, D. V., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Matichard, F., Matone, L., Mavalvala, N., Mazumder, N., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGuire, S. C., McIntyre, G., McIver, J., McManus, D. J., McNeill, L., McRae, T., McWilliams, S. T., Meacher, D., Meadors, G. D., Mehmet, M., Meidam, J., Mejuto-Villa, E., Melatos, A., Mendell, G., Mercer, R. A., Merilh, E. L., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. M., Miao, H., Michel, C., Middleton, H., Mikhailov, E. E., Milano, L., Miller, A. L., Miller, B. B., Miller, J., Millhouse, M., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Ming, J., Mishra, C., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Moffa, D., Moggi, A., Mogushi, K., Mohan, M., Mohapatra, S. R. P., Montani, M., Moore, C. J., Moraru, D., Moreno, G., Morriss, S. R., Mours, B., Mow-Lowry, C. M., Mueller, G., Muir, A. W., Mukherjee, Arunava, Mukherjee, D., Mukherjee, S., Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Muratore, M., Murray, P. G., Napier, K., Nardecchia, I., Naticchioni, L., Nayak, R. K., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Nevin, L., Newport, J. M., Newton, G., Ng, K. K. Y., Nguyen, T. T., Nichols, D., Nielsen, A. B., Nissanke, S., Nitz, A., Noack, A., Nocera, F., Nolting, D., North, C., Nuttall, L. K., Oberling, J., O'Dea, G. D., Ogin, G. H., Oh, J. J., Oh, S. H., Ohme, F., Okada, M. A., Oliver, M., Oppermann, P., Oram, Richard J., O'Reilly, B., Ormiston, R., Ortega, L. F., O'Shaughnessy, R., Ossokine, S., Ottaway, D. J., Overmier, H., Owen, B. J., Pace, A. E., Page, J., Page, M. A., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Pal-Singh, A., Pan, Howard, Pan, Huang-Wei, Pang, B., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Papa, M. A., Parida, A., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patil, M., Patricelli, B., Pearlstone, B. L., Pedraza, M., Pedurand, R., Pekowsky, L., Pele, A., Penn, S., Perez, C. J., Perreca, A., Perri, L. M., Pfeiffer, H. P., Phelps, M., Piccinni, O. J., Pichot, M., Piergiovanni, F., Pierro, V., Pillant, G., Pinard, L., Pinto, I. M., Pirello, M., Pitkin, M., Poe, M., Poggiani, R., Popolizio, P., Porter, E. K., Post, A., Powell, J., Prasad, J., Pratt, J. W. W., Pratten, G., Predoi, V., Prestegard, T., Prijatelj, M., Principe, M., Privitera, S., Prodi, G. A., Prokhorov, L. G., Puncken, O., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Quetschke, V., Quintero, E. A., Quitzow-James, R., Raab, F. J., Rabeling, D. S., Radkins, H., Raffai, P., Raja, S., Rajan, C., Rajbhandari, B., Rakhmanov, M., Ramirez, K. E., Ramos-Buades, A., Rapagnani, P., Raymond, V., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Ren, W., Reyes, S. D., Ricci, F., Ricker, P. M., Rieger, S., Riles, K., Rizzo, M., Robertson, N. A., Robie, R., Robinet, F., Rocchi, A., Rolland, L., Rollins, J. G., Roma, V. J., Romano, J. D., Romano, R., Romel, C. L., Romie, J. H., Rosińska, D., Ross, M. P., Rowan, S., Rüdiger, A., Ruggi, P., Rutins, G., Ryan, K., Sachdev, S., Sadecki, T., Sadeghian, L., Sakellariadou, M., Salconi, L., Saleem, M., Salemi, F., Samajdar, A., Sammut, L., Sampson, L. M., Sanchez, E. J., Sanchez, L. E., Sanchis-Gual, N., Sandberg, V., Sanders, J. R., Sassolas, B., Sathyaprakash, B. S., Saulson, P. R., Sauter, O., Savage, R. L., Sawadsky, A., Schale, P., Scheel, M., Scheuer, J., Schmidt, J., Schmidt, P., Schnabel, R., Schofield, R. M. S., Schönbeck, A., Schreiber, E., Schuette, D., Schulte, B. W., Schutz, B. F., Schwalbe, S. G., Scott, J., Scott, S. M., Seidel, E., Sellers, D., Sengupta, A. S., Sentenac, D., Sequino, V., Sergeev, A., Shaddock, D. A., Shaffer, T. J., Shah, A. A., Shahriar, M. S., Shaner, M. B., Shao, L., Shapiro, B., Shawhan, P., Sheperd, A., Shoemaker