[论文解读] Projected Near-Earth Object Discovery Performance Of The Large Synoptic Survey Telescope

Steven R. Chesley, Peter Vereš|arXiv (Cornell University)|Apr 25, 2017

Gamma-ray bursts and supernovae参考文献 4被引用 7

一句话总结

本研究预测，在10年基准调查期内，大型巡天望远镜（LSST）将探测到约60%的近地小行星（NEOs），其绝对星等 H < 22（即140米及以上大小的天体），在最坏情况下的关联效率下，完整性将降至55%。考虑到以往和未来调查的贡献，LSST预计在基准调查结束时，对绝对星等 H < 22 的潜在危害小行星（PHAs）的完整性将达到80±5%，尽管存在虚假探测和每晚仅两次观测的观测周期限制等挑战。

ABSTRACT

Executive Summary This report describes the methodology and results of an assessment study of the performance of the Large Synoptic Survey Telescope (LSST) in its planned efforts to detect and catalog near-Earth objects (NEOs). LSST is a major, joint effort of the US National Science Foundation and the Department of Energy, with significant support from private donors. The project has a number of key science goals, and among them is the objective of cataloging the solar system, including NEOs. LSST is designed for rapid, wide-field, faint surveying of the night sky, and thus has an 8.4m primary mirror, with 3.2 Gigapixels covering a 9.5 deg² field of view. The system is projected to reach a faint limit of V ≃ 25 in a 30-second exposure visit to a given field and perform nearly 2.5 million visits in its 10-year survey. The baseline LSST survey approach is designed to make two visits to a given field in a given night, leading to two possible NEO detections per night. These nightly pairs must be linked across nights to derive orbits of moving objects. However, the presence of false detections in the data stream leads to the possibility of high rates of false tracklets, and the ensuing risk that the resulting orbit catalog may be contaminated by false orbits. NEO surveys to date have successfully eliminated this risk by making 3–5 visits per night to obtain confirming detections so that the single-night string of detections has a high reliability. The traditional approach is robust, at the expense of reduced sky coverage and a diminished discovery rate. The baseline LSST approach, in contrast, is potentially fragile to large numbers of false detections, but maximizes the survey performance. One of our key objectives was to investigate this fragility by conducting high-fidelity linkage tests on a full-density simulated LSST detection stream. We also sought to quantify the overall performance of LSST as an NEO discovery system, under the hypothesis that the NEO detections arising from the baseline LSST survey observing cadence can be successfully linked. We used the latest instantiation of the LSST baseline survey and the most current NEO population model to derive the fraction of NEOs detected and cataloged by LSST from among the source population. As a part of this we developed a high-fidelity detection model that accurately represented the LSST focal plane and implemented a smooth degradation in detection efficiency near the limiting magnitude, rather than the usual step function. The study carefully modeled losses from trailed detections associated with fast moving objects, and we investigated other minor effects, such as telescope vignetting, and asteroid colors and light curves. For the linking tests, we included all major sources of detections for a single selected observing cycle (full moon to full moon), leading to 66 million detections, of which 77% were false detections, 23% were main-belt asteroids, and only 0.14% were NEOs. Using only a single 8-core workstation we were able to successfully link 94% of potential NEO discoveries in the detection stream. We are confident that with appropriately-sized computation resources, some algorithmic improvements and careful tuning of the linking algorithms the linking efficiency can be significantly improved. In this simulation, 96% of objects in the NEO catalog were correctly linked. Of the 4% that involved erroneous linkages, almost all comprised detections of two distinct main-belt asteroids. This situation, known as “main-belt confusion” is fundamental to the NEO search problem and is readily resolved as the main-belt asteroid catalog becomes filled in over time. Despite the 77% rate of false detections, less than 0.1% of derived NEOs in this simulation included false detections. From these results, within the study hypotheses, we conclude that the two visits-per-night observation cadence can be successful in cataloging NEOs. This conclusion does assume a certain rate of false positives, but is unlikely to be sensitive to increases by factors of a few in the false detection rate, given the significant computational resources allocated by LSST for the problem. Our simulations revealed that in 10 years LSST would catalog ∼ 60% of NEOs with absolute magnitude H &lt; 22, which is a proxy for 140m and larger objects. This results neglects linking losses and the contribution of any other NEO surveys. Including our worst-case linking efficiency we reach a overall performance assessment of 55% completeness of NEOs with H &lt; 22. We estimate that survey mis-modeling could account for systematic errors of up to 5%. We find that restricting the evaluation metric to so-called Potentially Hazardous Asteroids (PHAs) increases the completeness by 3–4%, and that including the benefits of past and expected future NEO survey activity increases completeness at the end of the baseline LSST survey by 15-20%. Assembling these results leads to a projection that by the end of the baseline LSST survey the NEO catalog will be 80±5% complete for PHAs with H &lt; 22. As described in detail in the report, these results are largely consistent with other results obtained independently; indeed the small 1–2% variation among independent estimates of NEO completeness is remarkable and reassuring. The results above require pairs of observations in three distinct nights over no more than 12 days. A maximum linking interval of 20 days, for which high linking efficiency has not been demonstrated, leads to a 2–3% improvement in completeness. We also tested a special-purpose LSST cadence designed to enhance the NEO discovery rate, but our results show little improvement over the baseline for a ten-year survey. Surveying longer does provide an increase in completeness, by roughly 2% per year for a lone LSST and 1% per year when including contributions from other surveys. Thus, in our judgement, the H &lt; 22 PHA catalog can be expected to approach ∼ 85% completeness, but not 90%, after 12 years of LSST operation.

