Searcharxiv⌕ Search

arXiv subjects

Krzysztof Suberlak

Publications and source records attributed to Krzysztof Suberlak.

7 recordsLinked to original sources

Laser-based metrology systems vs wavefront sensing techniques: a comparative overview between the Large Binocular Telescope and the Vera C. Rubin Observatory for the telescope alignment and collimation tracking

This work presents a comparative overview of the collimation and alignment strategies employed by two leading 8m-class facilities: the Large Binocular Telescope (LBT) and the Vera C. Rubin Observatory. While both telescopes share a challenging fast f-number of approximately f/1.2 (considering the LBT in its Prime Focus configuration), they have adopted reciprocal architectures for the initial optical alignment strategy and for maintaining collimation during the night. As an initial alignment strategy, the LBT relies on a Wavefront Sensing technique called Focal Plane Image Analysis. Conversely, the Vera C. Rubin Observatory baseline foresees the usage of a Laser Tracker system to establish the initial optical states. The strategies for preserving the optical alignment and maintaining the collimation against gravitational flexure and thermal drift during observations are instead reversed. Besides the use of open-loop corrections based on Look-Up Tables, common on both telescopes, the LBT utilizes a laser-based Telescope Metrology System to monitor the relative position of optics in real-time, applying the corrections between the exposures. In contrast, the Rubin Observatory employs a Curvature Wavefront Sensing technique, using dedicated detectors at the four corners of the focal plane. Rather than identifying a best strategy, this work aims to synthesize the strengths, limitations, and operational trade-offs of these complementary approaches, from the perspective of the next generation of Extremely Large Telescopes and their instruments.

astro-ph.IM↗

Enabling Early Transient Discovery in LSST via Difference Imaging with DECam

We present SLIDE, a pipeline that enables transient discovery in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), using archival images from the Dark Energy Camera (DECam) as templates for difference imaging. We apply this pipeline to the recently released Data Preview 1 (DP1; the first public release of Rubin commissioning data) and search for transients in the resulting difference images. The image subtraction, photometry extraction, and transient detection are all performed on the Rubin Science Platform. We demonstrate that SLIDE effectively extracts clean photometry by circumventing poor or missing LSST templates. We identified 29 previously unreported transients, 12 of which would not have been detected based on the DP1 DiaObject catalog. SLIDE will be especially useful for transient analysis in the early years of LSST, when template coverage will be largely incomplete or when templates may be contaminated by transients present at the time of acquisition. We present multiband light curves for a sample of known transients, along with new transient candidates identified through our search. Finally, we discuss the prospects of applying this pipeline during the main LSST survey. Our pipeline is broadly applicable and will support studies of all transients with slowly evolving phases.

astro-ph.HE↗

Identification and photometric classification of extragalactic transients in the Vera C. Rubin Observatory's Data Preview 1

The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a LSSTComCam observing campaign between November and December 2024 with multi-band imaging of seven fields, covering roughly 0.4 square degrees each, providing a first glimpse into the data products that will become available once the Legacy Survey of Space and Time begins. In this work, we search three fields for extragalactic transients. We identify eight new likely supernovae, and three known ones from a sample of 369,644 difference image analysis objects. Photometric classification using Superphot+ assigns sub-classes with >95% confidence to only one SN Ia and one SN II in this sample. Our findings are in agreement with supernova detection rate predictions of $15\pm4$ supernovae from simulations using simsurvey. The supernova detection rate in the data is possibly affected by the lack of suitable templates. Nevertheless, this work demonstrates the quality of the data products delivered in DP1 and indicates that the Rubin Observatory's Legacy Survey of Space and Time (LSST) is well placed to fulfill its discovery potential in time domain astronomy.

astro-ph.HE↗

Using AI for Wavefront Estimation with the Rubin Observatory Active Optics System

The Vera C. Rubin Observatory will, over a period of 10 years, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an Active Optics System (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wavefront. We have designed and integrated a deep learning model for wavefront estimation into the AOS pipeline. In this paper, we compare the performance of this deep learning approach to Rubin's baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the deep learning approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images, and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the deep learning model is 40x faster, the median error 2x better under ideal conditions, 5x better in the presence of vignetting by the Rubin camera, and 14x better in the presence of blending in crowded fields. In addition, the deep learning model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.

astro-ph.IM↗

Improving Damped Random Walk parameters for SDSS Stripe 82 Quasars with Pan-STARRS1