, D. H., Shoemaker, D. M., Siellez, K., Siemens, X., Sieniawska, M., Sigg, D., Silva, A. D., Singer, L. P., Singh, A., Singhal, A., Sintes, A. M., Slagmolen, B. J. J., Smith, B., Smith, J. R., Smith, R. J. E., Somala, S., Son, E. J., Sonnenberg, J. A., Sorazu, B., Sorrentino, F., Souradeep, T., Spencer, A. P., Srivastava, A. K., Staats, K., Staley, A., Steer, D., Steinke, M., Steinlechner, J., Steinlechner, S., Steinmeyer, D., Stevenson, S. P., Stone, R., Stops, D. J., Strain, K. A., Stratta, G., Strigin, S. E., Strunk, A., Sturani, R., Stuver, A. L., Summerscales, T. Z., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Swinkels, B. L., Szczepańczyk, M. J., Tacca, M., Tait, S. C., Talbot, C., Talukder, D., Tanner, D. B., Tápai, M., Taracchini, A., Tasson, J. D., Taylor, J. A., Taylor, R., Tewari, S. V., Theeg, T., Thies, F., Thomas, E. G., Thomas, M., Thomas, P., Thorne, K. A., Thrane, E., Tiwari, S., Tiwari, V., Tokmakov, K. V., Toland, K., Tonelli, M., Tornasi, Z., Torres-Forné, A., Torrie, C. I., Töyrä, D., Travasso, F., Traylor, G., Trinastic, J., Tringali, M. C., Trozzo, L., Tsang, K. W., Tse, M., Tso, R., Tsukada, L., Tsuna, D., Tuyenbayev, D., Ueno, K., Ugolini, D., Unnikrishnan, C. S., Urban, A. L., Usman, S. A., Vahlbruch, H., Vajente, G., Valdes, G., van Bakel, N., van Beuzekom, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Schaaf, L., van Heijningen, J. V., van Veggel, A. A., Vardaro, M., Varma, V., Vass, S., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venkateswara, K., Venugopalan, G., Verkindt, D., Vetrano, F., Viceré, A., Viets, A. D., Vinciguerra, S., Vine, D. J., Vinet, J. -Y., Vitale, S., Vo, T., Vocca, H., Vorvick, C., Vyatchanin, S. P., Wade, A. R., Wade, L. E., Wade, M., Walet, R., Walker, M., Wallace, L., Walsh, S., Wang, G., Wang, H., Wang, J. Z., Wang, W. H., Wang, Y. F., Ward, R. L., Warner, J., Was, M., Watchi, J., Weaver, B., Wei, L. -W., Weinert, M., Weinstein, A. J., Weiss, R., Wen, L., Wessel, E. K., Weßels, P., Westerweck, J., Westphal, T., Wette, K., Whelan, J. T., Whitcomb, S. E., Whiting, B. F., Whittle, C., Wilken, D., Williams, D., Williams, R. D., Williamson, A. R., Willis, J. L., Willke, B., Wimmer, M. H., Winkler, W., Wipf, C. C., Wittel, H., Woan, G., Woehler, J., Wofford, J., Wong, K. W. K., Worden, J., Wright, J. L., Wu, D. S., Wysocki, D. M., Xiao, S., Yamamoto, H., Yancey, C. C., Yang, L., Yap, M. J., Yazback, M., Yu, Hang, Yu, Haocun, Yvert, M., Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, L., Zhang, M., Zhang, T., Zhang, Y. -H., Zhao, C., Zhou, M., Zhou, Z., Zhu, S. J., Zhu, X. J., Zimmerman, A. B., Zucker, M. E., Zweizig, J., Foley, R. J., Coulter, D. A., Drout, M. R., Kasen, D., Kilpatrick, C. D., Madore, B. F., Murguia-Berthier, A., Pan, Y. -C., Piro, A. L., Prochaska, J. X., Ramirez-Ruiz, E., Rest, A., Rojas-Bravo, C., Shappee, B. J., Siebert, M. R., Simon, J. D., Ulloa, N., Annis, J., Soares-Santos, M., Brout, D., Scolnic, D., Diehl, H. T., Frieman, J., Berger, E., Alexander, K. D., Allam, S., Balbinot, E., Blanchard, P., Butler, R. E., Chornock, R., Cook, E. R., Cowperthwaite, P., Drlica-Wagner, A., Durret, F., Eftekhari, T., Finley, D. A., Fong, W., Fryer, C. L., García-Bellido, J., Gill, M. S . S., Gruendl, R. A., Hartley, W., Herner, K., Huterer, D., Kessler, R., Li, T. S., Lin, H., Lopes, P. A. A., Lourenço, A. C. C., Margutti, R., Marriner, J., Marshall, J. L., Matheson, T., Medina, G. E., Metzger, B. D., Muñoz, R. R., Muir, J., Nicholl, M., Nugent, P., Palmese, A., Paz-Chinchón, F., Quataert, E., Sako, M., Sauseda, M., Schlegel, D. J., Secco, L. F., Smith, N., Sobreira, F., Stebbins, A., Villar, V. A., Vivas, A. K., Wester, W., Williams, P. K. G., Yanny, B., Zenteno, A., Abbott, T. M. C., Abdalla, F. B., Bechtol, K., Benoit-Lévy, A., Bertin, E., Bridle, S. L., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., DePoy, D. L., Desai, S., Dietrich, J. P., Estrada, J., Fernandez, E., Flaugher, B., Fosalba, P., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Goldstein, D. A., Gruen, D., Gutierrez, G., Hartley, W. G., Honscheid, K., Jain, B., James, D. J., Jeltema, T., Johnson, M. W. G., Kent, S., Krause, E., Kron, R., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Lahav, O., Lima, M., Maia, M. A. G., March, M., Miller, C. J., Miquel, R., Neilsen, E., Nord, B., Ogando, R. L. C., Plazas, A. A., Romer, A. K., Roodman, A., Rykoff, E. S., Sanchez, E., Scarpine, V., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Suchyta, E., Tarle, G., Thomas, D., Thomas, R. C., Troxel, M. A., Tucker, D. L., Vikram, V., Walker, A. R., Weller, J., Zhang, Y., Haislip, J. B., Kouprianov, V. V., Reichart, D. E., Tartaglia, L., Sand, D. J., Valenti, S., Yang, S., Arcavi, Iair, Hosseinzadeh, Griffin, Howell, D. Andrew, McCully, Curtis, Poznanski, Dovi, Vasylyev, Sergiy, Tanvir, N. R., Levan, A. J., Hjorth, J., Cano, Z., Copperwheat, C., de Ugarte-Postigo, A., Evans, P. A., Fynbo, J. P. U., González-Fernández, C., Greiner, J., Irwin, M., Lyman, J., Mandel, I., McMahon, R., Milvang-Jensen, B., O'Brien, P., Osborne, J. P., Perley, D. A., Pian, E., Palazzi, E., Rol, E., Rosetti, S., Rosswog, S., Rowlinson, A., Schulze, S., Steeghs, D. T. H., Thöne, C. C., Ulaczyk, K., Watson, D., Wiersema, K., Lipunov, V. M., Gorbovskoy, E., Kornilov, V. G., Tyurina, N ., Balanutsa, P., Vlasenko, D., Gorbunov, I., Podesta, R., Levato, H., Saffe, C., Buckley, D. A. H., Budnev, N. M., Gress, O., Yurkov, V., Rebolo, R., and Serra-Ricart, M.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The detection of GW170817 in both gravitational waves and electromagnetic waves heralds the age of gravitational-wave multi-messenger astronomy. On 17 August 2017 the Advanced LIGO and Virgo detectors observed GW170817, a strong signal from the merger of a binary neutron-star system. Less than 2 seconds after the merger, a gamma-ray burst (GRB 170817A) was detected within a region of the sky consistent with the LIGO-Virgo-derived location of the gravitational-wave source. This sky region was subsequently observed by optical astronomy facilities, resulting in the identification of an optical transient signal within $\sim 10$ arcsec of the galaxy NGC 4993. These multi-messenger observations allow us to use GW170817 as a standard siren, the gravitational-wave analog of an astronomical standard candle, to measure the Hubble constant. This quantity, which represents the local expansion rate of the Universe, sets the overall scale of the Universe and is of fundamental importance to cosmology. Our measurement combines the distance to the source inferred purely from the gravitational-wave signal with the recession velocity inferred from measurements of the redshift using electromagnetic data. This approach does not require any form of cosmic "distance ladder;" the gravitational wave analysis can be used to estimate the luminosity distance out to cosmological scales directly, without the use of intermediate astronomical distance measurements. We determine the Hubble constant to be $70.0^{+12.0}_{-8.0} \, \mathrm{km} \, \mathrm{s}^{-1} \, \mathrm{Mpc}^{-1}$ (maximum a posteriori and 68% credible interval). This is consistent with existing measurements, while being completely independent of them. Additional standard-siren measurements from future gravitational-wave sources will provide precision constraints of this important cosmological parameter., Comment: 26 pages, 5 figures, Nature in press. For more information see https://dcc.ligo.org/LIGO-P1700296/public

