Supervised Extraction of the Thermal Sunyaev–Zel’dovich Effect with a Three-dimensional Convolutional Neural Network
The thermal Sunyaev–Zel’dovich (SZ) effect offers a unique probe of the hot and diffuse Universe that could help close the missing baryon problem. Traditional extractions of the SZ effect, however, exhibit systematic noise that may lead to unreliable results. In this work, we provide an alternative...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/adf069 |
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| author | Cameron T. Pratt Zhijie Qu Joel N. Bregman |
| author_facet | Cameron T. Pratt Zhijie Qu Joel N. Bregman |
| author_sort | Cameron T. Pratt |
| collection | DOAJ |
| description | The thermal Sunyaev–Zel’dovich (SZ) effect offers a unique probe of the hot and diffuse Universe that could help close the missing baryon problem. Traditional extractions of the SZ effect, however, exhibit systematic noise that may lead to unreliable results. In this work, we provide an alternative solution using a 3D attention nested U-Net trained end to end with supervised learning. Our labeled data consist of simulated SZ signals injected into Planck frequency maps, allowing our model to learn how to extract SZ signals in the presence of realistic noise. We implement a curriculum learning scheme that gradually exposed the model to weaker SZ signals. The absence/presence of curriculum learning significantly impacted the amount of bias and variance present in the reconstructed SZ signal. The results from our method were comparable to those from the popular needlet internal linear combination method when evaluated on simulated data as well as real-world SZ signals. We conclude by discussing future avenues for advancing machine learning extractions of SZ signals. |
| format | Article |
| id | doaj-art-36e4fed9a8e54b5b8ef1b9567a367d5e |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-36e4fed9a8e54b5b8ef1b9567a367d5e2025-08-21T06:01:08ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-019901210.3847/1538-4357/adf069Supervised Extraction of the Thermal Sunyaev–Zel’dovich Effect with a Three-dimensional Convolutional Neural NetworkCameron T. Pratt0https://orcid.org/0000-0002-6653-8490Zhijie Qu1https://orcid.org/0000-0002-2941-646XJoel N. Bregman2https://orcid.org/0000-0001-6276-9526Department of Astronomy, University of Michigan , Ann Arbor, MI 48109, USA ; campratt@umich.eduDepartment of Astronomy & Astrophysics, The University of Chicago , 5640 S. Ellis Ave., Chicago, IL 60637, USA; Department of Astronomy, Tsinghua University , Beijing, People's Republic of ChinaDepartment of Astronomy, University of Michigan , Ann Arbor, MI 48109, USA ; campratt@umich.eduThe thermal Sunyaev–Zel’dovich (SZ) effect offers a unique probe of the hot and diffuse Universe that could help close the missing baryon problem. Traditional extractions of the SZ effect, however, exhibit systematic noise that may lead to unreliable results. In this work, we provide an alternative solution using a 3D attention nested U-Net trained end to end with supervised learning. Our labeled data consist of simulated SZ signals injected into Planck frequency maps, allowing our model to learn how to extract SZ signals in the presence of realistic noise. We implement a curriculum learning scheme that gradually exposed the model to weaker SZ signals. The absence/presence of curriculum learning significantly impacted the amount of bias and variance present in the reconstructed SZ signal. The results from our method were comparable to those from the popular needlet internal linear combination method when evaluated on simulated data as well as real-world SZ signals. We conclude by discussing future avenues for advancing machine learning extractions of SZ signals.https://doi.org/10.3847/1538-4357/adf069Sunyaev-Zeldovich effectCosmic microwave background radiationAstronomical simulations |
| spellingShingle | Cameron T. Pratt Zhijie Qu Joel N. Bregman Supervised Extraction of the Thermal Sunyaev–Zel’dovich Effect with a Three-dimensional Convolutional Neural Network The Astrophysical Journal Sunyaev-Zeldovich effect Cosmic microwave background radiation Astronomical simulations |
| title | Supervised Extraction of the Thermal Sunyaev–Zel’dovich Effect with a Three-dimensional Convolutional Neural Network |
| title_full | Supervised Extraction of the Thermal Sunyaev–Zel’dovich Effect with a Three-dimensional Convolutional Neural Network |
| title_fullStr | Supervised Extraction of the Thermal Sunyaev–Zel’dovich Effect with a Three-dimensional Convolutional Neural Network |
| title_full_unstemmed | Supervised Extraction of the Thermal Sunyaev–Zel’dovich Effect with a Three-dimensional Convolutional Neural Network |
| title_short | Supervised Extraction of the Thermal Sunyaev–Zel’dovich Effect with a Three-dimensional Convolutional Neural Network |
| title_sort | supervised extraction of the thermal sunyaev zel dovich effect with a three dimensional convolutional neural network |
| topic | Sunyaev-Zeldovich effect Cosmic microwave background radiation Astronomical simulations |
| url | https://doi.org/10.3847/1538-4357/adf069 |
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