We use the Panoramic Survey Telescope and Rapid Response System 1 Survey (Pan-STARRS1, PS1) data to extend the Sloan Digital Sky Survey (SDSS) Stripe 82 quasar light curves. Combining PS1 and SDSS light curves provides a 15 yr baseline for 9248 quasars - 5 yr longer than prior studies that used only SDSS. We fit the light curves with the damped random walk (DRW) model model - a statistical description of their variability. We correlate the resulting DRW model parameters: asymptotic variability amplitude SF$_{\infty}$, and characteristic timescale $τ$, with quasar physical properties - black hole mass, bolometric luminosity, and redshift. Using simulated light curves, we find that a longer baseline allows us to better constrain the DRW parameters. After adding PS1 data, the variability amplitude is a stronger function of the black hole mass, and has a weaker dependence on quasar luminosity. In addition, the characteristic timescale $τ$ dependence on quasar luminosity is marginally weaker. We also make predictions for the fidelity of DRW model parameter retrieval when light curves will be further extended with Zwicky Transient Facility (ZTF) and the Rubin Observatory Legacy Survey of Space and Time (LSST) data. Finally, we show how updated DRW parameters offer an independent method of discovering changing-look quasar candidates (CLQSOs). The candidates are outliers in terms of differences in magnitude and scatter between SDSS and PS1 segments. We identify 40 objects (35 newly reported) with tenfold increase in variability timescale between SDSS and SDSS--PS1 data, which is due to a large change in brightness (over 0.5 mag) - characteristic for CLQSOs.

astro-ph.GA↗

AXS: A framework for fast astronomical data processing based on Apache Spark

We introduce AXS (Astronomy eXtensions for Spark), a scalable open-source astronomical data analysis framework built on Apache Spark, a widely used industry-standard engine for big data processing. Building on capabilities present in Spark, AXS aims to enable querying and analyzing almost arbitrarily large astronomical catalogs using familiar Python/AstroPy concepts, DataFrame APIs, and SQL statements. We achieve this by i) adding support to Spark for efficient on-line positional cross-matching and ii) supplying a Python library supporting commonly-used operations for astronomical data analysis. To support scalable cross-matching, we developed a variant of the ZONES algorithm (Gray et al. 2004) capable of operating in distributed, shared-nothing architecture. We couple this to a data partitioning scheme that enables fast catalog cross-matching and handles the data skew often present in deep all-sky data sets. The cross-match and other often-used functionalities are exposed to the end users through an easy-to-use Python API. We demonstrate AXS' technical and scientific performance on SDSS, ZTF, Gaia DR2, and AllWise catalogs. Using AXS we were able to perform on-the-fly cross-match of Gaia DR2 (1.8 billion rows) and AllWise (900 million rows) data sets in ~ 30 seconds. We discuss how cloud-ready distributed systems like AXS provide a natural way to enable comprehensive end-user analyses of large datasets such as LSST.

astro-ph.IM↗

Solving the puzzle of discrepant quasar variability on monthly time-scales implied by SDSS and CRTS data sets

We present an improved photometric error analysis for the 7,100 CRTS (Catalina Real-Time Transient Survey) optical light curves for quasars from the SDSS (Sloan Digital Sky Survey) Stripe 82 catalogue. The SDSS imaging survey has provided a time-resolved photometric data set which greatly improved our understanding of the quasar optical continuum variability: Data for monthly and longer time-scales are consistent with a damped random walk (DRW). Recently, newer data obtained by CRTS provided puzzling evidence for enhanced variability, compared to SDSS results, on monthly time-scales. Quantitatively, SDSS results predict about 0.06 mag root-mean-square (rms) variability for monthly time-scales, while CRTS data show about a factor of 2 larger rms, for spectroscopically confirmed SDSS quasars. Our analysis has successfully resolved this discrepancy as due to slightly underestimated photometric uncertainties from the CRTS image processing pipelines. As a result, the correction for observational noise is too small and the implied quasar variability is too large. The CRTS photometric error correction factors, derived from detailed analysis of non-variable SDSS standard stars that were re-observed by CRTS, are about 20-30%, and result in reconciling quasar variability behaviour implied by the CRTS data with earlier SDSS results. An additional analysis based on independent light curve data for the same objects obtained by the Palomar Transient Factory provides further support for this conclusion. In summary, the quasar variability constraints on weekly and monthly time-scales from SDSS, CRTS and PTF surveys are mutually compatible, as well as consistent with DRW model.

astro-ph.GA↗