Published
2017
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43. The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. I. Dark Energy Camera Discovery of the Optical Counterpart

Author

Soares-Santos, M., Holz, D. E., Annis, J., Chornock, R., Herner, K., Berger, E., Brout, D., Chen, H., Kessler, R., Sako, M., Allam, S., Tucker, D. L., Butler, R. E., Palmese, A., Doctor, Z., Diehl, H. T., Frieman, J., Yanny, B., Lin, H., Scolnic, D., Cowperthwaite, P., Neilsen, E., Marriner, J., Kuropatkin, N., Hartley, W. G., Paz-Chinchón, F., Alexander, K. D., Balbinot, E., Blanchard, P., Brown, D. A., Carlin, J. L., Conselice, C., Cook, E. R., Drlica-Wagner, A., Drout, M. R., Durret, F., Eftekhari, T., Farr, B., Finley, D. A., Foley, R. J., Fong, W., Fryer, C. L., García-Bellido, J., Gill, M. S . S., Gruendl, R. A., Hanna, C., Kasen, D., Li, T. S., Lopes, P. A. A., Lourenço, A. C. C., Margutti, R., Marshall, J. L., Matheson, T., Medina, G. E., Metzger, B. D., Muñoz, R. R., Muir, J., Nicholl, M., Quataert, E., Rest, A., Sauseda, M., Schlegel, D. J., Secco, L. F., Sobreira, F., Stebbins, A., Villar, V. A., Walker, A. R., Wester, W., Williams, P. K. G., Zenteno, A., Zhang, Y., Abbott, T. M. C., Abdalla, F. B., Banerji, M., Bechtol, K., Benoit-Lévy, A., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Crocce, M., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., Desai, S., Dietrich, J. P., Doel, P., Eifler, T. F., Fernandez, E., Flaugher, B., Fosalba, P., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Goldstein, D. A., Gruen, D., Gschwend, J., Gutierrez, G., Honscheid, K., Jain, B., James, D. J., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Kent, S., Krause, E., Kron, R., Kuehn, K., Kuhlmann, S., Lahav, O., Lima, M., Maia, M. A. G., March, M., McMahon, R. G., Menanteau, F., Miquel, R., Mohr, J. J., Nichol, R. C., Nord, B., Ogando, R. L. C., Petravick, D., Plazas, A. A., Romer, A. K., Roodman, A., Rykoff, E. S., Sanchez, E., Scarpine, V., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Thomas, R. C., Troxel, M. A., Vikram, V., Wechsler, R. H., and Weller, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the Dark Energy Camera (DECam) discovery of the optical counterpart of the first binary neutron star merger detected through gravitational wave emission, GW170817. Our observations commenced 10.5 hours post-merger, as soon as the localization region became accessible from Chile. We imaged 70 deg$^2$ in the $i$ and $z$ bands, covering 93\% of the initial integrated localization probability, to a depth necessary to identify likely optical counterparts (e.g., a kilonova). At 11.4 hours post-merger we detected a bright optical transient located $10.6''$ from the nucleus of NGC\,4993 at redshift $z=0.0098$, consistent (for $H_0 = 70$\, km s$^{-1}$ Mpc$^{-1}$) with the distance of $40 \pm 8$\, Mpc reported by the LIGO Scientific Collaboration and the Virgo Collaboration (LVC). At detection the transient had magnitudes $i\approx 17.30$ and $z\approx 17.45$, and thus an absolute magnitude of $M_i = -15.7$, in the luminosity range expected for a kilonova. We identified 1,500 potential transient candidates. Applying simple selection criteria aimed at rejecting background events such as supernovae, we find the transient associated with NGC\,4993 as the only remaining plausible counterpart, and reject chance coincidence at the 99.5\% confidence level. We therefore conclude that the optical counterpart we have identified near NGC\,4993 is associated with GW170817. This discovery ushers in the era of multi-messenger astronomy with gravitational waves, and demonstrates the power of DECam to identify the optical counterparts of gravitational-wave sources., Comment: 9 Pages, 3 Figures, 2 Tables. ApJL, In Press. Keywords: GW170817, LVC

Published
2017
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44. Density split statistics: joint model of counts and lensing in cells

