Monitoring LSST system performance during construction

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 Large Synoptic Survey Telescope (LSST) program is jointly funded by the NSF, the DOE, and private institutions and donors. From an NSF funding standpoint, the LSST is a Major Research Equipment and Facilities (MREFC) project. The NSF funding process requires proposals and D&D reviews to include activity-based budgets and schedules; documented basis of estimates; risk-based contingency analysis; cost escalation and categorization. "Out-of-the box," the commercial tool Primavera P6 contains approximately 90% of the planning and estimating capability needed to satisfy R&D phase requirements, and it is customizable/configurable for remainder with relatively little effort. We describe the customization/configuration and use of Primavera for the LSST Project Management Control System (PMCS), assess our experience to date, and describe future directions. Examples in this paper are drawn from the LSST Data Management System (DMS), which is one of three main subsystems of the LSST and is funded by the NSF. By astronomy standards the LSST DMS is a large data management project, processing and archiving over 70 petabyes of image data, producing over 20 petabytes of catalogs annually, and generating 2 million transient alerts per night. Over the 6-year construction and commissioning phase, the DM project is estimated to require 600,000 hours of engineering effort. In total, the DMS cost is approximately 60% hardware/system software and 40% labor.

The upcoming Large Synoptic Survey Telescope (LSST) will detect many strongly lensed Type Ia supernovae (LSNe Ia) for time-delay cosmography. This will provide an independent and direct way for measuring the Hubble constant H0, which is necessary to address the current 4.4σ tension in H0 between the local distance ladder and the early Universe measurements. We present a detailed analysis of different observing strategies (also referred to as cadence strategy) for the LSST, and quantify their impact on time-delay measurement between multiple images of LSNe Ia. For this, we simulated observations by using mock LSNe Ia for which we produced mock-LSST light curves that account for microlensing. Furthermore, we used the free-knot splines estimator from the software PyCS to measure the time delay from the simulated observations. We find that using only LSST data for time-delay cosmography is not ideal. Instead, we advocate using LSST as a discovery machine for LSNe Ia, enabling time delay measurements from follow-up observations from other instruments in order to increase the number of systems by a factor of 2–16 depending on the observing strategy. Furthermore, we find that LSST observing strategies, which provide a good sampling frequency (the mean inter-night gap is around two days) and high cumulative season length (ten seasons with a season length of around 170 days per season), are favored. Rolling cadences subdivide the survey and focus on different parts in different years; these observing strategies trade the number of seasons for better sampling frequency. In our investigation, this leads to half the number of systems in comparison to the best observing strategy. Therefore rolling cadences are disfavored because the gain from the increased sampling frequency cannot compensate for the shortened cumulative season length. We anticipate that the sample of lensed SNe Ia from our preferred LSST cadence strategies with rapid follow-up observations would yield an independent percent-level constraint on H0.

The Large Synoptic Survey Telescope (LSST) is a proposed 8.4-meter telescope that will be located in the Andes mountains in Chile. Every 17 seconds, a 6.4 GB image is transferred to Illinois for immediate processing. That transfer needs to complete within approximately five seconds. LSST is provisioning an international WAN with a 10Gbps bandwidth guarantee for this and other project-related data transfers. The stringent latency requirement drives a re-examination of TCP congestion control for this use case. Specifically, prior work on dedicated Long Fat Networks (LFNs) does not go far enough in fully leveraging the opportunity provided by the bandwidth guarantee. This paper presents an approach for how optimal network throughput can be obtained for the LSST use case, and the conditions under which any project can achieve data throughput rates on long-distance networks approaching wire speed.

In the coming decade we will build on the foundations of current large scale imaging surveys such as the SDSS, 2MASS and MACHO to develop deep, wide-field imaging surveys covering over 15,000 square degrees that are designed to probe to the time domain. One such project is the Large Synoptic Survey Telescope (LSST). We describe here some of the data management challenges we face in moving from the current generation of surveys to an imaging program of the size of the LSST. Scaling from todays deep CCD imaging surveys and wide-field photometric surveys we show that the computational challenge of analyzing the data from a three Gigapixel camera with 10s integrations should be manageable on the time frame that the LSST is expected to be delivered. While encouraging, these hardware considerations are only a very small aspect of the software engineering and data management procedures that must be developed in order for the LSST to succeed.

