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ABSTRACT With high-quality data from programs like the Hubble Frontier Fields, cluster lensing has reached the point that models are dominated by systematic rather than statistical uncertainties. We introduce a Bayesian framework to quantify systematic effects by determining how different lens modelling choices affect the results. Our framework includes a new two-sample test for quantifying the difference between posterior probability distributions that are sampled by methods like Monte Carlo Markov chains. We use the framework to examine choices related to the selection and treatment of cluster member galaxies in two of the Frontier Field clusters: Abell 2744 and MACS J0416.1–2403. When selecting member galaxies, choices about depth and area affect the models; we find that model results are robust for an I-band magnitude limit of mlim ≥ 22.5 mag and a radial cut of rlim ≥ 90 arcsec (from the centre of the field), although the radial limit likely depends on the spatial extent of lensed images. Mass is typically assigned to galaxies using luminosity/mass scaling relations. We find that the slopes of the scaling relations can have significant effects on lens model parameters but only modest effects on lensing magnifications. Interestingly, scatter in the scaling relations affects the two fields differently. This analysis illustrates how our framework can be used to analyse lens modelling choices and guide future cluster lensing programs.
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This content will become publicly available on December 31, 2025
From situated knowledges to situated modelling: a relational framework for simulation modelling
In this paper we extend the use of a relational approach to simulation modelling, a widely used knowledge practice in sustainability science. Among modellers, there is awareness that model results can only be interpreted in view of the assumptions that inform model construction and analysis, but less systematic questioning of those assumptions. Moreover, current methodological discussions tend to focus on integrating social and ecological dynamics or diverse knowledges and data within a model. Yet choices regarding types of modelling, model structure, data handling, interpretation of results and model validation are not purely epistemic. They are entangled with values, contexts of production and use, power relations, and pragmatic considerations. Situated Modelling extends a relational understanding of the world to scientific knowledge production and with that to modelling itself in order to enable a systematic interrogation of these choices and to research social-ecological transformations relationally. To make tangible the situatedness of simulation modelling, we build on existing practices and describe the situatedness of three distinct modelling approaches. We then suggest four guiding principles for Situated Modelling: 1. attending to the apparatus of knowledge production that is socially and materially embedded and produced by e.g. research infrastructures, power relations, and ways of thinking; 2. considering how agency is distributed between model, world, data, modeller in model construction; 3. creating heterogenous collectives which together occupy the formerly individualised subject position; and 4. using agonism as an epistemic virtue to retain and work with significant differentiations of social-ecological dynamics throughout the modelling process.
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- Award ID(s):
- 2206739
- PAR ID:
- 10563438
- Publisher / Repository:
- Taylor & Francis Online
- Date Published:
- Journal Name:
- Ecosystems and People
- Volume:
- 20
- Issue:
- 1
- ISSN:
- 2639-5908
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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