Abstract
Many charged particle imaging measurements rely on the inverse Abel transform (or related methods) to reconstruct three-dimensional (3D) photoproduct distributions from a single two-dimensional (2D) projection image. This technique allows for both energy- and angle-resolved information to be recorded in a relatively inexpensive experimental setup, and its use is now widespread within the field of photochemical dynamics. There are restrictions, however, as cylindrical symmetry constraints on the overall form of the distribution mean that it can only be used with a limited range of laser polarization geometries. The more general problem of reconstructing arbitrary 3D distributions from a single 2D projection remains open. Here, we demonstrate how artificial neural networks can be used as a replacement for the inverse Abel transform and-more importantly-how they can be used to directly "reinflate" 2D projections into their original 3D distributions, even in cases where no cylindrical symmetry is present. This is subject to the simulation of appropriate training data based on known analytical expressions describing the general functional form of the overall anisotropy. Using both simulated and real experimental data, we show how our arbitrary image reinflation (AIR) neural network can be utilized for a range of different examples, potentially offering a simple and flexible alternative to more expensive and complicated 3D imaging techniques.
Highlights
Over the past three decades, charged particle imaging has become a widely used experimental approach within the field of gas phase chemical dynamics.[1]
We demonstrate how artificial neural networks can be used as a replacement for the inverse Abel transform and—more importantly—how they can be used to directly “reinflate” 2D projections into their original 3D distributions, even in cases where no cylindrical symmetry is present
This is subject to the simulation of appropriate training data based on known analytical expressions describing the general functional form of the overall anisotropy. Using both simulated and real experimental data, we show how our arbitrary image reinflation (AIR) neural network can be utilized for a range of different examples, potentially offering a simple and flexible alternative to more expensive and complicated 3D imaging techniques
Summary
Over the past three decades, charged particle imaging has become a widely used experimental approach within the field of gas phase chemical dynamics.[1]. The low mass of hydrogen ions and, in particular, photoelectrons makes data acquisition with these species much more challenging, .[20,21] Using an alternative strategy, advanced time-resolved detectors[14,15,16,17,18] are capable of recording both the (y, z) pixel coordinate and arrival time t of incoming photoproducts This time information can be related to the x spatial coordinate, enabling direct recording of the full 3D distribution. Such complex detector technology is often expensive and so a reliable and stable inverse Abel transform for 2D projected data remains essential for simple image analysis in many experiments
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