Author

Friedrich, O., Gruen, D., DeRose, J., Kirk, D., Krause, E., McClintock, T., Rykoff, E. S., Seitz, S., Wechsler, R. H., Bernstein, G. M., Blazek, J., Chang, C., Hilbert, S., Jain, B., Kovacs, A., Lahav, O., Abdalla, F. B., Allam, S., Annis, J., Bechtol, K., Benoit-Levy, A., Bertin, E., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., Desai, S., Diehl, H. T., Dietrich, J. P., Drlica-Wagner, A., Eifler, T. F., Fosalba, P., Frieman, J., Garcia-Bellido, J., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Gruendl, R. A., Gschwend, J., Gutierrez, G., Honscheid, K., James, D. J., Jarvis, M., Kuehn, K., Kuropatkin, N., Lima, M., March, M., Marshall, J. L., Melchior, P., Menanteau, F., Miquel, R., Mohr, J. J., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Sevilla-Noarbe, I., Sheldon, E., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Troxel, M. A., Vikram, V., and Weller, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present density split statistics, a framework that studies lensing and counts-in-cells as a function of foreground galaxy density, thereby providing a large-scale measurement of both 2-point and 3-point statistics. Our method extends our earlier work on trough lensing and is summarized as follows: given a foreground (low redshift) population of galaxies, we divide the sky into subareas of equal size but distinct galaxy density. We then measure lensing around uniformly spaced points separately in each of these subareas, as well as counts-in-cells statistics (CiC). The lensing signals trace the matter density contrast around regions of fixed galaxy density. Through the CiC measurements this can be related to the density profile around regions of fixed matter density. Together, these measurements constitute a powerful probe of cosmology, the skewness of the density field and the connection of galaxies and matter. In this paper we show how to model both the density split lensing signal and CiC from basic ingredients: a non-linear power spectrum, clustering hierarchy coefficients from perturbation theory and a parametric model for galaxy bias and shot-noise. Using N-body simulations, we demonstrate that this model is sufficiently accurate for a cosmological analysis on year 1 data from the Dark Energy Survey., Comment: 21 pages + appendix, accepted for publication in PRD

Published
2017
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45. Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts in the DES -- Calibration of the Weak Lensing Source Redshift Distributions

Author

Davis, C., Gatti, M., Vielzeuf, P., Cawthon, R., Rozo, E., Alarcon, A., Bernstein, G. M., Bonnett, C., Rosell, A. Carnero, Castander, F. J., Chang, C., da Costa, L. N., Davis, T. M., De Vicente, J., DeRose, J., Drlica-Wagner, A., Elvin-Poole, J., Gaztanaga, E., Gruen, D., Gschwend, J., Hartley, W. G., Hoyle, B., Lin, H., Maia, M. A. G., Miquel, R., Ogando, R. L. C., Rau, M. M., Roodman, A., Rykoff, E. S., Sevilla-Noarbe, I., Troxel, M. A., Wechsler, R. H., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Bechtol, K., Benoit-Lévy, A., Brooks, D., Buckley-Geer, E., Burke, D. L., Kind, M. Carrasco, Carretero, J., Crocce, M., Cunha, C. E., Desai, S., Diehl, H. T., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., García-Bellido, J., Gerdes, D. W., Gruendl, R. A., Gutierrez, G., Honscheid, K., James, D. J., Jeltema, T., Krause, E., Kron, R., Kuehn, K., Kuropatkin, N., Lahav, O., Lima, M., March, M., Marshall, J. L., Menanteau, F., Nichol, R. C., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, D. L., Vikram, V., Walker, A. R., and Zuntz, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present the calibration of the Dark Energy Survey Year 1 (DES Y1) weak lensing source galaxy redshift distributions from clustering measurements. By cross-correlating the positions of source galaxies with luminous red galaxies selected by the redMaGiC algorithm we measure the redshift distributions of the source galaxies as placed into different tomographic bins. These measurements constrain any such shifts to an accuracy of $\sim0.02$ and can be computed even when the clustering measurements do not span the full redshift range. The highest-redshift source bin is not constrained by the clustering measurements because of the minimal redshift overlap with the redMaGiC galaxies. We compare our constraints with those obtained from $\texttt{COSMOS}$ 30-band photometry and find that our two very different methods produce consistent constraints., Comment: 13 pages, 7 figures. Submitting to MNRAS. Feedback welcome!

