2022
DOI: 10.1162/artl_a_00351
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Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks

Abstract: We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To an… Show more

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Cited by 12 publications
(13 citation statements)
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“…field estimate of the large-scale structure spanning 37.6k SDSS galaxies within the 0.018 < z < 0.038 range. The detailed description of methodology and analyses are described in Elek et al (2022). We provide a brief summary of the model here.…”
Section: The Mcpm Algorithmmentioning
confidence: 99%
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“…field estimate of the large-scale structure spanning 37.6k SDSS galaxies within the 0.018 < z < 0.038 range. The detailed description of methodology and analyses are described in Elek et al (2022). We provide a brief summary of the model here.…”
Section: The Mcpm Algorithmmentioning
confidence: 99%
“…This section describes how we calibrate the MCPM algorithm using the Bolshoi-Planck data. We refer readers to Elek et al (2022) for more details of the fitting procedure and the impact of the model hyperparameters on the resulting reconstruction geometry. Readers interested in the catalog data can skip to Section 3.3.…”
Section: Mcpm Fit To Bolshoi-planckmentioning
confidence: 99%
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“…There are several other cosmic web reconstruction techniques that have been employed for cosmic web studies, which have advantages and disadvantages over the DISPERSE framework (see Libeskind et al 2018 for a detailed comparison of many of these methods). We are currently applying a new state-of-theart cosmic web reconstruction algorithm called the Monte Carlo Physarum Machine (MCPM), inspired by the Physarum polycephalum (slime mold) organism (Elek et al 2021(Elek et al , 2022, to compare to the local density estimation and global cosmic web characterization from DISPERSE. This method produces continuous cosmic matter densities (as opposed to discrete DTFE densities at the locations of galaxies) and has been applied successfully to both theoretical and observational data sets (e.g., Burchett et al 2020;Simha et al 2020;Wilde et al 2023).…”
Section: Other Caveatsmentioning
confidence: 99%