Inter-cluster filaments in a Λ\LambdaCDM Universe

The large--scale structure (LSS) in the Universe comprises a complicated filamentary network of matter. We study this network using a high--resolution simulation of structure formation of a $\Lambda$ Cold Dark Matter cosmology. We investigate the distribution of matter between neighbouring large haloes whose masses are comparable to massive clusters of galaxies. We identify a total of 228 filaments between neighbouring clusters. Roughly half of the filaments are either warped or lie off the cluster--cluster axis. We find that straight filaments on the average are shorter than warped ones. More massive clusters are connected to more filaments than less massive ones on average. This finding indicates that the most massive clusters form at the intersections of the filamentary backbone of LSS. For straight filaments, we compute mass profiles. Radial profiles show a fairly well--defined radius, $r_s$, beyond which the profiles follow an $r^{-2}$ power law fairly closely. For the majority of filaments, $r_s$ lies between 1.5 $h^{-1}$ Mpc and 2.0 $h^{-1}$ Mpc. The enclosed overdensity inside $r_s$ varies between a few times up to 25 times mean density, independent of the length of the filaments. Along the filaments' axes, material is not distributed uniformly. Towards the clusters, the density rises, indicating the presence of the cluster infall regions. In addition, we also find some sheet--like connections between clusters. In roughly a fifth of all cluster--cluster connections where we could not identify a filament or sheet, projection effects lead to filamentary structures in the projected mass distribution. (abridged)Comment: 10 pages, 18 figures; submitted to MNRAS; updated: final version, accepted for publicatio

Improving the LSST dithering pattern and cadence for dark energy studies

The Large Synoptic Survey Telescope (LSST) will explore the entire southern sky over 10 years starting in 2022 with unprecedented depth and time sampling in six filters, $ugrizy$. Artificial power on the scale of the 3.5 deg LSST field-of-view will contaminate measurements of baryonic acoustic oscillations (BAO), which fall at the same angular scale at redshift $z \sim 1$. Using the HEALPix framework, we demonstrate the impact of an "un-dithered" survey, in which $17\%$ of each LSST field-of-view is overlapped by neighboring observations, generating a honeycomb pattern of strongly varying survey depth and significant artificial power on BAO angular scales. We find that adopting large dithers (i.e., telescope pointing offsets) of amplitude close to the LSST field-of-view radius reduces artificial structure in the galaxy distribution by a factor of $\sim$10. We propose an observing strategy utilizing large dithers within the main survey and minimal dithers for the LSST Deep Drilling Fields. We show that applying various magnitude cutoffs can further increase survey uniformity. We find that a magnitude cut of $r < 27.3$ removes significant spurious power from the angular power spectrum with a minimal reduction in the total number of observed galaxies over the ten-year LSST run. We also determine the effectiveness of the observing strategy for Type Ia SNe and predict that the main survey will contribute $\sim$100,000 Type Ia SNe. We propose a concentrated survey where LSST observes one-third of its main survey area each year, increasing the number of main survey Type Ia SNe by a factor of $\sim$1.5, while still enabling the successful pursuit of other science drivers.Comment: 9 pages, 6 figures, published in SPIE proceedings; corrected typo in equation

Simulated LSST Survey of RR Lyrae Stars throughout the Local Group

We report on a study to determine the efficiency of the Large Synoptic Survey Telescope (LSST) to recover the periods, brightnesses, and shapes of RR Lyrae stars' light curves in the volume extending to heliocentric distances of 1.5 Mpc. We place the smoothed light curves of 30 type ab and 10 type c RR Lyrae stars in 1007 fields across the sky, each of which represents a different realization of the LSST sampling cadences, and that sample five particular observing modes. A light curve simulation tool was used to sample the idealized RR Lyrae stars' light curves, returning each as it would have been observed by LSST, including realistic photometric scatter, limiting magnitudes, and telescope downtime. We report here the period, brightness, and light curve shape recovery as a function of apparent magnitude and for survey lengths varying from 1 to 10 years. We find that 10 years of LSST data are sufficient to recover the pulsation periods with a fractional precision of ~10^(–5) for ≥90% of ab stars within ≈360 kpc of the Sun in Universal Cadence fields and out to ≈760 kpc for Deep Drilling fields. The 50% completeness level extends to ≈600 kpc and ≈1.0 Mpc for the same fields, respectively. For virtually all stars that had their periods recovered, their light curve shape parameter φ_31 was recovered with sufficient precision to also recover photometric metallicities to within 0.14 dex (the systematic error in the photometric relations). With RR Lyrae stars' periods and metallicities well measured to these distances, LSST will be able to search for halo streams and dwarf satellite galaxies over half of the Local Group, informing galaxy formation models and providing essential data for mapping the Galactic potential. This study also informs the LSST science operations plan for optimizing observing strategies to achieve particular science goals. We additionally present a new [Fe/H]-φ_31 photometric relation in the r band and a new and generally useful metric for defining period recovery for time domain surveys