Published
2017

46. Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization

Author

Gatti, M., Vielzeuf, P., Davis, C., Cawthon, R., Rau, M. M., DeRose, J., De Vicente, J., Alarcon, A., Rozo, E., Gaztanaga, E., Hoyle, B., Miquel, R., Bernstein, G. M., Bonnett, C., Rosell, A. Carnero, Castander, F. J., Chang, C., da Costa, L. N., Gruen, D., Gschwend, J., Hartley, W. G., Lin, H., MacCrann, N., Maia, M. A. G., Ogando, R. L. C., Roodman, A., Sevilla-Noarbe, I., Troxel, M. A., Wechsler, R. H., Asorey, J., Davis, T. M., Glazebrook, K., Hinton, S. R., Lewis, G., Lidman, C., Macaulay, E., Möller, A., O'Neill, C. R., Sommer, N. E., Uddin, S. A., Yuan, F., Zhang, B., Abbott, T. M. C., Allam, S., Annis, J., Bechtol, K., Brooks, D., Burke, D. L., Carollo, D., Kind, M. Carrasco, Carretero, J., Cunha, C. E., D'Andrea, C. B., DePoy, D. L., Desai, S., Eifler, T. F., Evrard, A. E., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Goldstein, D. A., Gruendl, R. A., Gutierrez, G., Honscheid, K., Hoormann, J. K., Jain, B., James, D. J., Jarvis, M., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Krause, E., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Li, T. S., Lima, M., Marshall, J. L., Melchior, P., Menanteau, F., Nichol, R. C., Nord, B., Plazas, A. A., Reil, K., Rykoff, E. S., Sako, M., Sanchez, E., Scarpine, V., Schubnell, M., Sheldon, E., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, B. E., Tucker, D. L., Vikram, V., Walker, A. R., Weller, J., Wester, W., and Wolf, R. C.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-$z$ methods applied to the same source galaxy sample. We apply the method to three photo-$z$ codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhood Fitting (DNF), and Random Forest-based photo-$z$ (RF). We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering vs photo-$z$'s. The systematic uncertainty in the mean redshift bias of the source galaxy sample is $\Delta z \lesssim 0.02$, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses., Comment: submitted to MNRAS

Published
2017
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47. OzDES multifibre spectroscopy for the Dark Energy Survey: Three year results and first data release

Author

Childress, M. J., Lidman, C., Davis, T. M., Tucker, B. E., Asorey, J., Yuan, F., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Banerji, M., Benoit-Levy, A., Bernard, S. R., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Carollo, D., Kind, M. Carrasco, Carretero, J., Castander, F. J., Cunha, C. E., da Costa, L. N., D'Andrea, C. B., Doel, P., Eifler, T. F., Evrard, A. E., Flaugher, B., Foley, R. J., Fosalba, P., Frieman, J., Garcia-Bellido, J., Glazebrook, K., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gschwend, J., Gupta, R. R., Gutierrez, G., Hinton, S. R., Hoormann, J. K., James, D. J., Kessler, R., Kim, A. G., King, A. L., Kovacs, E., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Lagattuta, D. J., Lewis, G. F., Li, T. S., Lima, M., Lin, H., Macaulay, E., Maia, M. A. G., Marriner, J., March, M., Marshall, J. L., Martini, P., McMahon, R. G., Menanteau, F., Miquel, R., Moller, A., Morganson, E., Mould, J., Mudd, D., Muthukrishna, D., Nichol, R. C., Nord, B., Ogando, R. L. C., Ostrovski, F., Parkinson, D., Plazas, A. A., Reed, S. L., Reil, K., Romer, A. K., Rykoff, E. S., Sako, M., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Scolnic, D., Sevilla-Noarbe, I., Seymour, N., Sharp, R., Smith, M., Soares-Santos, M., Sobreira, F., Sommer, N. E., Spinka, H., Suchyta, E., Sullivan, M., Swanson, M. E. C., Tarle, G., Uddin, S. A., Walker, A. R., Wester, W., and Zhang, B. R.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

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 multi-year 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 2,566 supernova hosts. We examine how our ability to measure redshifts for targets of various types depends on signal-to-noise, magnitude, and exposure time, finding that our redshift success rate increases significantly at a signal-to-noise of 2 to 3 per 1-Angstrom bin. We also find that the change in signal-to-noise with exposure time closely matches the Poisson limit for stacked exposures as long as 10 hours. 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 the 4m Multi-Object Spectroscopic Telescope (4MOST), the Subaru Prime Focus Spectrograph (PFS), and the Maunakea Spectroscopic Explorer (MSE). This work marks the first OzDES data release, comprising 14,693 redshifts. OzDES is on target to obtain over a yield of approximately 5,700 supernova host-galaxy redshifts., Comment: Accepted for publication in MNRAS. Redshift data release is available at http://www.mso.anu.edu.au/ozdes/DR1

Published
2017
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48. Chemical Abundance Analysis of Three $\alpha$-Poor, Metal-Poor Stars in the Ultra-Faint Dwarf Galaxy Horologium I

