Fix citation, better explanations, and fix English

.pre-commit-config.yaml

@@ -40,7 +40,7 @@ repos:
     rev: v2.3.0
     hooks:
       - id: codespell
-        args: ["-LOnd"]
+        args: ["-LOnd,Leary"]
         exclude: ^docs/usage/intro.ipynb$
 
   - repo: https://github.com/rbubley/mirrors-prettier

paper/paper.bib

@@ -45,7 +45,8 @@ @inproceedings{rocklin:2015
   booktitle={Proceedings of the 14th python in science conference},
   number={130-136},
   year={2015},
-  organization={Citeseer}
+  organization={Citeseer},
+  doi={10.25080/Majora-7b98e3ed-013}
 }
 
 @inproceedings{lam:2015,
@@ -201,3 +202,22 @@ @software{root:2020
   doi          = {10.5281/zenodo.3895860},
   url          = {https://doi.org/10.5281/zenodo.3895860}
 }
+
+@software{Bradbury:2018,
+  author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
+  title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
+  url = {http://github.com/jax-ml/jax},
+  version = {0.3.13},
+  year = {2018},
+}
+
+@inproceedings{Ansel:2024,
+author = {Ansel, Jason and Yang, Edward and He, Horace and Gimelshein, Natalia and Jain, Animesh and Voznesensky, Michael and Bao, Bin and Bell, Peter and Berard, David and Burovski, Evgeni and Chauhan, Geeta and Chourdia, Anjali and Constable, Will and Desmaison, Alban and DeVito, Zachary and Ellison, Elias and Feng, Will and Gong, Jiong and Gschwind, Michael and Hirsh, Brian and Huang, Sherlock and Kalambarkar, Kshiteej and Kirsch, Laurent and Lazos, Michael and Lezcano, Mario and Liang, Yanbo and Liang, Jason and Lu, Yinghai and Luk, CK and Maher, Bert and Pan, Yunjie and Puhrsch, Christian and Reso, Matthias and Saroufim, Mark and Siraichi, Marcos Yukio and Suk, Helen and Suo, Michael and Tillet, Phil and Wang, Eikan and Wang, Xiaodong and Wen, William and Zhang, Shunting and Zhao, Xu and Zhou, Keren and Zou, Richard and Mathews, Ajit and Chanan, Gregory and Wu, Peng and Chintala, Soumith},
+booktitle = {29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS '24)},
+doi = {10.1145/3620665.3640366},
+month = apr,
+publisher = {ACM},
+title = {{PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation}},
+url = {https://pytorch.org/assets/pytorch2-2.pdf},
+year = {2024}
+}

paper/paper.md

@@ -41,7 +41,8 @@ data, data with variable-sized rows (or a nested data structure with variable-si
 entries); thus, the library is expected to perform operations on an entire
 ragged structure in minimum passes. Furthermore, optimizing memory usage and
 processing time has become essential with the increasing computational demands
-at the LHC. Vector is a Python library for creating and manipulating 2D, 3D,
+at the the Large Hadron Collider (LHC), world’s largest particle accelerator.
+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
@@ -51,12 +52,12 @@ high energy physics experiments.
 
 # Statement of need
 
-Vector is one of the few Lorentz vector libraries providing a Pythonic interface
-but a compiled (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
+Vector is the only actively developed Lorentz vector library with
+a Pythonic interface but a compiled (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 a set of diverse
 backends, 3 numerical backends for experimental physicists and 1 symbolic
@@ -68,21 +69,22 @@ backend for theoretical physicists. These backends include:
 - an Awkward backend for computations on ragged collection-type data
 
 There also exists implementations of the Object and the Awkward backend in Numba
-for just-in-time compilable operations. Further, support for JAX and Dask is
-provided through the Awkward backend, which enables vector functionalities to
-support automatic differentiation and parallel computing.
+for just-in-time compilable operations. Further, support for JAX [@Bradbury:2018]
+and Dask is provided through the Awkward backend, which enables vector
+functionalities to support automatic differentiation and parallel computing.
 
 ## Impact
 
 Besides PyROOT's LorentzVectors and TLorentzVector [@root:2020], vector has
 become a popular choice for mathematical manipulations in Python based high energy
 physics analysis pipelines. Along with being utilized directly in
-analysis pipelines at LHC and other experiments [@Kling:2023; @Held:2024; @Qu:2022],
-the library is being 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 often used in non high energy physics use cases.
+analysis pipelines at LHC [@Kling:2023; @Held:2024; @Qu:2022], the library is
+being used in other high energy physics experiments asn as a dependency in other
+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
+often used in non high energy physics use cases.
 
 # Acknowledgements