text: 394,18,1399666122140626947,2872853608,Paula Soares,"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: <LINK> 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: <LINK> 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!! 🙂",http://arxiv.org/abs/2105.12665,"We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift HI intensity mapping, and present an open-source Python toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21cm foregrounds (including polarisation leakage), HI cosmological signal and instrumental noise. We find that it is possible to use GPR as a foreground removal technique in this context, and that it is better suited in some cases to recover the HI power spectrum than Principal Component Analysis (PCA), especially on small scales. GPR is especially good at recovering the radial power spectrum, outperforming PCA when considering the full bandwidth of our data. Both methods are worse at recovering the transverse power spectrum, since they rely on frequency-only covariance information. When halving our data along frequency, we find that GPR performs better in the low frequency range, where foregrounds are brighter. It performs worse than PCA when frequency channels are missing, to emulate RFI flagging. We conclude that GPR is an excellent foreground removal option for the case of single-dish, low redshift HI intensity mapping in the absence of missing frequency channels. Our Python toolkit gpr4im and the data used in this analysis are publicly available on GitHub. ","Gaussian Process Regression for foreground removal in HI intensity