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JOSS_submission/paper.md

@@ -122,7 +122,7 @@ aas-journal: Astronomical Journal
 
 The upcoming Legacy Survey of Space and Time (LSST)  at the Vera C. Rubin Observatory [@lsstsciencebook2009; @ivezic2019; @bianco2022] is expected to revolutionize solar system astronomy. Unprecedented in scale, this ten-year wide-field survey will take ~2 million exposures split between 6 filters while also discovering and monitoring millions more solar system objects than are currently known [@jones2009; @jones2018; @lsstsciencebook2009; @solontoi2010; @shannon2015; @grav2016; @silsbee2016; @veres2017; @schwamb2018; @ivezic2019; @fedorets2020; @hoover2022; @kurlander2025; @murtagh2025]. This wealth of new information surpasses any survey to date in its combination of depth, sky coverage and sheer number of observations, The LSST will enable planetary astronomers to probe the dynamics and formation history of the solar system on a scale never before attempted. However, all astronomical surveys are affected by a complex set of intertwined observational biases, including observational strategy and cadence, limiting magnitude, instrumentation effects and observing conditions. The small body discoveries from an astronomical survey therefore provide a biased and distorted view of the actual underlying population. To help address this, survey simulators have emerged as powerful tools for assessing the impact of observational biases and aiding in the study of the target population. Survey simulators have long been used in smaller population-specific surveys such as the Canada–France Ecliptic Plane Survey (CFEPS) [@jones2006; @kavelaars2009; @petit2011] and the Outer Solar System Origins Survey (OSSOS) [@bannister2016; @bannister2018; @lawler2018] to forward-model the effects of biases on a given population, allowing for a direct comparison to real discoveries. However, the scale and tremendous scope of the LSST requires the development of a new tool capable of handling the scale of the Rubin Observatory’s LSST and all solar system small body populations.
 
-Probing the orbital/size/brightness distributions and surface composition in each of the solar system's small body reservoirs is the top science priority in the Rubin Observatory LSST Solar System Science Collaboration (SSSC) Science Roadmap [@Schwamb2018]. In order to perform these detailed population studies, one must account for all the survey biases (the complex and often intertwined detection biases – brightness limits, pointing, cadence, on-sky motion limits, software detection efficiencies) in the discovery survey (see @lawler2018 for a more detailed discussion). The SSSC’s Software Roadmap has identified a solar system survey simulator as one of the key software tools that must be developed in order to achieve the collaboration’s top science goals [@Schwamb2019]. A survey simulator takes an input model small body population and outputs (biases the population to) what LSST should have detected by utilizing the LSST pointing history, observation metadata, and Rubin Observatory Solar System Processing (SSP) pipeline’s detection efficiency so one can compare those simulated LSST detections to what was actually found by Rubin Observatory.
+Probing the orbital/size/brightness distributions and surface composition in each of the solar system's small body reservoirs is the top science priority in the Rubin Observatory LSST Solar System Science Collaboration (SSSC) Science Roadmap [@schwamb2018]. In order to perform these detailed population studies, one must account for all the survey biases (the complex and often intertwined detection biases – brightness limits, pointing, cadence, on-sky motion limits, software detection efficiencies) in the discovery survey (see @lawler2018 for a more detailed discussion). The SSSC’s Software Roadmap has identified a solar system survey simulator as one of the key software tools that must be developed in order to achieve the collaboration’s top science goals [@Schwamb2019]. A survey simulator takes an input model small body population and outputs (biases the population to) what LSST should have detected by utilizing the LSST pointing history, observation metadata, and Rubin Observatory Solar System Processing (SSP) pipeline’s detection efficiency so one can compare those simulated LSST detections to what was actually found by Rubin Observatory.
 
 # Summary