Relic gravitational waves(RGWs)from the early Universe carry crucial and fundamental cosmological information.Therefore,it is of extraordinary importance to investigate potential RGW signals in the data from observato...Relic gravitational waves(RGWs)from the early Universe carry crucial and fundamental cosmological information.Therefore,it is of extraordinary importance to investigate potential RGW signals in the data from observatories such as the LIGO-Virgo-KAGRA network.Here,focusing on typical RGWs from the inflation and the first-order phase transition(by sound waves and bubble collisions),effective and targeted deep learning neural networks are established to search for these RGW signals within the real LIGO data(O2,O3a and O3b).Through adjustment and adaptation processes,we develop suitable Convolutional Neural Networks(CNNs)to estimate the likelihood(characterized by quantitative values and distributions)that the focused RGW signals are present in the LIGO data.We find that if the constructed CNN properly estimates the parameters of the RGWs,it can determine with high accuracy(approximately 94%to 99%)whether the samples contain such RGW signals;otherwise,the likelihood provided by the CNN cannot be considered reliable.After testing a large amount of LIGO data,the findings show no evidence of RGWs from:1)inflation,2)sound waves,or 3)bubble collisions,as predicted by the focused theories.The results also provide upper limits of their GW spectral energy densities of h^(2)Ω_(gw)~10^(-5),respectively for parameter boundaries within 1)[β∈(-1.87,-1.85)×α∈(0.005,0.007)],2)[β/H_(pt)∈(0.02,0.16)×α∈(1,10)×T_(pt)∈(5*10^(9),10^(10))Gev],and 3)[β/H_(pt)∈(0.08,0.2)×α∈(1,10)×T_(pt)∈(5*10^(9),8*10^(10))Gev].In short,null results and upper limits are obtained,and the analysis suggests that our developed methods and neural networks to search for typical RGWs in the LIGO data are effective and reliable,providing a viable scheme for exploring possible RGWs from the early Universe and placing constraints on relevant cosmological theories.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant Nos.11605015,12347101 and 12147102the Natural Scienceof Chongqing under Grant No.cstc2020jcyjmsxm X0944the Research Funds for the Central Universities under Grant No.2022CDJXY-002。
文摘Relic gravitational waves(RGWs)from the early Universe carry crucial and fundamental cosmological information.Therefore,it is of extraordinary importance to investigate potential RGW signals in the data from observatories such as the LIGO-Virgo-KAGRA network.Here,focusing on typical RGWs from the inflation and the first-order phase transition(by sound waves and bubble collisions),effective and targeted deep learning neural networks are established to search for these RGW signals within the real LIGO data(O2,O3a and O3b).Through adjustment and adaptation processes,we develop suitable Convolutional Neural Networks(CNNs)to estimate the likelihood(characterized by quantitative values and distributions)that the focused RGW signals are present in the LIGO data.We find that if the constructed CNN properly estimates the parameters of the RGWs,it can determine with high accuracy(approximately 94%to 99%)whether the samples contain such RGW signals;otherwise,the likelihood provided by the CNN cannot be considered reliable.After testing a large amount of LIGO data,the findings show no evidence of RGWs from:1)inflation,2)sound waves,or 3)bubble collisions,as predicted by the focused theories.The results also provide upper limits of their GW spectral energy densities of h^(2)Ω_(gw)~10^(-5),respectively for parameter boundaries within 1)[β∈(-1.87,-1.85)×α∈(0.005,0.007)],2)[β/H_(pt)∈(0.02,0.16)×α∈(1,10)×T_(pt)∈(5*10^(9),10^(10))Gev],and 3)[β/H_(pt)∈(0.08,0.2)×α∈(1,10)×T_(pt)∈(5*10^(9),8*10^(10))Gev].In short,null results and upper limits are obtained,and the analysis suggests that our developed methods and neural networks to search for typical RGWs in the LIGO data are effective and reliable,providing a viable scheme for exploring possible RGWs from the early Universe and placing constraints on relevant cosmological theories.
