arXiv subjects

Andrew J. Connolly

Publications and source records attributed to Andrew J. Connolly.

At least 55 records · Page 3Linked to original sources

arXiv2018

A Framework for Telescope Schedulers: With Applications to the Large Synoptic Survey Telescope

How ground-based telescopes schedule their observations in response to competing science priorities and constraints, variations in the weather, and the visibility of a particular part of the sky can significantly impact their efficiency. In this paper we introduce the Feature-Based telescope scheduler that is an automated, proposal-free decision making algorithm that offers \textit{controllability} of the behavior, \textit{adjustability} of the mission, and quick \textit{recoverability} from interruptions for large ground-based telescopes. By framing this scheduler in the context of a coherent mathematical model the functionality and performance of the algorithm is simple to interpret and adapt to a broad range of astronomical applications. This paper presents a generic version of the Feature-Based scheduler, with minimal manual tailoring, to demonstrate its potential and flexibility as a foundation for large ground-based telescope schedulers which can later be adjusted for other instruments. In addition, a modified version of the Feature-Based scheduler for the Large Synoptic Survey Telescope (LSST) is introduced and compared to previous LSST scheduler simulations.

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arXiv2018

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pachón in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$σ$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $δ<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.

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arXiv2018

APO Time Resolved Color Photometry of Highly-Elongated Interstellar Object 1I/'Oumuamua

We report on $g$, $r$ and $i$ band observations of the Interstellar Object 'Oumuamua (1I) taken on 2017 October 29 from 04:28 to 08:40 UTC by the Apache Point Observatory (APO) 3.5m telescope's ARCTIC camera. We find that 1I's colors are $g-r=0.41\pm0.24$ and $r-i=0.23\pm0.25$, consistent with the visible spectra of Masiero (2017), Ye et al. (2017) and Fitzsimmons et al. (2017), and most comparable to the population of Solar System C/D asteroids, Trojans, or comets. We find no evidence of any cometary activity at a heliocentric distance of 1.46 au, approximately 1.5 months after 1I's closest approach distance to the Sun. Significant brightness variability was seen in the $r$ observations, with the object becoming notably brighter towards the end of the run. By combining our APO photometric time series data with the Discovery Channel Telescope (DCT) data of Knight et al. (2017), taken 20 h later on 2017 October 30, we construct an almost complete light curve with a most probable lightcurve period of $P \simeq 4~{\rm h}$. Our results imply a double peaked rotation period of 8.1 $\pm$ 0.02 h, with a peak-to-peak amplitude of 1.5 - 2.1 mags. Assuming that 1I's shape can be approximated by an ellipsoid, the amplitude constraint implies that 1I has an axial ratio of 3.5 to 10.3, which is strikingly elongated. Assuming that 1I is rotating above its critical break up limit, our results are compatible with 1I having having modest cohesive strength and may have obtained its elongated shape during a tidal disruption event before being ejected from its home system. Astrometry useful for constraining 1I's orbit was also obtained and published in Weaver et al. (2017).

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arXiv2017

Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies

In this paper we present and characterize a nearest-neighbors color-matching photometric redshift estimator that features a direct relationship between the precision and accuracy of the input magnitudes and the output photometric redshifts. This aspect makes our estimator an ideal tool for evaluating the impact of changes to LSST survey parameters that affect the measurement errors of the photometry, which is the main motivation of our work (i.e., it is not intended to provide the "best" photometric redshifts for LSST data). We show how the photometric redshifts will improve with time over the 10-year LSST survey and confirm that the nominal distribution of visits per filter provides the most accurate photo-$z$ results. The LSST survey strategy naturally produces observations over a range of airmass, which offers the opportunity of using an SED- and $z$-dependent atmospheric affect on the observed photometry as a color-independent redshift indicator. We show that measuring this airmass effect and including it as a prior has the potential to improve the photometric redshifts and can ameliorate extreme outliers, but that it will only be adequately measured for the brightest galaxies, which limits its overall impact on LSST photometric redshifts. We furthermore demonstrate how this airmass effect can induce a bias in the photo-$z$ results, and caution against survey strategies that prioritize high-airmass observations for the purpose of improving this prior. Ultimately, we intend for this work to serve as a guide for the expectations and preparations of the LSST science community with regards to the minimum quality of photo-$z$ as the survey progresses.

