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docs/JOSS/paper.md

@@ -38,22 +38,22 @@ bibliography: paper.bib
 Statistical analysis of High Energy Physics (HEP) data relies on quantifying the compatibility of observed collision events with theoretical predictions.
 The relationship between them is often formalised in a statistical model $f(\mathbf{x}|\mathbf{\phi})$ describing the probability of data $\mathbf{x}$ given model parameters $\mathbf{\phi}$.
 Given observed data, the likelihood $\mathcal{L}(\mathbf{\phi})$ then serves as the basis for inference on the parameters $\mathbf{\phi}$.
-For measurements based on binned data (histograms), the `HistFactory` family of statistical models [@Cranmer:1456844] has been widely used in both Standard Model measurements [@HIGG-2013-02] as well as searches for new physics [@ATLAS-CONF-2018-041].
-`pyhf` is a pure-Python implementation of the `HistFactory` model specification and implements a declarative, plain-text format for describing `HistFactory`-based likelihoods that is targeted for reinterpretation and long-term preservation in analysis data repositories such as HEPData [@Maguire:2017ypu].
+For measurements based on binned data (histograms), the `HistFactory` family of statistical models @Cranmer:1456844 has been widely used in both Standard Model measurements @HIGG-2013-02 as well as searches for new physics @ATLAS-CONF-2018-041.
+`pyhf` is a pure-Python implementation of the `HistFactory` model specification and implements a declarative, plain-text format for describing `HistFactory`-based likelihoods that is targeted for reinterpretation and long-term preservation in analysis data repositories such as HEPData @Maguire:2017ypu.
 
 Through adoption of open source "tensor" computational Python libraries, `pyhf` decreases the abstractions between a physicist performing an analysis and the statistical modeling without sacrificing computational speed.
 By taking advantage of tensor calculations, `pyhf` outperforms the traditional `C++` implementation of `HistFactory` on data from real LHC analyses.
 `pyhf`'s default computational backend is built from NumPy and SciPy, and supports TensorFlow, PyTorch, and JAX as alternative backend choices.
 These alternative backends support hardware acceleration on GPUs, and in the case of JAX JIT compilation, as well as auto-differentiation allowing for calculating the full gradient of the likelihood function — all contributing to speeding up fits.
-The source code for `pyhf` has been archived on Zenodo with the linked DOI: [@pyhf_zenodo]
+The source code for `pyhf` has been archived on Zenodo with the linked DOI: @pyhf_zenodo
 
 ## Impact on Physics
 
-In addition to enabling the first publication of full likelihoods by an LHC experiment [@ATL-PHYS-PUB-2019-029], `pyhf` has been used by the `SModelS` library to improve the reinterpretation of results of searches for new physics at LHC experiments [@Abdallah:2020pec], [@Khosa:2020zar], [@Alguero:2020grj].
+In addition to enabling the first publication of full likelihoods by an LHC experiment @ATL-PHYS-PUB-2019-029, `pyhf` has been used by the `SModelS` library to improve the reinterpretation of results of searches for new physics at LHC experiments @Abdallah:2020pec, @Khosa:2020zar, @Alguero:2020grj.
 
 ## Future work
 
-Future development aims to provide support limit setting through pseudoexperiment generation in the regimes in which asymptotic approximations [@Cowan:2010js] are no longer valid.
+Future development aims to provide support limit setting through pseudoexperiment generation in the regimes in which asymptotic approximations @Cowan:2010js are no longer valid.
 Further improvements to the performance of the library as well as API refinement are also planned.
 
 # Acknowledgements