Gonzalo Martínez-Muñoz's research while affiliated with Universidad Autónoma de Madrid and other places
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Publications (3)
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only cluster-size halos. The training set is built from The Three Hundred project which consists of a series of zoomed...
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only cluster-size halos. The training set is built from THE THREE HUNDRED project which consists of a series of zoomed...
Citations
... The300 Project (Cui et al. 2020) is a re-simulation of a mass-complete sample of 324 galaxy clusters from the Multi-Dark Planck 2 (MDPL2) N -body simulation (Klypin et al. 2016) 3 with 1Gpc/h simulation box size (see Zhang et al. 2022;de Andres et al. 2022de Andres et al. , 2023, for the benefit of this particular setup). These clusters are identified with the rockstar (Behroozi et al. 2013) halo finder. ...
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... The method called Gradient Boosted Neural Network (GBNN) involves constructing one NN by utilizing the trained weights of multiple individual networks, each trained on the residual loss sequentially. This work has been further extended to handle multi-output regression [21]. ...
