[en] Here, we present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution (HOD), the conditional luminosity function (CLF), abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos, or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. Here, the package has an optimized toolkit to make mock observations on a synthetic galaxy population, including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others, allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation.

[en] Here, we describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (2013 September through 2014 February) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1.0% of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithm's performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at at http://portal.nersc.gov/project/dessn/autoscan.

[en] Many scientific goals for the Dark Energy Survey (DES) require calibration of optical/NIR broadband b=grizY photometry that is stable in time and uniform over the celestial sky to one percent or better. It is also necessary to limit to similar accuracy systematic uncertainty in the calibrated broadband magnitudes due to uncertainty in the spectrum of the source. Here we present a "Forward Global Calibration Method (FGCM)" for photometric calibration of the DES, and we present results of its application to the first three years of the survey (Y3A1). The FGCM combines data taken with auxiliary instrumentation at the observatory with data from the broad-band survey imaging itself and models of the instrument and atmosphere to estimate the spatial- and time-dependence of the passbands of individual DES survey exposures. "Standard" passbands are chosen that are typical of the passbands encountered during the survey. The passband of any individual observation is combined with an estimate of the source spectral shape to yield a magnitude m_b^std in the standard system. This "chromatic correction" to the standard system is necessary to achieve sub-percent calibrations. The FGCM achieves reproducible and stable photometric calibration of standard magnitudes m_b^std of stellar sources over the multi-year Y3A1 data sample with residual random calibration errors of σ=5−6 mmag per exposure. In conclusion, the accuracy of the calibration is uniform across the 5000 deg² DES footprint to within σ=7 mmag. The systematic uncertainties of magnitudes in the standard system due to the spectra of sources are less than 5 mmag for main sequence stars with 0.5< g−i<3.0.

[en] “Cosmological redshift surveys” are experiments conducted with astronomical telescopes, imagers, and spectrographs, which map the three-dimensional structure of the universe on the largest scales. These maps are delineated by the positions of galaxies, quasars, and intergalactic hydrogen clouds. When interpreted in the context of Einstein’s theory of gravity, these maps can be used to infer the nature of the contents of the universe, including the mysterious “dark energy” that is driving the expansion of the universe to accelerate. While the directional positions of galaxies and other objects can be measured directly in images of the sky, the third dimension of their position (i.e., their distance from the Earth and the Milky Way Galaxy) must be measured by spectrographs that distribute their light as a function of frequency, enabling a measurement of their cosmological Doppler shift (or “redshift”), which serves as an observable proxy for distance. The largest cosmological redshift surveys, such as the “eBOSS” experiment of the fourth Sloan Digital Sky Survey, collect spectroscopic data for hundreds of thousands to millions of galaxies. Future experiments such as the Dark Energy Spectroscopic Instrument will in turn collect tens of millions of spectra. To be feasible, redshift measurement methods in datasets of this scale must be made with automated software. This paper describes the algorithms, astrophysical templates, and implementation of a new redshift measurement software package that is optimized to run on large numbers of spectra with relatively low signal-to-noise ratio, typical of the most ambitious current and future cosmological redshift surveys. The software is demonstrated on spectroscopic data from the eBOSS survey, with performance that meets the scientific requirements of that experiment. The software is implemented in a general framework that will allow application to spectra from the DESI project in the future.

[en] This paper presents the non-homogeneous Poisson process (NHPP) for modeling the rate of fast radio bursts (FRBs) and other infrequently observed astronomical events. The NHPP, well-known in statistics, can model dependence of the rate on both astronomical features and the details of an observing campaign. This is particularly helpful for rare events like FRBs because the NHPP can combine information across surveys, making the most of all available information. The goal of the paper is two-fold. First, it is intended to be a tutorial on the use of the NHPP. Second, we build an NHPP model that incorporates beam patterns and a power law flux distribution for the rate of FRBs. Using information from 12 surveys including 15 detections, we find an all-sky FRB rate of 587 events per sky per day above a flux of 1 Jy (95% CI: 272, 924) and a flux power-law index of 0:91 (95% CI: 0.57, 1.25).