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arXiv2017

Scientific Synergy Between LSST and Euclid

Euclid and the Large Synoptic Survey Telescope (LSST) are poised to dramatically change the astronomy landscape early in the next decade. The combination of high cadence, deep, wide-field optical photometry from LSST with high resolution, wide-field optical photometry and near-infrared photometry and spectroscopy from Euclid will be powerful for addressing a wide range of astrophysical questions. We explore Euclid/LSST synergy, ignoring the political issues associated with data access to focus on the scientific, technical, and financial benefits of coordination. We focus primarily on dark energy cosmology, but also discuss galaxy evolution, transient objects, solar system science, and galaxy cluster studies. We concentrate on synergies that require coordination in cadence or survey overlap, or would benefit from pixel-level co-processing that is beyond the scope of what is currently planned, rather than scientific programs that could be accomplished only at the catalog level without coordination in data processing or survey strategies. We provide two quantitative examples of scientific synergies: the decrease in photo-z errors (benefitting many science cases) when high resolution Euclid data are used for LSST photo-z determination, and the resulting increase in weak lensing signal-to-noise ratio from smaller photo-z errors. We briefly discuss other areas of coordination, including high performance computing resources and calibration data. Finally, we address concerns about the loss of independence and potential cross-checks between the two missions and potential consequences of not collaborating.

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arXiv2017

Estimating Spectra from Photometry

Measuring the physical properties of galaxies such as redshift frequently requires the use of Spectral Energy Distributions (SEDs). SED template sets are, however, often small in number and cover limited portions of photometric color space. Here we present a new method to estimate SEDs as a function of color from a small training set of template SEDs. We first cover the mathematical background behind the technique before demonstrating our ability to reconstruct spectra based upon colors and then compare to other common interpolation and extrapolation methods. When the photometric filters and spectra overlap we show reduction of error in the estimated spectra of over 65% compared to the more commonly used techniques. We also show an expansion of the method to wavelengths beyond the range of the photometric filters. Finally, we demonstrate the usefulness of our technique by generating 50 additional SED templates from an original set of 10 and applying the new set to photometric redshift estimation. We are able to reduce the photometric redshifts standard deviation by at least 22.0% and the outlier rejected bias by over 86.2% compared to original set for z $\leq$ 3.

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arXiv2017

The Discovery of a Five-Image Lensed Quasar at z = 3.34 using PanSTARRS1 and Gaia

We report the discovery, spectroscopic confirmation, and mass modelling of the gravitationally lensed quasar system PS J0630-1201. The lens was discovered by matching a photometric quasar catalogue compiled from Pan-STARRS and WISE photometry to the Gaia DR1 catalogue, exploiting the high spatial resolution of the latter (FWHM $\sim $0.1") to identify the three brightest components of the lens. Follow-up spectroscopic observations with the WHT confirm the multiple objects are quasars at redshift $z_{q}=3.34$. Further follow-up with Keck AO high-resolution imaging reveals that the system is composed of two lensing galaxies and the quasar is lensed into a $\sim$2.8" separation four-image cusp configuration with a fifth image clearly visible, and a 1.0" arc due to the lensed quasar host galaxy. The system is well-modelled with two singular isothermal ellipsoids, reproducing the position of the fifth image. We discuss future prospects for measuring time delays between the images and constraining any offset between mass and light using the faintly detected Einstein arcs associated with the quasar host galaxy.

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arXiv2017

A hybrid type Ia supernova with an early flash triggered by helium-shell detonation

Type Ia supernovae (SNe Ia) arise from the thermonuclear explosion of carbon-oxygen white dwarfs. Though the uniformity of their light curves makes them powerful cosmological distance indicators, long-standing issues remain regarding their progenitors and explosion mechanisms. Recent detection of the early ultraviolet pulse of a peculiar subluminous SN Ia has been claimed as new evidence for the companion-ejecta interaction through the single-degenerate channel. Here, we report the discovery of a prominent but red optical flash at $\sim$ 0.5 days after the explosion of a SN Ia which shows hybrid features of different SN Ia sub-classes: a light curve typical of normal-brightness SNe Ia, but with strong titanium absorptions, commonly seen in the spectra of subluminous ones. We argue that the early flash of such a hybrid SN Ia is different from predictions of previously suggested scenarios such as the companion-ejecta interaction. Instead it can be naturally explained by a SN explosion triggered by a detonation of a thin helium shell either on a near-Chandrasekhar-mass white dwarf ($\gtrsim$ 1.3 M$_{\odot}$) with low-yield $^{56}$Ni or on a sub-Chandrasekhar-mass white dwarf ($\sim$ 1.0 M$_{\odot}$) merging with a less massive white dwarf. This finding provides compelling evidence that one branch of the previously proposed explosion models, the helium-ignition scenario, does exist in nature, and such a scenario may account for explosions of white dwarfs in a wider mass range in contrast to what was previously supposed.

