text:   mapping experiments",9,"['New paper! In <LINK> we showed for the first time that Gaussian Process Regression (GPR) can be used as a foreground removal technique in single-dish, low redshift 21cm intensity mapping. With @CunningtonSD, @CatAstro_Phy, and @AlkistisPou. [thread]', 'We also released some user-friendly code for running GPR as a foreground removal technique, which has a lot of introductory Jupyter notebooks and easy to follow examples: https://t.co/jT5EU3V41c', 'Astrophysical foregrounds dominate over the cosmological 21cm signal so need to be removed. GPR has already been successfully used as a foreground cleaning technique in the context of EoR (see e.g. arXiv:1711.10834), so we wanted to try it in the case of large-scale structure!', 'We used MeerKAT-like simulations of foregrounds, 21cm signal and instrumental noise, and compared the performance of GPR to another widely used foreground cleaning technique: Principal Component Analysis (PCA).', 'We find that GPR is very good at recovering the 21cm power spectrum, especially the radial power spectrum. It is in many cases better suited to recover the power spectrum than PCA, especially on small scales. Check out how well it performs when there is no polarisation leakage: https://t.co/jgNmuRPhz8', 'We also tested the case of including polarisation leakage, trimming the bandwidth, removing frequency channels, and tested if it is possible to implement a foreground transfer function with GPR (spoiler: it is!)', ""In summary: it works!! And you can use our code to try it out yourself 🙂 we also describe how GPR works in detail in the paper, so if you're interested check that out too!"", '@AmelieSaintonge @CunningtonSD @CatAstro_Phy @AlkistisPou Thank you Amélie! 🙂', '@guadalupecah Thank you!! 🙂']",21,05,1692