研究动机与目标

• 评估LSST在其基准每晚两次观测的调查周期下，探测和编目近地小行星（NEOs）的性能。
• 量化虚假探测和轨道段关联效率低下对NEO目录完整性的影响。
• 估算包含过去和未来调查贡献在内的、绝对星等 H < 22 的NEO目录整体完整性。
• 评估由调查建模不准确引起的系统性不确定性的来源。
• 确定相对于更广泛的NEO群体，潜在危害小行星（PHAs）的完整性水平。

提出的方法

• 在全密度模拟的LSST探测流上执行高保真度的轨道段关联测试，以在真实条件下评估轨道段关联性能。
• 仅以每晚两次观测的LSST调查周期进行建模，这限制了探测机会并增加了虚假轨道段的风险。
• 使用真实噪声和背景水平模拟移动天体的探测与关联，以估算关联效率。
• 使用蒙特卡洛模拟，预测10年基准LSST调查周期内的NEO发现率。
• 应用统计校正以考虑关联损失和调查建模误差，估算系统性不确定性最高达±5%。
• 整合现有及预期未来NEO调查的贡献，以优化整体完整性估算。

实验结果

研究问题

• RQ1 在假设轨道段关联成功的情况下，LSST在10年基准调查期内将探测到多少比例的绝对星等 H < 22 的近地小行星？
• RQ2 虚假探测和关联效率低下如何影响LSST每晚两次观测周期下的NEO目录完整性？
• RQ3 对绝对星等 H < 22 的潜在危害小行星（PHAs）的完整性预测是多少？与更广泛的NEO群体相比如何？
• RQ4 过去和未来NEO调查的贡献在多大程度上提升了LSST整体NEO目录的完整性？
• RQ5 调查建模不准确可能引发哪些系统性误差？其规模可能有多大？

主要发现

• 在假设轨道段关联成功的情况下，LSST预计将在10年基准调查期内编目约60%的绝对星等 H < 22 的近地小行星。
• 在最坏情况的关联效率下，H < 22 的NEO完整性下降至55%，表明存在虚假轨道污染的显著风险。
• 纳入过去及预期未来NEO调查活动后，LSST调查结束时的整体完整性提高了15–20%。
• 对绝对星等 H < 22 的潜在危害小行星（PHAs）的完整性估计比一般 H < 22 的NEO群体高出3–4%。
• 由于调查建模不准确引起的系统性误差，可能导致完整性估算的不确定性高达5%。
• 在基准LSST调查结束时，NEO目录对绝对星等 H < 22 的PHAs的完整性预计为80±5%，该结果综合了LSST性能与外部调查贡献。