Agile software development in an earned value world: a survival guide

Agile methodologies are current best practice in software development. They are favored for, among other reasons, preventing premature optimization by taking a somewhat short-term focus, and allowing frequent replans/reprioritizations of upcoming development work based on recent results and current backlog. At the same time, funding agencies prescribe earned value management accounting for large projects which, these days, inevitably include substantial software components. Earned Value approaches emphasize a more comprehensive and typically longer-range plan, and tend to characterize frequent replans and reprioritizations as indicative of problems. Here we describe the planning, execution and reporting framework used by the LSST Data Management team, that navigates these opposite tensions

Investigating interoperability of the LSST Data Management software stack with Astropy

The Large Synoptic Survey Telescope (LSST) will be an 8.4m optical survey telescope sited in Chile and capable of imaging the entire sky twice a week. The data rate of approximately 15TB per night and the requirements to both issue alerts on transient sources within 60 seconds of observing and create annual data releases means that automated data management systems and data processing pipelines are a key deliverable of the LSST construction project. The LSST data management software has been in development since 2004 and is based on a C++ core with a Python control layer. The software consists of nearly a quarter of a million lines of code covering the system from fundamental WCS and table libraries to pipeline environments and distributed process execution. The Astropy project began in 2011 as an attempt to bring together disparate open source Python projects and build a core standard infrastructure that can be used and built upon by the astronomy community. This project has been phenomenally successful in the years since it has begun and has grown to be the de facto standard for Python software in astronomy. Astropy brings with it considerable expectations from the community on how astronomy Python software should be developed and it is clear that by the time LSST is fully operational in the 2020s many of the prospective users of the LSST software stack will expect it to be fully interoperable with Astropy. In this paper we describe the overlap between the LSST science pipeline software and Astropy software and investigate areas where the LSST software provides new functionality. We also discuss the possibilities of re-engineering the LSST science pipeline software to build upon Astropy, including the option of contributing affliated packages

The Zwicky Transient Facility Alert Distribution System

The Zwicky Transient Facility (ZTF) survey generates real-time alerts for optical transients, variables, and moving objects discovered in its wide-field survey. We describe the ZTF alert stream distribution and processing (filtering) system. The system uses existing open-source technologies developed in industry: Kafka, a real-time streaming platform, and Avro, a binary serialization format. The technologies used in this system provide a number of advantages for the ZTF use case, including (1) built-in replication, scalability, and stream rewind for the distribution mechanism; (2) structured messages with strictly enforced schemas and dynamic typing for fast parsing; and (3) a Python-based stream processing interface that is similar to batch for a familiar and user-friendly plug-in filter system, all in a modular, primarily containerized system. The production deployment has successfully supported streaming up to 1.2 million alerts or roughly 70 GB of data per night, with each alert available to a consumer within about 10 s of alert candidate production. Data transfer rates of about 80,000 alerts/minute have been observed. In this paper, we discuss this alert distribution and processing system, the design motivations for the technology choices for the framework, performance in production, and how this system may be generally suitable for other alert stream use cases, including the upcoming Large Synoptic Survey Telescope.Comment: Published in PASP Focus Issue on the Zwicky Transient Facility (doi: 10.1088/1538-3873/aae904). 9 Pages, 2 Figure

Software Architecture and System Design of Rubin Observatory

Starting from a description of the Rubin Observatory Data Management System Architecture, and drawing on our experience with and involvement in a range of other projects including Gaia, SDSS, UKIRT, and JCMT, we derive a series of generic design patterns and lessons learned.Comment: 10 pages ADASS XXXII submissio

Agile software development in an earned value world: a survival guide

Agile methodologies are current best practice in software development. They are favored for, among other reasons, preventing premature optimization by taking a somewhat short-term focus, and allowing frequent replans/reprioritizations of upcoming development work based on recent results and current backlog. At the same time, funding agencies prescribe earned value management accounting for large projects which, these days, inevitably include substantial software components. Earned Value approaches emphasize a more comprehensive and typically longer-range plan, and tend to characterize frequent replans and reprioritizations as indicative of problems. Here we describe the planning, execution and reporting framework used by the LSST Data Management team, that navigates these opposite tensions