Hyper-py: HYbrid Photometry and Extraction Routine in PYthon
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911178623549440 |
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| author | Traficante, Alessio De Angelis, Fabrizio Nucara, Alice Benedettini, Milena |
| author_facet | Traficante, Alessio De Angelis, Fabrizio Nucara, Alice Benedettini, Milena |
| contents | We present Hyper-Py, a fully restructured and extended Python implementation of HYPER (HYbrid Photometry and Extraction Routine, Traficante et al. 2015). HYPER was originally implemented in IDL, aiming to deliver robust and reproducible photometry of compact sources in FIR/sub-mm/mm maps. HYPER combines source detection via high-pass filtering, background estimation through local polynomial fitting, and source modeling with 2D elliptical Gaussians, simultaneously fitting multiple Gaussians to deblend overlapping sources. Hyper-Py preserves the original logic while offering improvements in performance, configurability, and background modeling capabilities, making it a flexible modern tool for source extraction and photometry across diverse datasets. Notably, Hyper-Py enables background estimation and subtraction across individual slices of 3D datacubes, allowing consistent background modeling along the spectral axis for line or continuum studies in spectrally resolved observations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22453 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hyper-py: HYbrid Photometry and Extraction Routine in PYthon Traficante, Alessio De Angelis, Fabrizio Nucara, Alice Benedettini, Milena Instrumentation and Methods for Astrophysics We present Hyper-Py, a fully restructured and extended Python implementation of HYPER (HYbrid Photometry and Extraction Routine, Traficante et al. 2015). HYPER was originally implemented in IDL, aiming to deliver robust and reproducible photometry of compact sources in FIR/sub-mm/mm maps. HYPER combines source detection via high-pass filtering, background estimation through local polynomial fitting, and source modeling with 2D elliptical Gaussians, simultaneously fitting multiple Gaussians to deblend overlapping sources. Hyper-Py preserves the original logic while offering improvements in performance, configurability, and background modeling capabilities, making it a flexible modern tool for source extraction and photometry across diverse datasets. Notably, Hyper-Py enables background estimation and subtraction across individual slices of 3D datacubes, allowing consistent background modeling along the spectral axis for line or continuum studies in spectrally resolved observations. |
| title | Hyper-py: HYbrid Photometry and Extraction Routine in PYthon |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2509.22453 |