Author

Nagasawa, D. Q., Marshall, J. L., Simon, J. D., Hansen, T. T., Li, T. S., Bernstein, R. A., Balbinot, E., Drlica-Wagner, A., Pace, A. B., Strigari, L. E., Pellegrino, C. M., DePoy, D. L., Suntzeff, N. B., Bechtol, K., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Benoit-Lévy, A., Bertin, E., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., Desai, S., Doel, P., Eifler, T. F., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Honscheid, K., James, D. J., Jeltema, T., Krause, E., Kuehn, K., Kuhlmann, S., Kuropatkin, N., March, M., Miquel, R., Nord, B., Roodman, A., Sanchez, E., Santiago, B., Scarpine, V., Schindler, R., Schubnell, M., Sevilla-Noarbe, I., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Tarle, G., Thomas, D., Tucker, D. L., Walker, A. R., Wechsler, R. H., Wolf, R. C., and Yanny, B.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

We present chemical abundance measurements of three stars in the ultra-faint dwarf galaxy Horologium I, a Milky Way satellite discovered by the Dark Energy Survey. Using high resolution spectroscopic observations we measure the metallicity of the three stars as well as abundance ratios of several $\alpha$-elements, iron-peak elements, and neutron-capture elements. The abundance pattern is relatively consistent among all three stars, which have a low average metallicity of [Fe/H] $\sim -2.6$ and are not $\alpha$-enhanced ([$\alpha$/Fe] $\sim 0.0$). This result is unexpected when compared to other low-metallicity stars in the Galactic halo and other ultra-faint dwarfs and hints at an entirely different mechanism for the enrichment of Hor I compared to other satellites. We discuss possible scenarios that could lead to this observed nucleosynthetic signature including extended star formation, a Population III supernova, and a possible association with the Large Magellanic Cloud.

Published
2017
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49. Dark Energy Survey Year 1 Results: Cosmological Constraints from Cosmic Shear

Author

Troxel, M. A., MacCrann, N., Zuntz, J., Eifler, T. F., Krause, E., Dodelson, S., Gruen, D., Blazek, J., Friedrich, O., Samuroff, S., Prat, J., Secco, L. F., Davis, C., Ferté, A., DeRose, J., Alarcon, A., Amara, A., Baxter, E., Becker, M. R., Bernstein, G. M., Bridle, S. L., Cawthon, R., Chang, C., Choi, A., De Vicente, J., Drlica-Wagner, A., Elvin-Poole, J., Frieman, J., Gatti, M., Hartley, W. G., Honscheid, K., Hoyle, B., Huff, E. M., Huterer, D., Jain, B., Jarvis, M., Kacprzak, T., Kirk, D., Kokron, N., Krawiec, C., Lahav, O., Liddle, A. R., Peacock, J., Rau, M. M., Refregier, A., Rollins, R. P., Rozo, E., Rykoff, E. S., Sánchez, C., Sevilla-Noarbe, I., Sheldon, E., Stebbins, A., Varga, T. N., Vielzeuf, P., Wang, M., Wechsler, R. H., Yanny, B., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Bechtol, K., Benoit-Lévy, A., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Castander, F. J., Crocce, M., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., DePoy, D. L., Desai, S., Diehl, H. T., Dietrich, J. P., Doel, P., Fernandez, E., Flaugher, B., Fosalba, P., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Giannantonio, T., Goldstein, D. A., Gruendl, R. A., Gschwend, J., Gutierrez, G., James, D. J., Jeltema, T., Johnson, M. W. G., Johnson, M. D., Kent, S., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Li, T. S., Lima, M., Lin, H., Maia, M. A. G., March, M., Marshall, J. L., Martini, P., Melchior, P., Menanteau, F., Miquel, R., Mohr, J. J., Neilsen, E., Nichol, R. C., Nord, B., Petravick, D., Plazas, A. A., Romer, A. K., Roodman, A., Sako, M., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, D. L., Vikram, V., Walker, A. R., Weller, J., and Zhang, Y.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We use 26 million galaxies from the Dark Energy Survey (DES) Year 1 shape catalogs over 1321 deg$^2$ of the sky to produce the most significant measurement of cosmic shear in a galaxy survey to date. We constrain cosmological parameters in both the flat $\Lambda$CDM and $w$CDM models, while also varying the neutrino mass density. These results are shown to be robust using two independent shape catalogs, two independent \photoz\ calibration methods, and two independent analysis pipelines in a blind analysis. We find a 3.5\% fractional uncertainty on $\sigma_8(\Omega_m/0.3)^{0.5} = 0.782^{+0.027}_{-0.027}$ at 68\% CL, which is a factor of 2.5 improvement over the fractional constraining power of our DES Science Verification results. In $w$CDM, we find a 4.8\% fractional uncertainty on $\sigma_8(\Omega_m/0.3)^{0.5} = 0.777^{+0.036}_{-0.038}$ and a dark energy equation-of-state $w=-0.95^{+0.33}_{-0.39}$. We find results that are consistent with previous cosmic shear constraints in $\sigma_8$ -- $\Omega_m$, and see no evidence for disagreement of our weak lensing data with data from the CMB. Finally, we find no evidence preferring a $w$CDM model allowing $w\ne -1$. We expect further significant improvements with subsequent years of DES data, which will more than triple the sky coverage of our shape catalogs and double the effective integrated exposure time per galaxy., Comment: 32 pages, 19 figures; matches PRD referee response version