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arXiv2017

Robust period estimation using mutual information for multi-band light curves in the synoptic survey era

The Large Synoptic Survey Telescope (LSST) will produce an unprecedented amount of light curves using six optical bands. Robust and efficient methods that can aggregate data from multidimensional sparsely-sampled time series are needed. In this paper we present a new method for light curve period estimation based on the quadratic mutual information (QMI). The proposed method does not assume a particular model for the light curve nor its underlying probability density and it is robust to non-Gaussian noise and outliers. By combining the QMI from several bands the true period can be estimated even when no single-band QMI yields the period. Period recovery performance as a function of average magnitude and sample size is measured using 30,000 synthetic multi-band light curves of RR Lyrae and Cepheid variables generated by the LSST Operations and Catalog simulators. The results show that aggregating information from several bands is highly beneficial in LSST sparsely-sampled time series, obtaining an absolute increase in period recovery rate up to 50%. We also show that the QMI is more robust to noise and light curve length (sample size) than the multiband generalizations of the Lomb Scargle and Analysis of Variance periodograms, recovering the true period in 10-30% more cases than its competitors. A python package containing efficient Cython implementations of the QMI and other methods is provided.

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arXiv2016

Everything we'd like to do with LSST data, but we don't know (yet) how

The Large Synoptic Survey Telescope (LSST), the next-generation optical imaging survey sited at Cerro Pachon in Chile, will provide an unprecedented database of astronomical measurements. The LSST design, with an 8.4m (6.7m effective) primary mirror, a 9.6 sq. deg. field of view, and a 3.2 Gigapixel camera, will allow about 10,000 sq. deg. of sky to be covered twice per night, every three to four nights on average, with typical 5-sigma depth for point sources of $r$=24.5 (AB). With over 800 observations in $ugrizy$ bands over a 10-year period, these data will enable a deep stack reaching $r$=27.5 (about 5 magnitudes deeper than SDSS) and faint time-domain astronomy. The measured properties of newly discovered and known astrometric and photometric transients will be publicly reported within 60 sec after observation. The vast database of about 30 trillion observations of 40 billion objects will be mined for the unexpected and used for precision experiments in astrophysics. In addition to a brief introduction to LSST, we discuss a number of astro-statistical challenges that need to be overcome to extract maximum information and science results from LSST dataset.

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arXiv2016

VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning

We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift $z_{s}=2.74$ and image separation of $2.9"$ lensed by a foreground $z_{l}=0.40$ elliptical galaxy. Since the images of gravitationally lensed quasars are the superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and $gi$ multicolour photometric observations from the Dark Energy Survey (DES), near IR $JK$ photometry from the VISTA Hemisphere Survey (VHS) and WISE mid IR photometry, we have identified a candidate system with two catalogue components with $i_{AB}=18.61$ and $i_{AB}=20.44$ comprised of an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as a lensed quasar with an emission line redshift of $z=2.739\pm0.003$ and a foreground early type galaxy with $z=0.400\pm0.002$. We model the system as a single isothermal ellipsoid and find the Einstein radius $θ_E \sim 1.47"$, enclosed mass $M_{enc} \sim 4 \times 10^{11}$M$_{\odot}$ and a time delay of $\sim$52 days. The relatively wide separation, month scale time delay duration and high redshift make this an ideal system for constraining the expansion rate beyond a redshift of 1.

