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The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation. However, exactly how the LSST observations will be taken (the observing strategy or "cadence") is not yet finalized. In this dynamically-evolving community white paper, we explore how the detailed performance of the anticipated science investigations is expected to depend on small changes to the LSST observing strategy. Using realistic simulations of the LSST schedule and observation properties, we design and compute diagnostic metrics and Figures of Merit that provide quantitative evaluations of different observing strategies, analyzing their impact on a wide range of proposed science projects. This is work in progress: we are using this white paper to communicate to each other the relative merits of the observing strategy choices that could be made, in an effort to maximize the scientific value of the survey. The investigation of some science cases leads to suggestions for new strategies that could be simulated and potentially adopted. Notably, we find motivation for exploring departures from a spatially uniform annual tiling of the sky: focusing instead on different parts of the survey area in different years in a "rolling cadence" is likely to have significant benefits for a number of time domain and moving object astronomy projects. The communal assembly of a suite of quantified and homogeneously coded metrics is the vital first step towards an automated, systematic, science-based assessment of any given cadence simulation, that will enable the scheduling of the LSST to be as well-informed as possible., Comment: 312 pages, 90 figures. Browse the current version at https://github.com/LSSTScienceCollaborations/ObservingStrategy, new contributions welcome!

The Large Synoptic Survey Telescope (LSST) will enable revolutionary studies of galaxies, dark matter, and black holes over cosmic time. The LSST Galaxies Science Collaboration has identified a host of preparatory research tasks required to leverage fully the LSST dataset for extragalactic science beyond the study of dark energy. This Galaxies Science Roadmap provides a brief introduction to critical extragalactic science to be conducted ahead of LSST operations, and a detailed list of preparatory science tasks including the motivation, activities, and deliverables associated with each. The Galaxies Science Roadmap will serve as a guiding document for researchers interested in conducting extragalactic science in anticipation of the forthcoming LSST era., Comment: For more information, see https://galaxies.science.lsst.org

AGN exhibit rapid, high amplitude stochastic flux variations across the entire electromagnetic spectrum on timescales ranging from hours to years. The cause of this variability is poorly understood. We present a Green's Function-based method for using variability to (1) measure the time-scales on which flux perturbations evolve and (2) characterize the driving flux perturbations. We model the observed light curve of an AGN as a linear differential equation driven by stochastic impulses. We analyze the light curve of the Kepler AGN Zw 229-15 and find that the observed variability behavior can be modeled as a damped harmonic oscillator perturbed by a colored noise process. The model powerspectrum turns over on time-scale $385$~d. On shorter time-scales, the log-powerspectrum slope varies between $2$ and $4$, explaining the behavior noted by previous studies. We recover and identify both the $5.6$~d and $67$~d timescales reported by previous work using the Green's Function of the C-ARMA equation rather than by directly fitting the powerspectrum of the light curve. These are the timescales on which flux perturbations grow, and on which flux perturbations decay back to the steady-state flux level respectively. We make the software package KALI used to study light curves using our method available to the community., Comment: 24 pages, 7 figures, submitted to MNRAS

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 Pach\'on 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., Comment: 10 pages, 3 figures, to appear in the Proceedings of ADASS XXV

We gauge the impact of spacecraft-induced effects on the inferred variability properties of the light curve of the Seyfert 1 AGN Zw 229-15 observed by \Kepler. We compare the light curve of Zw 229-15 obtained from the Kepler MAST database with a re-processed light curve constructed from raw pixel data (Williams & Carini, 2015). We use the first-order structure function, $SF(\delta t)$, to fit both light curves to the damped power-law PSD of Kasliwal, Vogeley & Richards, 2015. On short timescales, we find a steeper log-PSD slope ($\gamma = 2.90$ to within $10$ percent) for the re-processed light curve as compared to the light curve found on MAST ($\gamma = 2.65$ to within $10$ percent)---both inconsistent with a damped random walk which requires $\gamma = 2$. The log-PSD slope inferred for the re-processed light curve is consistent with previous results (Carini & Ryle, 2012, Williams & Carini, 2015) that study the same re-processed light curve. The turnover timescale is almost identical for both light curves ($27.1$ and $27.5$~d for the reprocessed and MAST database light curves). Based on the obvious visual difference between the two versions of the light curve and on the PSD model fits, we conclude that there remain significant levels of spacecraft-induced effects in the standard pipeline reduction of the Kepler data. Re-processing the light curves will change the model inferenced from the data but is unlikely to change the overall scientific conclusion reached by Kasliwal et al. 2015---not all AGN light curves are consistent with the DRW., Comment: 7 pages, 3 figures

The Thirty Meter Telescope (TMT) International Observatory (TIO) will be a revolutionary leap forward in astronomical observing capabilities, enabling us to address some of the most profound questions about the universe. From unraveling the mysteries of dark matter and dark energy to exploring the origins of stars and planets, TMT will transform our understanding of the cosmos. The TIO Detailed Science Case (DSC) presents science goals that inform the top-level requirements for the observatory's design and operations, including the telescope, enclosure, instruments, and adaptive optics system., Comment: 275 pages. 2024 version. Updated from 2015 and 2022

We test the consistency of active galactic nuclei (AGN) optical flux variability with the $\textit{damped random walk}$ (DRW) model. Our sample consists of 20 multi-quarter $\textit{Kepler}$ AGN light curves including both Type 1 and 2 Seyferts, radio-loud and -quiet AGN, quasars, and blazars. $\textit{Kepler}$ observations of AGN light curves offer a unique insight into the variability properties of AGN light curves because of the very rapid ($11.6-28.6$ min) and highly uniform rest-frame sampling combined with a photometric precision of $1$ part in $10^{5}$ over a period of 3.5 yr. We categorize the light curves of all 20 objects based on visual similarities and find that the light curves fall into 5 broad categories. We measure the first order structure function of these light curves and model the observed light curve with a general broken power-law PSD characterized by a short-timescale power-law index $\gamma$ and turnover timescale $\tau$. We find that less than half the objects are consistent with a DRW and observe variability on short timescales ($\sim 2$ h). The turnover timescale $\tau$ ranges from $\sim 10-135$ d. Interesting structure function features include pronounced dips on rest-frame timescales ranging from $10-100$ d and varying slopes on different timescales. The range of observed short-timescale PSD slopes and the presence of dip and varying slope features suggests that the DRW model may not be appropriate for all AGN. We conclude that AGN variability is a complex phenomenon that requires a more sophisticated statistical treatment., Comment: 20 pages, 13 figures