text: 161,37,1288395874192830464,196749454,Natalie Hogg,"Paper day! <LINK> Ever wondered if you've discovered some beyond LCDM physics by accident? Could there be some hidden modified gravity effects lurking in gravitational wave detections? Have we found a new smoking gun for modified gravity? Read on to find out! Matteo (@matmartinelli1), Savvas and I created and used mock standard siren datasets to forecast the ability of the Einstein Telescope (ET), LSST and DESI to constrain the distance duality relation (DDR), which relates luminosity distances to angular diameter distances. We used a toy model in which photons decay into axions to break the DDR, and found that the combination of SNIa + GW events is competitive with the more commonly used SNIa + BAO when constraining deviations from DDR. (Paging the chair of the axion fan club @duetosymmetry 😉) But it pays to be careful when using a probe of the gravitational sector as modified gravity (MG) effects could be at play! By including a generic MG model in our mock datasets, we found that the DDR analysis became extremely biased, leading to a false detection of DDR violation! <LINK> However, the problem can be resolved by explicitly including the modified gravity in your analysis. Here, the full combination of mock datasets broke the degeneracies in parameter space and correctly recovered the fiducial cosmology. No more false detection of DDR violation! <LINK> Of course, if you have modified gravity, you will likely have a screening mechanism to go with it. GW events and SNIa are both events in which MG could be screened -- how does this affect our results? If GW are screened, we find another false detection of DDR violation... but if the SNIa are screened, we find that the cosmological parameters are also biased away from the fiducial cosmology, with only the combination LSST + DESI correctly recovering the fiducial. If this is seen in real data, it's a smoking gun for MG with this screening behaviour! <LINK> Savvas also applied his Genetic Algorithm machine learning code to reconstruct the DDR as a function of redshift, finding that it can correctly distinguish between the LCDM and MG mock datasets and finds the same biases as in the parameterised case, nicely confirming our results. Final tweet! This is the first time I've shared a paper draft with people other than fellow authors before posting it on arXiv and we're really grateful for all the comments and feedback we received -- shout out to Ian, Kazuya, Carlos, Isaac and Bill (@BillWrightCosmo)!",https://arxiv.org/abs/2007.14335,"We use gravitational wave (GW) standard sirens, in addition to Type Ia supernovae (SNIa) and baryon acoustic oscillation (BAO) mock data, to forecast constraints on the electromagnetic and gravitational distance duality relations (DDR). We make use of a parameterised approach based on a specific DDR violation model, along with a machine learning reconstruction method based on the Genetic Algorithms. We find that GW provide an alternative to the use of BAO data to constrain violations of the DDR, reaching $3\%$ constraints on the violation parameter we consider when combined with SNIa, which is only improved by a factor of $\approx1.4$ if one instead considers the combination of BAO and SNIa. We also investigate the possibility that a neglected modification of gravity might lead to a false detection of DDR violations, even when screening mechanisms are active. We find that such a false detection can be extremely significant, up to $\approx10\sigma$ for very extreme modified gravity scenarios, while this reduces to $\approx4\sigma$ in a more realistic case. False detections can also provide a smoking gun for the modified gravity mechanism at play, as a result of the tension introduced between the SNIa+GW and SNIa+BAO combinations. ",Constraints on the distance duality relation with standard sirens,9,"[""Paper day! <LINK>\n\nEver wondered if you've discovered some beyond LCDM physics by accident? Could there be some hidden modified gravity effects lurking in gravitational wave detections? Have we found a new smoking gun for modified gravity? Read on to find out!"", 'Matteo (@matmartinelli1), Savvas and I created and used mock standard siren datasets to forecast the ability of the Einstein Telescope (ET), LSST and DESI to constrain the distance duality relation (DDR), which relates luminosity distances to angular diameter distances.', 'We used a toy model in which photons decay into axions to break the DDR, and found that the combination of SNIa + GW events is competitive with the more commonly used SNIa + BAO when constraining deviations from DDR.\n\n(Paging the chair of the axion fan club @duetosymmetry 😉)', 'But it pays to be careful when using a probe of the gravitational sector as modified gravity (MG) effects could be at play! By including a generic MG model in our mock datasets, we found that the DDR analysis became extremely biased, leading to a false detection of DDR violation! https://t.co/etHLlyjjT7', 'However, the problem can be resolved by explicitly including the modified gravity in your analysis. Here, the full combination of mock datasets broke the degeneracies in parameter space and correctly recovered the fiducial cosmology. No more false detection of DDR violation! https://t.co/xLLRczzwIb', 'Of course, if you have modified gravity, you will likely have a screening mechanism to go with it. GW events and SNIa are both events in which MG could be screened -- how does this affect our results? If GW are screened, we find another false detection of DDR violation...', ""but if the SNIa are screened, we find that the cosmological parameters are also biased away from the fiducial cosmology, with only the combination LSST + DESI correctly recovering the fiducial. If this is seen in real data, it's a smoking gun for MG with this screening behaviour! https://t.co/wai5D8oxgK"", 'Savvas also applied his Genetic Algorithm machine learning code to reconstruct the DDR as a function of redshift, finding that it can correctly distinguish between the LCDM and MG mock datasets and finds the same biases as in the parameterised case, nicely confirming our results.', ""Final tweet! This is the first time I've shared a paper draft with people other than fellow authors before posting it on arXiv and we're really grateful for all the comments and feedback we received -- shout out to Ian, Kazuya, Carlos, Isaac and Bill (@BillWrightCosmo)!""]",20,07,2489