DeepVoid: A Deep Learning Void Detector
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917292362694656 |
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| author | Kumagai, Sam Vogeley, Michael S. Aragon-Calvo, Miguel A. Douglass, Kelly A. BenZvi, Segev Neyrinck, Mark |
| author_facet | Kumagai, Sam Vogeley, Michael S. Aragon-Calvo, Miguel A. Douglass, Kelly A. BenZvi, Segev Neyrinck, Mark |
| contents | We present DeepVoid, an application of deep learning trained on a physical definition of cosmic voids to detect voids in density fields and galaxy distributions. By semantically segmenting the IllustrisTNG simulation volume using the tidal tensor, we train a deep convolutional neural network to classify local structure using a U-Net architecture for training and prediction. The model achieves a void F1 score of 0.96 and a Matthews correlation coefficient over all structural classes of 0.81 for dark matter particles in IllustrisTNG with interparticle spacing of $λ=0.33 h^{-1} \text{Mpc}$. We then apply the machine learning technique of curricular learning to enable the model to classify structure in data with significantly larger intertracer separation. At the highest tracer separation tested, $λ=10 h^{-1} \text{Mpc}$, the model achieves a void F1 score of 0.89 and a Matthews correlation coefficient of 0.6 on IllustrisTNG subhalos. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21134 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepVoid: A Deep Learning Void Detector Kumagai, Sam Vogeley, Michael S. Aragon-Calvo, Miguel A. Douglass, Kelly A. BenZvi, Segev Neyrinck, Mark Instrumentation and Methods for Astrophysics We present DeepVoid, an application of deep learning trained on a physical definition of cosmic voids to detect voids in density fields and galaxy distributions. By semantically segmenting the IllustrisTNG simulation volume using the tidal tensor, we train a deep convolutional neural network to classify local structure using a U-Net architecture for training and prediction. The model achieves a void F1 score of 0.96 and a Matthews correlation coefficient over all structural classes of 0.81 for dark matter particles in IllustrisTNG with interparticle spacing of $λ=0.33 h^{-1} \text{Mpc}$. We then apply the machine learning technique of curricular learning to enable the model to classify structure in data with significantly larger intertracer separation. At the highest tracer separation tested, $λ=10 h^{-1} \text{Mpc}$, the model achieves a void F1 score of 0.89 and a Matthews correlation coefficient of 0.6 on IllustrisTNG subhalos. |
| title | DeepVoid: A Deep Learning Void Detector |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2504.21134 |