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Expand Up @@ -39,22 +39,22 @@ at the LHC. Vector is a Python library for creating and manipulating 2D, 3D,
and Lorentz vectors, especially arrays of vectors, to solve common physics
problems in a NumPy-like [@harris:2020] way. The library enables physicists to
operate on high energy physics data in a high level language without
compromising speed. The library is already in use at LHC and is a part of
frameworks, like Coffea [@Gray:2023], employed by physicists across multiple
compromising speed. The library is already in use at LHC and is a part of
frameworks, like Coffea [@Gray:2023], employed by physicists across multiple
high energy physics experiments.

# Statement of need

Vector is currently the only Lorentz vector library providing a Pythonic
interface but a C++ (through Awkward Array [@Pivarski:2018]) computational
backend. Vector integrates seamlessly with the existing high energy physics
ecosystem and the broader scientific Python ecosystem, including libraries like
Dask [@rocklin:2015] and Numba [@lam:2015]. The library implements a variety of
backends for several purposes. Although vector was written with high energy
physics in mind, it is a general-purpose library that can be used for any
scientific or engineering application. The library houses 3+2 numerical
backends for experimental physicists and 1 symbolic backend for theoretical
physicists. These backends include a pure Python object backend for simple
interface but a C++ (through Awkward Array [@Pivarski:2018]) computational
backend. Vector integrates seamlessly with the existing high energy physics
ecosystem and the broader scientific Python ecosystem, including libraries like
Dask [@rocklin:2015] and Numba [@lam:2015]. The library implements a variety of
backends for several purposes. Although vector was written with high energy
physics in mind, it is a general-purpose library that can be used for any
scientific or engineering application. The library houses 3+2 numerical
backends for experimental physicists and 1 symbolic backend for theoretical
physicists. These backends include a pure Python object backend for simple
computations, a SymPy [@Meurer:2017] backend for symbolic computations, a
NumPy backend for computations on regular data, an Awkward backend for
computations on jagged data, and implementations of the Object and the Awkward
Expand All @@ -68,17 +68,17 @@ Vector has become the de facto library for vector algebra in Python based high
energy physics data analysis pipelines. The library has been installed over
2 million times and 314 GitHub repositories use it as a dependency at the time
of writing this paper. Along with being utilized directly in analysis pipelines
at LHC and other experiments [@Kling:2023; @Held:2024; @Qu:2022], the library
is also used as a dependency in user-facing frameworks, such as, Coffea,
MadMiner [@Brehmer:2020], FastJet [@aryan:2023], Spyral [@spyral-utils:2024],
Weaver [@weaver-core:2024], and pylhe [@pylhe]. The library is also used in
multiple teaching materials for graduate courses and workshops. Finally, given
the generic nature of the library, it is also often used in non high energy
at LHC and other experiments [@Kling:2023; @Held:2024; @Qu:2022], the library
is also used as a dependency in user-facing frameworks, such as, Coffea,
MadMiner [@Brehmer:2020], FastJet [@aryan:2023], Spyral [@spyral-utils:2024],
Weaver [@weaver-core:2024], and pylhe [@pylhe]. The library is also used in
multiple teaching materials for graduate courses and workshops. Finally, given
the generic nature of the library, it is also often used in non high energy
physics use cases.

# Acknowledgements

The work on vector was supported by NSF cooperative agreements OAC-1836650
The work on vector was supported by NSF cooperative agreements OAC-1836650
(IRIS-HEP) and PHY-2323298 (IRIS-HEP). We would also like to thank the
contributors of vector and the Scikit-HEP community for their support.

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