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arXiv2015

The LSST Data Management System

The Large Synoptic Survey Telescope (LSST) is a large-aperture, wide-field, ground-based survey system that will image the sky in six optical bands from 320 to 1050 nm, uniformly covering approximately $18,000$deg$^2$ of the sky over 800 times. The LSST is currently under construction on Cerro Pachón in Chile, and expected to enter operations in 2022. Once operational, the LSST will explore a wide range of astrophysical questions, from discovering "killer" asteroids to examining the nature of Dark Energy. The LSST will generate on average 15 TB of data per night, and will require a comprehensive Data Management system to reduce the raw data to scientifically useful catalogs and images with minimum human intervention. These reductions will result in a real-time alert stream, and eleven data releases over the 10-year duration of LSST operations. To enable this processing, the LSST project is developing a new, general-purpose, high-performance, scalable, well documented, open source data processing software stack for O/IR surveys. Prototypes of this stack are already capable of processing data from existing cameras (e.g., SDSS, DECam, MegaCam), and form the basis of the Hyper-Suprime Cam (HSC) Survey data reduction pipeline.

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arXiv2014

Introduction to astroML: Machine Learning for Astrophysics

Astronomy and astrophysics are witnessing dramatic increases in data volume as detectors, telescopes and computers become ever more powerful. During the last decade, sky surveys across the electromagnetic spectrum have collected hundreds of terabytes of astronomical data for hundreds of millions of sources. Over the next decade, the data volume will enter the petabyte domain, and provide accurate measurements for billions of sources. Astronomy and physics students are not traditionally trained to handle such voluminous and complex data sets. In this paper we describe astroML; an initiative, based on Python and scikit-learn, to develop a compendium of machine learning tools designed to address the statistical needs of the next generation of students and astronomical surveys. We introduce astroML and present a number of example applications that are enabled by this package.

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arXiv2014

Determining Frequentist Confidence Limits Using a Directed Parameter Space Search

We consider the problem of inferring constraints on a high-dimensional parameter space with a computationally expensive likelihood function. We propose a machine learning algorithm that maps out the Frequentist confidence limit on parameter space by intelligently targeting likelihood evaluations so as to quickly and accurately characterize the likelihood surface in both low- and high-likelihood regions. We compare our algorithm to Bayesian credible limits derived by the well-tested Markov Chain Monte Carlo (MCMC) algorithm using both multi-modal toy likelihood functions and the 7-year WMAP cosmic microwave background likelihood function. We find that our algorithm correctly identifies the location, general size, and general shape of high-likelihood regions in parameter space while being more robust against multi-modality than MCMC.

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arXiv2013

Variability-based AGN selection using image subtraction in the SDSS and LSST era

With upcoming all sky surveys such as LSST poised to generate a deep digital movie of the optical sky, variability-based AGN selection will enable the construction of highly-complete catalogs with minimum contamination. In this study, we generate $g$-band difference images and construct light curves for QSO/AGN candidates listed in SDSS Stripe 82 public catalogs compiled from different methods, including spectroscopy, optical colors, variability, and X-ray detection. Image differencing excels at identifying variable sources embedded in complex or blended emission regions such as Type II AGNs and other low-luminosity AGNs that may be omitted from traditional photometric or spectroscopic catalogs. To separate QSOs/AGNs from other sources using our difference image light curves, we explore several light curve statistics and parameterize optical variability by the characteristic damping timescale ($τ$) and variability amplitude. By virtue of distinguishable variability parameters of AGNs, we are able to select them with high completeness of 93.4% and efficiency (i.e., purity) of 71.3%. Based on optical variability, we also select highly variable blazar candidates, whose infrared colors are consistent with known blazars. One third of them are also radio detected. With the X-ray selected AGN candidates, we probe the optical variability of X-ray detected optically-extended sources using their difference image light curves for the first time. A combination of optical variability and X-ray detection enables us to select various types of host-dominated AGNs. Contrary to the AGN unification model prediction, two Type II AGN candidates (out of 6) show detectable variability on long-term timescales like typical Type I AGNs. This study will provide a baseline for future optical variability studies of extended sources.

