Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308343132> ?p ?o ?g. }
- W4308343132 endingPage "129" @default.
- W4308343132 startingPage "111" @default.
- W4308343132 abstract "ABSTRACT 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 (DM)-only cluster-size haloes. The training set is built from the three hundred project that consists of a series of zoomed hydrodynamical simulations of cluster-size regions extracted from the 1 Gpc volume MultiDark DM-only simulation (MDPL2). We use as target variables a set of baryonic properties for the intracluster gas and stars derived from the hydrodynamical simulations and correlate them with the properties of the DM haloes from the MDPL2 N-body simulation. The different ML models are trained from this data base and subsequently used to infer the same baryonic properties for the whole range of cluster-size haloes identified in the MDPL2. We also test the robustness of the predictions of the models against mass resolution of the DM haloes and conclude that their inferred baryonic properties are rather insensitive to their DM properties that are resolved with almost an order of magnitude smaller number of particles. We conclude that the ML models presented in this paper can be used as an accurate and computationally efficient tool for populating cluster-size haloes with observational related baryonic properties in large volume N-body simulations making them more valuable for comparison with full sky galaxy cluster surveys at different wavelengths. We make the best ML trained model publicly available." @default.
- W4308343132 created "2022-11-11" @default.
- W4308343132 creator A5010446090 @default.
- W4308343132 creator A5016508738 @default.
- W4308343132 creator A5025323595 @default.
- W4308343132 creator A5043173254 @default.
- W4308343132 creator A5046222584 @default.
- W4308343132 creator A5049657406 @default.
- W4308343132 creator A5054746168 @default.
- W4308343132 creator A5077540864 @default.
- W4308343132 date "2022-11-03" @default.
- W4308343132 modified "2023-10-11" @default.
- W4308343132 title "Machine learning methods to estimate observational properties of galaxy clusters in large volume cosmological <i>N</i>-body simulations" @default.
- W4308343132 cites W1520258040 @default.
- W4308343132 cites W1573878051 @default.
- W4308343132 cites W1840802971 @default.
- W4308343132 cites W1892812743 @default.
- W4308343132 cites W1927701384 @default.
- W4308343132 cites W1947909385 @default.
- W4308343132 cites W1952814243 @default.
- W4308343132 cites W1970601257 @default.
- W4308343132 cites W1980050709 @default.
- W4308343132 cites W2003098733 @default.
- W4308343132 cites W2006340638 @default.
- W4308343132 cites W2008877686 @default.
- W4308343132 cites W2012467990 @default.
- W4308343132 cites W2023344562 @default.
- W4308343132 cites W2033933651 @default.
- W4308343132 cites W2042956136 @default.
- W4308343132 cites W2076063813 @default.
- W4308343132 cites W2096825304 @default.
- W4308343132 cites W2097565757 @default.
- W4308343132 cites W2102636708 @default.
- W4308343132 cites W2103435342 @default.
- W4308343132 cites W2119131940 @default.
- W4308343132 cites W2129329254 @default.
- W4308343132 cites W2141968105 @default.
- W4308343132 cites W2160146618 @default.
- W4308343132 cites W2177551589 @default.
- W4308343132 cites W2197026494 @default.
- W4308343132 cites W2204815181 @default.
- W4308343132 cites W2285223852 @default.
- W4308343132 cites W2299369549 @default.
- W4308343132 cites W2434357840 @default.
- W4308343132 cites W2515276161 @default.
- W4308343132 cites W2604475176 @default.
- W4308343132 cites W2604504584 @default.
- W4308343132 cites W2604575963 @default.
- W4308343132 cites W2752493223 @default.
- W4308343132 cites W2773844955 @default.
- W4308343132 cites W2783192224 @default.
- W4308343132 cites W2797384911 @default.
- W4308343132 cites W2802643674 @default.
- W4308343132 cites W2887742289 @default.
- W4308343132 cites W2899767859 @default.
- W4308343132 cites W2904186977 @default.
- W4308343132 cites W2911964244 @default.
- W4308343132 cites W2912395310 @default.
- W4308343132 cites W2964186773 @default.
- W4308343132 cites W2981731882 @default.
- W4308343132 cites W3012085163 @default.
- W4308343132 cites W3016968766 @default.
- W4308343132 cites W3024377461 @default.
- W4308343132 cites W3045842115 @default.
- W4308343132 cites W3081125651 @default.
- W4308343132 cites W3084112123 @default.
- W4308343132 cites W3098140412 @default.
- W4308343132 cites W3100110286 @default.
- W4308343132 cites W3100377699 @default.
- W4308343132 cites W3102476541 @default.
- W4308343132 cites W3103101999 @default.
- W4308343132 cites W3103145119 @default.
- W4308343132 cites W3103913848 @default.
- W4308343132 cites W3105465984 @default.
- W4308343132 cites W3106014062 @default.
- W4308343132 cites W3121540997 @default.
- W4308343132 cites W3122238445 @default.
- W4308343132 cites W3157282329 @default.
- W4308343132 cites W3183567416 @default.
- W4308343132 cites W3208715870 @default.
- W4308343132 cites W3211732184 @default.
- W4308343132 cites W4281253255 @default.
- W4308343132 cites W4282822400 @default.
- W4308343132 cites W4285226910 @default.
- W4308343132 cites W4292875581 @default.
- W4308343132 cites W429766147 @default.
- W4308343132 cites W4306411705 @default.
- W4308343132 cites W3037253767 @default.
- W4308343132 doi "https://doi.org/10.1093/mnras/stac3009" @default.
- W4308343132 hasPublicationYear "2022" @default.
- W4308343132 type Work @default.
- W4308343132 citedByCount "4" @default.
- W4308343132 countsByYear W43083431322023 @default.
- W4308343132 crossrefType "journal-article" @default.
- W4308343132 hasAuthorship W4308343132A5010446090 @default.
- W4308343132 hasAuthorship W4308343132A5016508738 @default.
- W4308343132 hasAuthorship W4308343132A5025323595 @default.
- W4308343132 hasAuthorship W4308343132A5043173254 @default.