The NEO Surveyor Near Earth Asteroid Known Object Model

The known near-Earth object (NEO) population consists of over 32,000 objects, with a yearly discovery rate of over 3000 NEOs per year. An essential component of the next generation of NEO surveys is an understanding of the population of known objects, including an accounting of the discovery rate per year as a function of size. Using a near-Earth asteroid (NEA) reference model developed for NASA's NEO Surveyor (NEOS) mission and a model of the major current and historical ground-based surveys, an estimate of the current NEA survey completeness as a function of size and absolute magnitude has been determined (termed the Known Object Model; KOM). This allows for understanding of the intersection of the known catalog of NEAs and the objects expected to be observed by NEOS. The current NEA population is found to be $\sim38\%$ complete for objects larger than 140m, consistent with estimates by Harris & Chodas (2021). NEOS is expected to catalog more than two thirds of the NEAs larger than 140m, resulting in $\sim76\%$ of NEAs cataloged at the end of its 5 year nominal survey (Mainzer et al, 2023}, making significant progress towards the US Congressional mandate. The KOM estimates that $\sim77\%$ of the currently cataloged objects will be detected by NEOS, with those not detected contributing $\sim9\%$ to the final completeness at the end its 5 year mission. This model allows for placing the NEO Surveyor mission in the context of current surveys to more completely assess the progress toward the goal of cataloging the population of hazardous asteroids.Comment: 27 pages, 18 figures, 3 tables. Accepted for publication in Planetary Science Journal (PSJ

The Pan-STARRS Moving Object Processing System

We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves > 99.5% efficiency in producing orbits from a synthetic but realistic population of asteroids whose measurements were simulated for a Pan-STARRS4-class telescope. Additionally, using a non-physical grid population, we demonstrate that MOPS can detect populations of currently unknown objects such as interstellar asteroids. MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope despite differences in expected false detection rates, fill-factor loss and relatively sparse observing cadence compared to a hypothetical Pan-STARRS4 telescope and survey. MOPS remains >99.5% efficient at detecting objects on a single night but drops to 80% efficiency at producing orbits for objects detected on multiple nights. This loss is primarily due to configurable MOPS processing limits that are not yet tuned for the Pan-STARRS1 mission. The core MOPS software package is the product of more than 15 person-years of software development and incorporates countless additional years of effort in third-party software to perform lower-level functions such as spatial searching or orbit determination. We describe the high-level design of MOPS and essential subcomponents, the suitability of MOPS for other survey programs, and suggest a road map for future MOPS development.Comment: 57 Pages, 26 Figures, 13 Table

Emerging Capabilities for Detection and Characterization of Near-Earth Objects (NEOs)

Here we describe the status for the detection and characterization of Near- Earth Objects (NEO) with current and future observatories. A summary of the capabilities, limitations, and obtainable NEO parameters is provided. <p/

CfA3: 185 Type Ia Supernova Light Curves from the CfA

We present multi-band photometry of 185 type-Ia supernovae (SN Ia), with over 11500 observations. These were acquired between 2001 and 2008 at the F. L. Whipple Observatory of the Harvard-Smithsonian Center for Astrophysics (CfA). This sample contains the largest number of homogeneously-observed and reduced nearby SN Ia (z < 0.08) published to date. It more than doubles the nearby sample, bringing SN Ia cosmology to the point where systematic uncertainties dominate. Our natural system photometry has a precision of 0.02 mag or better in BVRIr'i' and roughly 0.04 mag in U for points brighter than 17.5 mag. We also estimate a systematic uncertainty of 0.03 mag in our SN Ia standard system BVRIr'i' photometry and 0.07 mag for U. Comparisons of our standard system photometry with published SN Ia light curves and comparison stars, where available for the same SN, reveal agreement at the level of a few hundredths mag in most cases. We find that 1991bg-like SN Ia are sufficiently distinct from other SN Ia in their color and light-curve-shape/luminosity relation that they should be treated separately in light-curve/distance fitter training samples. The CfA3 sample will contribute to the development of better light-curve/distance fitters, particularly in the few dozen cases where near-infrared photometry has been obtained and, together, can help disentangle host-galaxy reddening from intrinsic supernova color, reducing the systematic uncertainty in SN Ia distances due to dust.Comment: Accepted to the Astrophysical Journal. Minor changes from last version. Light curves, comparison star photometry, and passband tables are available at http://www.cfa.harvard.edu/supernova/CfA3