A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues
Monthly Notices of the Royal Astronomical Society, ISSN: 1365-2966, Vol: 482, Issue: 3, Page: 2861-2871
2019
- 34Citations
- 44Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
For modern large-scale structure survey techniques it has become standard practice to test data analysis pipelines on large suites of mock simulations, a task which is currently prohibitively expensive for full N-body simulations. Instead of calculating this costly gravitational evolution, we have trained a three-dimensional deep Convolutional Neural Network (CNN) to identify dark matter protohaloes directly from the cosmological initial conditions. Training on halo catalogues from the Peak Patch semi-analytic code, we test various CNN architectures and find they generically achieve a Dice coefficient of ∼92 per cent in only 24 h of training. We present a simple and fast geometric halo finding algorithm to extract haloes from this powerful pixel-wise binary classifier and find that the predicted catalogues match the mass function and power spectra of the ground truth simulations to within ∼10 per cent.We investigate the effect of long-range tidal forces on an object-by-object basis and find that the network's predictions are consistent with the non-linear ellipsoidal collapse equations used explicitly by the Peak Patch algorithm.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85067059489&origin=inward; http://dx.doi.org/10.1093/mnras/sty2949; https://academic.oup.com/mnras/article/482/3/2861/5154935; http://academic.oup.com/mnras/article-pdf/482/3/2861/26577013/sty2949.pdf; https://dx.doi.org/10.1093/mnras/sty2949
Oxford University Press (OUP)
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