Artículo
Galaxy rotation curve fitting using machine learning tools
Fecha de publicación:
08/2023
Editorial:
MDPI
Revista:
Universe
e-ISSN:
2218-1997
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Galaxy rotation curve (RC) fitting is an important technique which allows the placement of constraints on different kinds of dark matter (DM) halo models. In the case of non-phenomenological DM profiles with no analytic expressions, the art of finding RC best-fits including the full baryonic + DM free parameters can be difficult and time-consuming. In the present work, we use a gradient descent method used in the backpropagation process of training a neural network, to fit the so-called Grand Rotation Curve of the Milky Way (MW) ranging from ∼1 pc all the way to ∼10^5 pc. We model the mass distribution of our Galaxy including a bulge (inner + main), a disk, and a fermionic dark matter (DM) halo known as the Ruffini-Argüelles-Rueda (RAR) model. This is a semi-analytical model built from first-principle physics such as (quantum) statistical mechanics and thermodynamics, whose more general density profile has a dense core–diluted halo morphology with no analytic expression. As shown recently and further verified here, the dark and compact fermion-core can work as an alternative to the central black hole in SgrA* when including data at milliparsec scales from the S-cluster stars. Thus, we show the ability of this state-of-the-art machine learning tool in providing the best-fit parameters to the overall MW RC in the 10^−2–10^5 pc range, in a few hours of CPU time.
Palabras clave:
DARK MATTER
,
MILKY WAY
,
ROTATION CURVES
,
NUMERICAL METHODS
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Articulos(IALP)
Articulos de INST.DE ASTROFISICA LA PLATA
Articulos de INST.DE ASTROFISICA LA PLATA
Citación
Argüelles, Carlos Raúl; Collazo, Santiago; Galaxy rotation curve fitting using machine learning tools; MDPI; Universe; 9; 372; 8-2023; 1-9
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