Update README.rst (#121)

Add recent papers using Swyft.

README.rst

@@ -67,6 +67,27 @@ Swyft in action
 * The package additionally implements our prior truncation technique, routines to empirically test results by estimating the expected coverage, and a simulator manager with `zarr <https://zarr.readthedocs.io/en/stable/>`_ storage to simplify use with complex simulators.
 
 
+Papers using Swyft/TMNRE
+------------------------
+
+2021
+
+- “Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation“ Cole+ https://arxiv.org/abs/2111.08030
+
+2022
+
+- “Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation” Anau Montel+, https://arxiv.org/abs/2205.09126
+- “SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation” Karchev+ https://arxiv.org/abs/2209.06733
+- “One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses” Coogan+ https://arxiv.org/abs/2209.09918
+- “Detection is truncation: studying source populations with truncated marginal neural ratio estimation” Anau Montel+ https://arxiv.org/abs/2211.04291
+
+2023
+
+- “Debiasing Standard Siren Inference of the Hubble Constant with Marginal Neural Ratio Estimation” Gagnon-Hartman+ https://arxiv.org/abs/2301.05241
+- “Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation” Saxena+ https://arxiv.org/abs/2303.07339
+- “Peregrine: Sequential simulation-based inference for gravitational wave signals”, Bhardwaj+ https://arxiv.org/abs/2304.02035
+- “Albatross: A scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way”, Alvey+ https://arxiv.org/abs/2304.02032
+
 
 Further information
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