[en] A principal scientific goal of the Gemini Planet Imager (GPI) is obtaining milliarcsecond astrometry to constrain exoplanet orbits. However, astrometry of directly imaged exoplanets is subject to biases, systematic errors, and speckle noise. Here, we describe an analytical procedure to forward model the signal of an exoplanet that accounts for both the observing strategy (angular and spectral differential imaging) and the data reduction method (Karhunen–Loève Image Projection algorithm). We use this forward model to measure the position of an exoplanet in a Bayesian framework employing Gaussian processes and Markov-chain Monte Carlo to account for correlated noise. In the case of GPI data on β Pic b, this technique, which we call Bayesian KLIP-FM Astrometry (BKA), outperforms previous techniques and yields 1σ errors at or below the one milliarcsecond level. We validate BKA by fitting a Keplerian orbit to 12 GPI observations along with previous astrometry from other instruments. The statistical properties of the residuals confirm that BKA is accurate and correctly estimates astrometric errors. Our constraints on the orbit of β Pic b firmly rule out the possibility of a transit of the planet at 10-σ significance. However, we confirm that the Hill sphere of β Pic b will transit, giving us a rare chance to probe the circumplanetary environment of a young, evolving exoplanet. As a result, we provide an ephemeris for photometric monitoring of the Hill sphere transit event, which will begin at the start of April in 2017 and finish at the end of January in 2018.

[en] We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface, we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, two- and three-point correlation functions, a friends-of-friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enables nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high-performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.

[en] The MaNGA Survey (Mapping Nearby Galaxies at Apache Point Observatory) is one of three core programs in the Sloan Digital Sky Survey IV. It is obtaining integral field spectroscopy for 10,000 nearby galaxies at a spectral resolution of R  ∼ 2000 from 3622 to 10354 Å. The design of the survey is driven by a set of science requirements on the precision of estimates of the following properties: star formation rate surface density, gas metallicity, stellar population age, metallicity, and abundance ratio, and their gradients; stellar and gas kinematics; and enclosed gravitational mass as a function of radius. We describe how these science requirements set the depth of the observations and dictate sample selection. The majority of targeted galaxies are selected to ensure uniform spatial coverage in units of effective radius (R_e) while maximizing spatial resolution. About two-thirds of the sample is covered out to 1.5 R_e (Primary sample), and one-third of the sample is covered to 2.5 R_e (Secondary sample). We describe the survey execution with details that would be useful in the design of similar future surveys. We also present statistics on the achieved data quality, specifically the point-spread function, sampling uniformity, spectral resolution, sky subtraction, and flux calibration. For our Primary sample, the median r -band signal-to-noise ratio is ∼70 per 1.4 Å pixel for spectra stacked between 1 R_e and 1.5 R_e. Measurements of various galaxy properties from the first-year data show that we are meeting or exceeding the defined requirements for the majority of our science goals.

[en] We report radio SETI observations on a large number of known exoplanets and other nearby star systems using the Allen Telescope Array (ATA). Observations were made over about 19000 hr from 2009 May to 2015 December. This search focused on narrowband radio signals from a set totaling 9293 stars, including 2015 exoplanet stars and Kepler objects of interest and an additional 65 whose planets may be close to their habitable zones. The ATA observations were made using multiple synthesized beams and an anticoincidence filter to help identify terrestrial radio interference. Stars were observed over frequencies from 1 to 9 GHz in multiple bands that avoid strong terrestrial communication frequencies. Data were processed in near-real time for narrowband (0.7–100 Hz) continuous and pulsed signals with transmitter/receiver relative accelerations from −0.3 to 0.3 m s⁻². A total of 1.9 × 10⁸ unique signals requiring immediate follow-up were detected in observations covering more than 8 × 10⁶ star-MHz. We detected no persistent signals from extraterrestrial technology exceeding our frequency-dependent sensitivity threshold of 180–310 × 10⁻²⁶ W m⁻².

[en] We have used the Spitzer Space Telescope in 2016 February to obtain high cadence, high signal-to-noise, 17 hr duration light curves of Neptune at 3.6 and 4.5 μ m. The light curve duration was chosen to correspond to the rotation period of Neptune. Both light curves are slowly varying with time, with full amplitudes of 1.1 mag at 3.6 μ m and 0.6 mag at 4.5 μ m. We have also extracted sparsely sampled 18 hr light curves of Neptune at W1 (3.4 μ m) and W2 (4.6 μ m) from the Wide-feld Infrared Survey Explorer ( WISE )/ NEOWISE archive at six epochs in 2010–2015. These light curves all show similar shapes and amplitudes compared to the Spitzer light curves but with considerable variation from epoch to epoch. These amplitudes are much larger than those observed with Kepler / K 2 in the visible (amplitude ∼0.02 mag) or at 845 nm with the Hubble Space Telescope ( HST ) in 2015 and at 763 nm in 2016 (amplitude ∼0.2 mag). We interpret the Spitzer and WISE light curves as arising entirely from reflected solar photons, from higher levels in Neptune’s atmosphere than for K 2. Methane gas is the dominant opacity source in Neptune’s atmosphere, and methane absorption bands are present in the HST 763 and 845 nm, WISE W1, and Spitzer 3.6 μ m filters.