# Optimizing BAO measurements with non-linear transformations of the Lyman-α forest

## Abstract

We explore the effect of applying a non-linear transformation to the Lyman-α forest transmitted flux F=e{sup −τ} and the ability of analytic models to predict the resulting clustering amplitude. Both the large-scale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signal-to-noise ratio on large scales using Taylor expansion up to the third order. In particular, however, we achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore an analytic model for the large-scale biasing of the Lyα forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to large-scale density fluctuations, especially for very nonlinear transformations.

- Authors:

- Department of Physics, University of California, South Hall Rd, Berkeley (United States)

- Publication Date:

- OSTI Identifier:
- 22525907

- Resource Type:
- Journal Article

- Resource Relation:
- Journal Name: Journal of Cosmology and Astroparticle Physics; Journal Volume: 2015; Journal Issue: 04; Other Information: Country of input: International Atomic Energy Agency (IAEA)

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; BARYONS; DENSITY; FLUCTUATIONS; HYDRODYNAMICS; LYMAN LINES; NONLINEAR PROBLEMS; OPTIMIZATION; OSCILLATIONS; SIGNAL-TO-NOISE RATIO; TRANSFORMATIONS; VELOCITY

### Citation Formats

```
Wang, Xinkang, Font-Ribera, Andreu, and Seljak, Uroš, E-mail: xinkang.wang@berkeley.edu, E-mail: afont@lbl.gov, E-mail: useljak@berkeley.edu.
```*Optimizing BAO measurements with non-linear transformations of the Lyman-α forest*. United States: N. p., 2015.
Web. doi:10.1088/1475-7516/2015/04/009.

```
Wang, Xinkang, Font-Ribera, Andreu, & Seljak, Uroš, E-mail: xinkang.wang@berkeley.edu, E-mail: afont@lbl.gov, E-mail: useljak@berkeley.edu.
```*Optimizing BAO measurements with non-linear transformations of the Lyman-α forest*. United States. doi:10.1088/1475-7516/2015/04/009.

```
Wang, Xinkang, Font-Ribera, Andreu, and Seljak, Uroš, E-mail: xinkang.wang@berkeley.edu, E-mail: afont@lbl.gov, E-mail: useljak@berkeley.edu. Wed .
"Optimizing BAO measurements with non-linear transformations of the Lyman-α forest". United States.
doi:10.1088/1475-7516/2015/04/009.
```

```
@article{osti_22525907,
```

title = {Optimizing BAO measurements with non-linear transformations of the Lyman-α forest},

author = {Wang, Xinkang and Font-Ribera, Andreu and Seljak, Uroš, E-mail: xinkang.wang@berkeley.edu, E-mail: afont@lbl.gov, E-mail: useljak@berkeley.edu},

abstractNote = {We explore the effect of applying a non-linear transformation to the Lyman-α forest transmitted flux F=e{sup −τ} and the ability of analytic models to predict the resulting clustering amplitude. Both the large-scale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signal-to-noise ratio on large scales using Taylor expansion up to the third order. In particular, however, we achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore an analytic model for the large-scale biasing of the Lyα forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to large-scale density fluctuations, especially for very nonlinear transformations.},

doi = {10.1088/1475-7516/2015/04/009},

journal = {Journal of Cosmology and Astroparticle Physics},

number = 04,

volume = 2015,

place = {United States},

year = {Wed Apr 01 00:00:00 EDT 2015},

month = {Wed Apr 01 00:00:00 EDT 2015}

}