Bayesian Multi-line Intensity Mapping
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910534691979264 |
|---|---|
| author | Cheng, Yun-Ting Wang, Kailai Wandelt, Benjamin D. Chang, Tzu-Ching Doré, Olivier |
| author_facet | Cheng, Yun-Ting Wang, Kailai Wandelt, Benjamin D. Chang, Tzu-Ching Doré, Olivier |
| contents | Line intensity mapping (LIM) has emerged as a promising tool for probing the 3D large-scale structure through the aggregate emission of spectral lines. The presence of interloper lines poses a crucial challenge in extracting the signal from the target line in LIM. In this work, we introduce a novel method for LIM analysis that simultaneously extracts line signals from multiple spectral lines, utilizing the covariance of native LIM data elements defined in the spectral--angular space. We leverage correlated information from different lines to perform joint inference on all lines simultaneously, employing a Bayesian analysis framework. We present the formalism, demonstrate our technique with a mock survey setup resembling the SPHEREx deep field observation, and consider four spectral lines within the SPHEREx spectral coverage in the near infrared: H$α$, $[$\ion{O}{3}$]$, H$β$, and $[$\ion{O}{2}$]$. We demonstrate that our method can extract the power spectrum of all four lines at the $\gtrsim 10σ$ level at $z<2$. For the brightest line, H$α$, the $10σ$ sensitivity can be achieved out to $z\sim3$. Our technique offers a flexible framework for LIM analysis, enabling simultaneous inference of signals from multiple line emissions while accommodating diverse modeling constraints and parameterizations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Multi-line Intensity Mapping Cheng, Yun-Ting Wang, Kailai Wandelt, Benjamin D. Chang, Tzu-Ching Doré, Olivier Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Line intensity mapping (LIM) has emerged as a promising tool for probing the 3D large-scale structure through the aggregate emission of spectral lines. The presence of interloper lines poses a crucial challenge in extracting the signal from the target line in LIM. In this work, we introduce a novel method for LIM analysis that simultaneously extracts line signals from multiple spectral lines, utilizing the covariance of native LIM data elements defined in the spectral--angular space. We leverage correlated information from different lines to perform joint inference on all lines simultaneously, employing a Bayesian analysis framework. We present the formalism, demonstrate our technique with a mock survey setup resembling the SPHEREx deep field observation, and consider four spectral lines within the SPHEREx spectral coverage in the near infrared: H$α$, $[$\ion{O}{3}$]$, H$β$, and $[$\ion{O}{2}$]$. We demonstrate that our method can extract the power spectrum of all four lines at the $\gtrsim 10σ$ level at $z<2$. For the brightest line, H$α$, the $10σ$ sensitivity can be achieved out to $z\sim3$. Our technique offers a flexible framework for LIM analysis, enabling simultaneous inference of signals from multiple line emissions while accommodating diverse modeling constraints and parameterizations. |
| title | Bayesian Multi-line Intensity Mapping |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2403.19740 |