The Large Synoptic Survey Telescope (LSST) will provide for unbiased sampling of variability properties of objects with $r$ mag $<$ 24. This should allow for those objects whose variations reveal their orbital periods ($P_{orb}$), such as low mass X-ray binaries (LMXBs) and related objects, to be examined in much greater detail and with uniform systematic sampling. However, the baseline LSST observing strategy has temporal sampling that is not optimised for such work in the Galaxy. Here we assess four candidate observing strategies for measurement of $P_{orb}$ in the range 10 minutes to 50 days. We simulate multi-filter quiescent LMXB lightcurves including ellipsoidal modulation and stochastic flaring, and then sample these using LSST's operations simulator (OpSim) over the (mag, $P_{orb}$) parameter space, and over five sightlines sampling a range of possible reddening values. The percentage of simulated parameter space with correctly returned periods ranges from $\sim$23 %, for the current baseline strategy, to $\sim$70 % for the two simulated specialist strategies. Convolving these results with a $P_{orb}$ distribution, a modelled Galactic spatial distribution and reddening maps, we conservatively estimate that the most recent version of the LSST baseline strategy will allow $P_{orb}$ determination for $\sim$18 % of the Milky Way's LMXB population, whereas strategies that do not reduce observations of the Galactic Plane can improve this dramatically to $\sim$32 %. This increase would allow characterisation of the full binary population by breaking degeneracies between suggested $P_{orb}$ distributions in the literature. Our results can be used in the ongoing assessment of the effectiveness of various potential cadencing strategies.

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of a SN classifier that uses SN colors to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an AUC of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99$\%$ SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias ($\left<z_\mathrm{phot}-z_\mathrm{spec}\right>$) of 0.012 with $\sigma\left( \frac{z_\mathrm{phot}-z_\mathrm{spec}}{1+z_\mathrm{spec}}\right) = 0.0294$ without using a host-galaxy photo-z prior, and a mean bias ($\left<z_\mathrm{phot}-z_\mathrm{spec}\right>$) of 0.0017 with $\sigma\left( \frac{z_\mathrm{phot}-z_\mathrm{spec}}{1+z_\mathrm{spec}}\right) = 0.0116$ using a host-galaxy photo-z prior. Assuming a flat $\Lambda CDM$ model with $\Omega_m=0.3$, we obtain $\Omega_m$ of $0.305\pm0.008$ (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter $\sigma_\mathrm{int}=0.11$) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.

The prototype charge-coupled device created at the Stanford Linear Accelerator Center for the Large Synoptic Survey Telescope must be tested to check its functionality and performance. It was installed into the Calypso telescope in Arizona in November of 2008 for this purpose. Since then it has taken many images of various astronomical objects. By doing photometry on standard stars in these images, we can compare our magnitude results to the known magnitudes of these stars. This comparison allows us to then determine the chip's performance and functional capabilities. Expecting to see first light in 2016, the Large Synoptic Survey Telescope (LSST) is an extremely large ground based telescope that anticipates funding and will be built in Chile. Described as 'Wide-Fast-Deep', the LSST will have an unprecedented wide field of view (ten square degrees for surveys), short exposures (fifteen to thirty seconds and still see faint objects), and the largest digital camera in the world. One of the goals hoped to be achieved with this camera is the measurement of dark matter using strong and weak gravitational lensing. Gravitational lensing occurs when a large cluster of galaxies distorts the light from a galaxy behind this cluster. This causes an arc of light to form around the cluster. By measuring the length of this arc, one can calculate how much matter should be present in the cluster. Since the amount that should be present is vastly greater than the amount of visible matter that can be seen, it is postulated that the difference between these two numbers is made up of dark matter. This is a direct way of measuring the amount of dark matter in the universe. Thousands of galaxy clusters will be seen with LSST, allowing precise measurements of strong lensing effects. Weak lensing is a much smaller effect, distorting the shape of galaxies by only a few percent. The scale of LSST will allow these small effects to be measured with a precision unavailable with current smaller surveys. Some of the other uses for the LSST will be cataloging the entire sky, observing exploding supernovae and near Earth objects, and probing into the nature of dark energy. Since the LSST is such a large project, one organization alone cannot build it. Therefore many organizations have come together, each one working on a specific part of the telescope's construction. Here at the Stanford Linear Accelerator Center (SLAC) the camera is being designed.

Supernovae (SNe) could be powerful probes of the properties of stars and galaxies at high redshifts in future surveys. Wide fields and longer exposure times are required to offset diminishing star formation rates and lower fluxes to detect useful number of events at high redshift. In principle, the Large Synoptic Survey Telescope (LSST) could discover large numbers of early SNe because of its wide fields but only at lower redshifts because of its AB mag limit of ∼24. However, gravitational lensing by galaxy clusters and massive galaxies could boost flux from ancient SNe and allow LSST to detect them at earlier times. Here, we calculate detection rates for lensed SNe at z ∼ 5–7 for LSST. We find that the LSST Wide Fast Deep survey could detect up to 120 lensed Population (Pop) I and II SNe but no lensed Pop III SNe. Deep-drilling programs in 10 deg2 fields could detect Pop I and II core-collapse SNe at AB magnitudes of 27–28 and 26, respectively.

Shear measurement is a crucial task in current and future weak lensing survey projects. The reconstruction of the point spread function (PSF) is one of the essential steps involved in this process. In this work, we present three different methods, Gaussianlets, Moffatlets and Expectation Maximization Principal Component Analysis (EMPCA), and quantify their efficiency on PSF reconstruction using four sets of simulated Large Synoptic Survey Telescope (LSST) star images. Gaussianlets and Moffatlets are two different sets of basis functions whose profiles are based on Gaussian and Moffat functions respectively. EMPCA is a statistical method performing an iterative procedure to find the principal components (PCs) of an ensemble of star images. Our tests show that: (1) Moffatlets always perform better than Gaussianlets. (2) EMPCA is more compact and flexible, but the noise existing in the PCs will contaminate the size and ellipticity of PSF. By contrast, Moffatlets keep the size and ellipticity very well.

