Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos.In this work,we propose a widely a...Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos.In this work,we propose a widely applicable method for identifying groups through machine learning techniques in real space,taking into account the impact of redshift distortion.Our methodology involves two neural networks:one is a classification model for identifying central galaxy groups,and the other is a regression model for predicting the mass of these groups.Both models input observable galaxy traits,allowing future applicability to real survey data.Testing on simulated datasets indicates our method accurately identifies over 92%of groups with M_(vir)≥10^(11) h^(−1)M_(⊙),with 80%achieving a membership completeness of at least 80%.The predicted group masses vary by less than 0.3 dex across different mass scales,even in the absence of a priori data.Our network adapts seamlessly to expand to sparse samples with a flux limit of mr<14,to high redshift samples at z=1.08,and to galaxy samples from the TNG300 hydrodynamical simulation without further training.Furthermore,the framework can easily adjust to real surveys by training on redshift-distorted samples without needing parameter changes.Careful consideration of different observational effects in redshift space makes it promising that this method will be applicable to real galaxy surveys.展开更多
We present the application of a machine learning based galaxy group finder to real observational data from the Sloan Digital Sky Survey Data Release 13(SDSS DR13).Originally designed and validated using simulated gala...We present the application of a machine learning based galaxy group finder to real observational data from the Sloan Digital Sky Survey Data Release 13(SDSS DR13).Originally designed and validated using simulated galaxy surveys in redshift space,our method utilizes deep neural networks to recognize galaxy groups and assess their respective halo masses.The model comprises three components:a central galaxy identifier,a group mass estimator,and an iterative group finder.Using mock catalogs from the Millennium Simulation,our model attains above 90%completeness and purity for groups covering a wide range of halo masses from~10^(11)to~10^(15)h^(-1)Me.When applied to SDSS DR13,it successfully identifies over 420,000 galaxy groups,displaying a strong agreement in group abundance,redshift distribution,and halo mass distribution with conventional techniques.The precision in identifying member galaxies is also notably high,with more than 80%of groups with lower mass achieving perfect alignments.The model shows strong performance across different magnitude thresholds,making retraining unnecessary.These results confirm the efficiency and adaptability of our methodology,offering a scalable and accurate solution for upcoming large-scale galaxy surveys and studies of cosmological formations.Our SDSS group catalog and the essential observable properties of galaxies are available at https://github.com/Juntao Ma/SDSS-DR13-group-catalog.git.展开更多
基金supported by the National Key R&D Program of China(2022YFA1602901)the National Natural Science Foundation of China(NSFC,grant Nos.11988101,11873051,12125302,and 11903043)+2 种基金CAS Project for Young Scientists in Basic Research(grant No.YSBR-062)the China Manned Space Program(grant Nos.CMS-CSST-2025-A03 and CMSCSST-2025-A10)the K.C.Wong Education Foundation.
文摘Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos.In this work,we propose a widely applicable method for identifying groups through machine learning techniques in real space,taking into account the impact of redshift distortion.Our methodology involves two neural networks:one is a classification model for identifying central galaxy groups,and the other is a regression model for predicting the mass of these groups.Both models input observable galaxy traits,allowing future applicability to real survey data.Testing on simulated datasets indicates our method accurately identifies over 92%of groups with M_(vir)≥10^(11) h^(−1)M_(⊙),with 80%achieving a membership completeness of at least 80%.The predicted group masses vary by less than 0.3 dex across different mass scales,even in the absence of a priori data.Our network adapts seamlessly to expand to sparse samples with a flux limit of mr<14,to high redshift samples at z=1.08,and to galaxy samples from the TNG300 hydrodynamical simulation without further training.Furthermore,the framework can easily adjust to real surveys by training on redshift-distorted samples without needing parameter changes.Careful consideration of different observational effects in redshift space makes it promising that this method will be applicable to real galaxy surveys.
基金supported by the National Key R&D Program of China(2022YFA1602901)the National Natural Science Foundation of China(NSFC,grant Nos.11988101,11873051,12125302,and 11903043)+2 种基金CAS Project for Young Scientists in Basic Research(grant No.YSBR-062)the China Manned Space Program(grant Nos.CMS-CSST2025-A03 and CMS-CSST-2025-A10)the K.C.Wong Education Foundation。
文摘We present the application of a machine learning based galaxy group finder to real observational data from the Sloan Digital Sky Survey Data Release 13(SDSS DR13).Originally designed and validated using simulated galaxy surveys in redshift space,our method utilizes deep neural networks to recognize galaxy groups and assess their respective halo masses.The model comprises three components:a central galaxy identifier,a group mass estimator,and an iterative group finder.Using mock catalogs from the Millennium Simulation,our model attains above 90%completeness and purity for groups covering a wide range of halo masses from~10^(11)to~10^(15)h^(-1)Me.When applied to SDSS DR13,it successfully identifies over 420,000 galaxy groups,displaying a strong agreement in group abundance,redshift distribution,and halo mass distribution with conventional techniques.The precision in identifying member galaxies is also notably high,with more than 80%of groups with lower mass achieving perfect alignments.The model shows strong performance across different magnitude thresholds,making retraining unnecessary.These results confirm the efficiency and adaptability of our methodology,offering a scalable and accurate solution for upcoming large-scale galaxy surveys and studies of cosmological formations.Our SDSS group catalog and the essential observable properties of galaxies are available at https://github.com/Juntao Ma/SDSS-DR13-group-catalog.git.
