https://github.com/simonsobs/ksz4
Code for estimating the kSZ trispectrum
https://github.com/simonsobs/ksz4
Last synced: over 1 year ago
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Code for estimating the kSZ trispectrum
- Host: GitHub
- URL: https://github.com/simonsobs/ksz4
- Owner: simonsobs
- Created: 2025-01-15T12:40:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-12T11:02:51.000Z (over 1 year ago)
- Last Synced: 2025-02-12T11:37:15.327Z (over 1 year ago)
- Language: Python
- Size: 407 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# ksz4
ksz4 provides tools for estimating the kSZ trispectrum from CMB data, as motivated by Smith and Ferraro (https://arxiv.org/abs/1607.01769), Ferraro and Smith (https://arxiv.org/abs/1803.07036). Based on the code used for MacCrann et al. 2024 (https://arxiv.org/abs/2405.01188)
We use a quadratic estimator approach, i.e. the trispectrum, $C_L^{KK}$, is calculated as the power spectrum of $K = Q(T_1, T_2)$, with $Q(T_1, T_2)$ denoting a quadratic estimator applied to CMB temperature maps $T_1$ and $T_2$. We use the formalism because of the similiarities to CMB lensing estimation, from which we adapt various tools such as Gaussian noise ($N^0$) estimation and bias-hardening.
As well as "lensing-hardened" estimators (see MacCrann et al. 2024), we provide estimators and analytic $N^0$ calcualations for the general case $C_L^{KK} = < Q(T_1,T_2) Q(T_3,T_4) >$, where $T_i$ could be CMB temperature maps with e.g. different noise levels (e.g. because they have different foreground treatments applied).
## Dependencies
- [healpy](https://healpy.readthedocs.io/en/latest/index.html#)
- [pixell](https://github.com/simonsobs/pixell)
- [oprhics](https://github.com/msyriac/orphics)
- [falafel](https://github.com/simonsobs/falafel/tree/master)
- [tempura](https://github.com/simonsobs/tempura)
## How to use
- The notebook `examples/test_simple` goes through the basic usage - applying the estimator to a simulated map and subtracting $N^0$.
- The notebooks `examples/test_signal_and_N0.ipynb` and `examples/test_bias-hardening.ipynb` go through more complex usage, more general estimators and bias-hardening respectively.