Supernovae that are strongly gravitationally lensed (gLSNe) by elliptical galaxies are powerful probes of astrophysics and cosmology that will be discovered systematically by wide-field, high-cadence imaging surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Survey Telescope (LSST). Here we use pixel-level simulations that include observing strategy, target selection, supernova properties, and dust to forecast the rates and properties of gLSNe that ZTF and LSST will find. Applying the resolution-insensitive discovery strategy of Goldstein et al., we forecast that ZTF (LSST) can discover 0.02 (0.79) 91bg-like, 0.17 (5.92) 91T-like, 1.22 (47.84) Type Ia, 2.76 (88.51) Type IIP, 0.31 (12.78) Type IIL, and 0.36 (15.43) Type Ib/c gLSNe per year, with uncertainties dominated by uncertainties in the supernova rate. We also forecast that the surveys can discover at least 3.75 (209.32) Type IIn gLSNe per year, for a total of at least 8.60 (380.60) gLSNe per year under fiducial observing strategies. ZTF gLSNe have a median z s = 0.9, z l = 0.35, , Δt max = 10 days, min(θ) = 0.″25, and N img = 4. LSST gLSNe are less compact and less magnified, with a median z s = 1.0, z l = 0.4, , Δt max = 25 days, min(θ) = 0.″6, and N img = 2. We develop a model of the supernova–host galaxy connection and find that the vast majority of gLSN host galaxies will be multiply imaged, enabling detailed constraints on lens models with sufficiently deep high-resolution imaging taken after the supernova has faded. We release the results of our simulations as catalogs at http://portal.nersc.gov/project/astro250/glsne/.

The Large Synoptic Survey Telescope (LSST) has a 10 degrees square field of view which is achieved through a 3 mirror optical system comprised of an 8.4 meter primary, 3.5 meter secondary (M2) and a 5 meter tertiary mirror. The M2 is a 100mm thick meniscus convex asphere. The mirror surface is actively controlled by 72 axial electromechanical actuators (axial actuators). Transverse support is provided by 6 active tangential electromechanical actuators (tangent links). The final design has been completed by Harris Corporation. They are also providing the fabrication, integration and testing of the mirror cell assembly, as well as the figuring of the mirror. The final optical surface will be produced by ion figuring. All the actuators will experience 1 year of simulated life testing to ensure that they can withstand the rigorous demands produced by the LSST survey mission. Harris Corporation is providing optical surface metrology to demonstrate both the quality of the optical surface and the correctablility produced by the axial actuators.

Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs. We train a supervised classifier model on the HOG of mock strong galaxy–galaxy lens images similar to observations from the Hubble Space Telescope (HST) and LSST. We assess model performance with the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve. Models trained on 10,000 lens and non-lens containing images exhibit an AUC of 0.975 for an HST-like sample, 0.625 for one exposure of LSST, and 0.809 for 10 yr mock LSST observations. Performance appears to continually improve with the training set size. Models trained on fewer images perform better in the absence of the lens galaxy light. However, with larger training data sets, information from the lens galaxy actually improves model performance, indicating that HOG captures much of the morphological complexity of the arc-finding problem. We test our classifier on data from the Sloan Lens ACS Survey and find that small-scale image features reduce the efficiency of our trained model. However, these preliminary tests indicate that some parameterizations of HOG can compensate for differences between observed mock data. One example best-case parameterization results in an AUC of 0.6 in the F814 filter image, with other parameterization results equivalent to random performance.

The Large Synoptic Survey Telescope (LSST) is a large aperture, wide-field, ground-based telescope designed to provide a time domain survey of the entire southern hemisphere in six optical bands. Over the ten-year duration of the survey, LSST will obtain ~800-1,000 images of every part of the southern sky, yielding a catalog of stars, galaxies, and moving small bodies in the solar system with nearly 40 billion objects. A diverse array of scientific investigations can be performed with a common database addressing topics ranging from the detection of potentially hazardous asteroids to the structure and evolution of the Universe as a whole. LSST incorporates an 8-m class primary mirror with a 3.2 billion pixel camera. I will discuss the design of this facility and our technical progress with construction and fabrication of the key components.

Tree rings are one of the sensor effects that may affect precise measurements in the Large Synoptic Survey Telescope (LSST). The effect is caused by silicon wafer manufacturing process, resulting in formation of circular patterns due to variations of the silicon dopant concentration. We have analyzed flat-field images taken at Brookhaven National Laboratory and SLAC for all production sensors used to build the LSST camera in order to measure the amplitudes and periods of the tree ring patterns as a function of the radius, illumination wavelength, and sensor back bias voltage. With nominal back bias voltage settings, the tree ring amplitudes and periods for both ITL and e2v sensors are considered to have small impact on the galaxy shear measurement in LSST.