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arXiv2012

The DEEP2 Galaxy Redshift Survey: Design, Observations, Data Reduction, and Redshifts

We describe the design and data sample from the DEEP2 Galaxy Redshift Survey, the densest and largest precision-redshift survey of galaxies at z ~ 1 completed to date. The survey has conducted a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude M_B = -20 at z ~ 1 via ~90 nights of observation on the DEIMOS spectrograph at Keck Observatory. DEEP2 covers an area of 2.8 deg^2 divided into four separate fields, observed to a limiting apparent magnitude of R_AB=24.1. Objects with z < 0.7 are rejected based on BRI photometry in three of the four DEEP2 fields, allowing galaxies with z > 0.7 to be targeted ~2.5 times more efficiently than in a purely magnitude-limited sample. Approximately sixty percent of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets which fail to yield secure redshifts are blue objects that lie beyond z ~ 1.45. The DEIMOS 1200-line/mm grating used for the survey delivers high spectral resolution (R~6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. DEEP2 surpasses other deep precision-redshift surveys at z ~ 1 in terms of galaxy numbers, redshift accuracy, sample number density, and amount of spectral information. We also provide an overview of the scientific highlights of the DEEP2 survey thus far. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the publicly-available DEEP2 DEIMOS data reduction pipelines. [Abridged]

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arXiv2011

Pixel-z: Studying Substructure and Stellar Populations in Galaxies out to z~3 using Pixel Colors I. Systematics

We perform a pixel-by-pixel analysis of 467 galaxies in the GOODS-VIMOS survey to study systematic effects in extracting properties of stellar populations (age, dust, metallicity and SFR) from pixel colors using the pixel-z method. The systematics studied include the effect of the input stellar population synthesis model, passband limitations and differences between individual SED fits to pixels and global SED-fitting to a galaxy's colors. We find that with optical-only colors, the systematic errors due to differences among the models are well constrained. The largest impact on the age and SFR e-folding time estimates in the pixels arises from differences between the Maraston models and the Bruzual&Charlot models, when optical colors are used. This results in systematic differences larger than the 2σ uncertainties in over 10 percent of all pixels in the galaxy sample. The effect of restricting the available passbands is more severe. In 26 percent of pixels in the full sample, passband limitations result in systematic biases in the age estimates which are larger than the 2σ uncertainties. Systematic effects from model differences are reexamined using Near-IR colors for a subsample of 46 galaxies in the GOODS-NICMOS survey. For z > 1, the observed optical/NIR colors span the rest frame UV-optical SED, and the use of different models does not significantly bias the estimates of the stellar population parameters compared to using optical-only colors. We then illustrate how pixel-z can be applied robustly to make detailed studies of substructure in high redshift galaxies such as (a) radial gradients of age, SFR, sSFR and dust and (b) the distribution of these properties within subcomponents such as spiral arms and clumps. Finally, we show preliminary results from the CANDELS survey illustrating how the new HST/WFC3 data can be exploited to probe substructure in z~1-3 galaxies.

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arXiv2011

Milky Way Tomography IV: Dissecting Dust

We use SDSS photometry of 73 million stars to simultaneously obtain best-fit main-sequence stellar energy distribution (SED) and amount of dust extinction along the line of sight towards each star. Using a subsample of 23 million stars with 2MASS photometry, whose addition enables more robust results, we show that SDSS photometry alone is sufficient to break degeneracies between intrinsic stellar color and dust amount when the shape of extinction curve is fixed. When using both SDSS and 2MASS photometry, the ratio of the total to selective absorption, $R_V$, can be determined with an uncertainty of about 0.1 for most stars in high-extinction regions. These fits enable detailed studies of the dust properties and its spatial distribution, and of the stellar spatial distribution at low Galactic latitudes. Our results are in good agreement with the extinction normalization given by the Schlegel et al. (1998, SFD) dust maps at high northern Galactic latitudes, but indicate that the SFD extinction map appears to be consistently overestimated by about 20% in the southern sky, in agreement with Schlafly et al. (2010). The constraints on the shape of the dust extinction curve across the SDSS and 2MASS bandpasses support the models by Fitzpatrick (1999) and Cardelli et al. (1989). For the latter, we find an $R_V=3.0\pm0.1$(random) $\pm0.1$(systematic) over most of the high-latitude sky. At low Galactic latitudes (|b|<5), we demonstrate that the SFD map cannot be reliably used to correct for extinction as most stars are embedded in dust, rather than behind it. We introduce a method for efficient selection of candidate red giant stars in the disk, dubbed "dusty parallax relation", which utilizes a correlation between distance and the extinction along the line of sight. We make these best-fit parameters, as well as all the input SDSS and 2MASS data, publicly available in a user-friendly format.

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