# rBasex¶

## Introduction¶

This method resembles the pBasex [1] approach of expanding a velocity-map image over a 2D basis set in polar coordinates, but uses more convenient basis functions with analytical Abel transforms, developed by M. Ryazanov [2].

## How it works¶

In velocity-map imaging (VMI) with cylindrically symmetric photodissociation (in a broad sense, including photoionization and photodetachment) the 3D velocity distribution at each speed (3D radius) consists of a finite number of spherical harmonics $$Y_{nm}(\theta, \varphi)$$ with $$m = 0$$, which are also representable as Legendre polynomials $$P_n(\cos\theta)$$. This means that an $$N \times N$$ image has only $$N_r \times N_a$$ degrees of freedom, where $$N_r$$ is the number of radial samples, usually $$N / 2$$, and $$N_a$$ is the number of angular terms, a small number depending on the studied process. These degrees of freedom correspond to the “radial distribution” extracted from the transformed image in other, general Abel-inversion methods.

However, if these radial distributions are considered as a basis, the 3D distribution can be represented as a linear combination of these basis functions with some coefficients. And the corresponding image, being the forward Abel transform of the 3D distribution, will be represented as a linear combination of basis-function projections, that is, their forward Abel transforms, with the same coefficients. The reverse is also true: finding the expansion coefficients of an experimental velocity-map image over the projected basis directly gives the expansion coefficients of the initial 3D velocity direction and thus the sought radial distributions.

Finding the expansion coefficients is a simple linear problem, and the forward Abel transforms of the basis functions can be calculated easily if the basis is chosen wisely.

See rBasex: mathematical details for the complete description.

### Differences from pBasex¶

While rBasex is similar to pBasex in the idea of using VMI-oriented 3D basis functions, it has several key differences:

1. Triangular radial basis functions are used instead of Gaussians. They are more compact/orthogonal (only the adjacent functions overlap) and have analytical Abel transforms.

2. Cosine powers are used instead of Legendre polynomials for angular basis functions. This makes the projected basis functions also separable into radial and angular parts.

3. The basis separability allows decomposition of the problem in two steps: first, radial distributions are extracted from the image (without intermediate rebinning to polar grid, thus faster and avoiding accumulation of resampling errors); second, these radial distributions are expanded over radial bases for each angular order. This eliminates the necessity to work with large matrices.

4. Custom pixel weighting can be used, for example, to exclude image areas “damaged” in some way (obscured by a beam block, contaminated by parasitic signals, affected by detector imperfections and so on). Partial images (not including the whole angular range) can be reconstructed as well.

5. The forward Abel transform is implemented in addition to the inverse transform.

6. Additional (better) regularization methods are implemented.

### Differences from the reconstruction method described in [2]¶

Many ideas used in rBasex, including the analytically transformable basis functions, are taken from the previous work [2], but with some omissions, additions and modifications.

1. Instead of working with individual pixels and weighting them according to Poisson statistics, the binned radial distributions (not weighted by default) are transformed. This is less accurate, but much faster, especially in Python.

2. Slicing is not implemented.

3. Only the non-negativity constraints are implemented. However, several linear regularization options are added.

4. Odd angular orders can be included.

## When to use it¶

This method makes additional assumptions (beyond cylindrical symmetry) about the data, so it can be applied only to velocity-map images or in other similar situations involving a finite number of spherical harmonics. However, in this special case, it offers several benefits:

1. The reconstructed radial distributions, which are often the primary interest in VMI studies, are obtained directly.

2. Limitations on the angular behavior of the distribution also put strong constraints on the reconstruction noise, making the reconstructed images much cleaner.

3. Several optional regularization methods help to further reduce noise in reconstructed images, especially near the center. Regularization strengths can be adjusted to produce a desirable balance between noise reduction and blurring of sharp features.

4. Unlike general Abel-transform methods, which have time complexity with cubic dependence on the image size, this method is only quadratic, once the transform matrix is computed. Computing the transform matrix is still cubic, but after it is done, transforming a series of images is faster, especially for large images.

5. The optional non-negativity constraints implemented in this method allow obtaining physically meaningful intensity and anisotropy distributions. They can also help in denoising experimental images with very low event counts.

## How to use it¶

The method can be accessed through the universal abel.Transform class:

res = abel.Transform(image, method='rbasex')
recon = res.transform
distr = res.distr


optionally using other Transform arguments and passing additional rBasex parameters (see abel.rbasex.rbasex_transform() documentation for their full description) through the transform_options argument. Alternatively, it might be more convenient to use the method by calling its transform function directly:

recon, distr = abel.rbasex.rbasex_transform(image)
r, I, beta = distr.rIbeta()


It returns the transformed image recon and a Distributions.Results object distr, from which various radial distributions can be retrieved, such as the intensity and anisotropy-parameter distributions in this example.

If only the distributions are needed, but not the transformed image itself, the calculations can be accelerated by disabling the creation of the output image:

_, distr = abel.rbasex.rbasex_transform(image, out=None)
r, I, beta = distr.rIbeta()


Note that rBasex does not require the input image to be centered. Thus instead of centering it with center_image() (or using the origin argument of Transform), which will crop some data or fill it with zeros, it is better to pass the image origin directly to the transform function, determining it automatically, if needed:

origin = abel.tools.center.find_origin(image, method='convolution')
recon, distr = abel.rbasex.rbasex_transform(image, origin=origin)


This also must be done if optional pixel weighting is used, since otherwise the centered image would become inconsistent with the weights array. For example, when using the Transform class, pass the origin as follows:

res = abel.Transform(image, method='rbasex',
transform_options=dict(origin=..., weights=...))


The weights array can also be used as a mask, using zero weights to exclude unwanted pixels, as demonstrated in Example: rBasex beam block. In practice, instead of defining the mask geometry in the code, it might be more convenient to save the analyzed data as an image file:

# save as an RGB image using a chosen colormap


then open it in any raster graphics editor, paint the areas to be excluded with some distinct color (for example, blue in case of cmap='hot') and save it. This painted image then can be loaded in the program, and the mask is easily extracted from it:

# read as an array with R, G, B (or R, G, B, A) components