Published
2017
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50. Dark Energy Survey Year 1 Results: Galaxy clustering for combined probes

Author

Elvin-Poole, J., Crocce, M., Ross, A. J., Giannantonio, T., Rozo, E., Rykoff, E. S., Avila, S., Banik, N., Blazek, J., Bridle, S. L., Cawthon, R., Drlica-Wagner, A., Friedrich, O., Kokron, N., Krause, E., MacCrann, N., Prat, J., Sanchez, C., Secco, L. F., Sevilla-Noarbe, I., Troxel, M. A., Abbott, T. M. C., Abdalla, F. B., Allam, S., Annis, J., Asorey, J., Bechtol, K., Becker, M. R., Benoit-Levy, A., Bernstein, G. M., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Rosell, A. Carnero, Carollo, D., Kind, M. Carrasco, Carretero, J., Castander, F. J., Cunha, C. E., DAndrea, C. B., da Costa, L. N., Davis, T. M., Davis, C., Desai, S., Diehl, H. T., Dietrich, J. P., Dodelson, S., Doel, P., Eifler, T. F., Evrard, A. E., Fernandez, E., Flaugher, B., Fosalba, P., Frieman, J., Garcia-Bellido, J., Gaztanaga, E., Gerdes, D. W., Glazebrook, K., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hinton, S. R., Honscheid, K., Hoormann, J. K., Jain, B., James, D. J., Jarvis, M., Jeltema, T., Johnson, M. W. G., Johnson, M. D., King, A., Kuehn, K., Kuhlmann, S., Kuropatkin, N., Lahav, O., Lewis, G., Li, T. S., Lidman, C., Lima, M., Lin, H., Macaulay, E., March, M., Marshall, J. L., Martini, P., Melchior, P., Menanteau, F., Miquel, R., Mohr, J. J., Moller, A., Nichol, R. C., Nord, B., ONeill, C. R., Percival, W. J., Petravick, D., Plazas, A. A., Romer, A. K., Sako, M., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Sheldon, E., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Sommer, N. E., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, B. E., Tucker, D. L., Uddin, S. A., Vikram, V., Walker, A. R., Wechsler, R. H., Weller, J., Wester, W., Wolf, R. C., Yuan, F., Zhang, B., and Zuntz, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We measure the clustering of DES Year 1 galaxies that are intended to be combined with weak lensing samples in order to produce precise cosmological constraints from the joint analysis of large-scale structure and lensing correlations. Two-point correlation functions are measured for a sample of $6.6 \times 10^{5}$ luminous red galaxies selected using the \textsc{redMaGiC} algorithm over an area of $1321$ square degrees, in the redshift range $0.15 < z < 0.9$, split into five tomographic redshift bins. The sample has a mean redshift uncertainty of $\sigma_{z}/(1+z) = 0.017$. We quantify and correct spurious correlations induced by spatially variable survey properties, testing their impact on the clustering measurements and covariance. We demonstrate the sample's robustness by testing for stellar contamination, for potential biases that could arise from the systematic correction, and for the consistency between the two-point auto- and cross-correlation functions. We show that the corrections we apply have a significant impact on the resultant measurement of cosmological parameters, but that the results are robust against arbitrary choices in the correction method. We find the linear galaxy bias in each redshift bin in a fiducial cosmology to be $b(z$=$0.24)=1.40 \pm 0.08$, $b(z$=$0.38)=1.61 \pm 0.05$, $b(z$=$0.53)=1.60 \pm 0.04$ for galaxies with luminosities $L/L_*>$$0.5$, $b(z$=$0.68)=1.93 \pm 0.05$ for $L/L_*>$$1$ and $b(z$=$0.83)=1.99 \pm 0.07$ for $L/L_*$$>1.5$, broadly consistent with expectations for the redshift and luminosity dependence of the bias of red galaxies. We show these measurements to be consistent with the linear bias obtained from tangential shear measurements., Comment: 20 pages, 13 figures. Version accepted by PRD, minor changes to text, covariance matrix updated, conclusions unchanged

Published
2017
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