A constant characteristic volume density of dark matter haloes from SPARC rotation curve fits

Li, Pengfei; Lelli, Federico; McGaugh, Stacy S.; Starkman, Nathaniel; Schombert, James M.

doi:10.1093/mnras/sty2968

Cited by 46 publications

(75 citation statements)

References 46 publications

(74 reference statements)

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“…Simulated DM halos present a standard deviation of 0.16 dex around the mean relation. However, in real galaxies, the final distribution of α differs significantly from this relation if we do not impose it as a Bayesian prior (Li et al 2019). We hence include this relation as part of the ΛCDM priors (explained in Section 2.3).…”

Section: Dark Matter Halo Profilesmentioning

confidence: 99%

“…We hence set X = −1.3 as the largest acceptable value, following Katz et al (2017). The fitting results for the Einasto and DC14 profiles are presented in Li et al (2019). For completeness and comparison, we also include those fits in this paper.…”

Section: Dark Matter Halo Profilesmentioning

confidence: 99%

“…To fit these halo profiles, we map the posterior distributions of these fitting parameters using the open python package emcee (Foreman-Mackey et al 2013). As in Li et al (2019), we impose lognormal priors on Υ around their fiducial values (Υ disk =0.5 and Υ bul =0.7 according to McGaugh et al 2016;Lelli et al 2017) with a standard deviation of 0.1 dex suggested by stellar population synthesis models (e.g., see Bell & de Jong 2001;Portinari et al 2004;Meidt et al 2014;Schombert et al 2019), and Gaussian priors on D and i around their mean values as tabulated in the SPARC database with standard deviations given by their uncertainties.…”

Section: Mcmc Simulationsmentioning

confidence: 99%

“…The final posterior probability is proportional to the product of the likelihood function and priors according to Bayes theorem. We use the standard affine-invariant ensemble sampler in emcee as in Li et al (2019). We initialize the MCMC chains with 200 random walkers and the size of stretchmove a = 2.…”

Section: Mcmc Simulationsmentioning

confidence: 99%

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A Comprehensive Catalog of Dark Matter Halo Models for SPARC Galaxies

Lelli

McGaugh

et al. 2020

ApJS

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114

134

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We present rotation curve fits to 175 late-type galaxies from the Spitzer Photometry & Accurate Rotation Curves (SPARC) database using seven dark matter (DM) halo profiles: pseudo-isothermal (pISO), Burkert, Navarro-Frenk-White (NFW), Einasto, Di Cintio et al. (2014, DC14), coreNFW, and a new semi-empirical profile named Lucky13. We marginalize over stellar mass-to-light ratio, galaxy distance, disk inclination, halo concentration and halo mass (and an additional shape parameter for Einasto) using a Markov Chain Monte Carlo method. We find that cored halo models such as the DC14 and Burkert profiles generally provide better fits to rotation curves than the cuspy NFW profile. The stellar mass-halo mass relation from abundance matching is recovered by all halo profiles once imposed as a Bayesian prior, whereas the halo mass-concentration relation is not reproduced in detail by any halo model. We provide an extensive set of figures as well as best-fit parameters in machine-readable tables to facilitate model comparison and the exploration of DM halo properties.

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Section: Dark Matter Halo Profilesmentioning

confidence: 99%

Section: Dark Matter Halo Profilesmentioning

confidence: 99%

Section: Mcmc Simulationsmentioning

confidence: 99%

Section: Mcmc Simulationsmentioning

confidence: 99%

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A Comprehensive Catalog of Dark Matter Halo Models for SPARC Galaxies

Lelli

McGaugh

et al. 2020

ApJS

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114

134

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“…On the other hand, in the standard approach one selects a particular density profile or model of rotation curves, with a specific functional form between dependent and independent variables and then constrain the parameters of the chosen theory employing bayesian methods together observational data of galaxies (Bernal et al 2018;Garcia-Aspeitia et al 2017;Li et al 2019). The main problem of this method is the susceptibility to bias if the data is not well-represented by the assumed parametric model.…”

Section: Introductionmentioning

confidence: 99%

Galaxy rotation curves using a non-parametric regression method: core, cuspy and fuzzy scalar field dark matter models

Fernandez-Hernandez

Montiel

Rodríguez‐Meza

2019

Monthly Notices of the Royal Astronomical Society

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We present a non-parametric reconstruction of the rotation curves (RC) for 88 spiral galaxies under the LOESS+SIMEX technique. In order to compare methods we also perform the parametric approach assuming core and cuspy dark matter (DM) profiles: PISO, NFW, Burkert, Spano, the soliton and two fuzzy soliton+NFW. As result of this two approaches, a comparison of the RC obtained is carried out by computing the distance between central curves and the distance between 1σ error bands. Furthermore, we perform a model selection according to two statistical criteria, the BIC and the value of χ 2 r ed . We work with two groups. The first one is a comparison between PISO, NFW, Spano and Burkert showing that Spano is the most favored model satisfying our selection criteria. For the second group we select soliton, NFW and Fuzzy models, resulting the soliton as the best model. Moreover according to the statistical tools and non-parametric reconstruction we are able to classify galaxies as core or cusp. Finally, using an MCMC method, we compute for each of the DM models the characteristic surface density, µ D M = ρ s r s , and the mass within 300 pc. We found that there is a common mass for spiral galaxies of the order of 10 7 M , which is in agreement with results for dSph Milky Way satellites, independent of the model. This result is also consistent with our finding that there is a constant characteristic volume density of haloes. Finally, we also find that µ D M is not constant, which is in tension with previous literature.

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On the absence of a universal surface density, and a maximum Newtonian acceleration in dark matter haloes: Consequences for MOND

Zhou

Popolo

Chang

2020

Physics of the Dark Universe

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We study the dark matter (DM) surface density using the SPARC sample and compare it to Donato et al. [1] result. By means of MCMC method, we infer the best-fitting parameters for each galaxy. We reobtain the scaling relation between the surface density and luminosity, and several other scaling laws relating the dark matter halo properties to that of the galactic disc properties. We conclude, in contrast with Donato et al. [1], that the dark matter surface density is not a universal (constant) quantity but correlates with the luminosity as well as with other galactic disc properties. A derived posterior probability distribution of ρ 0 r 0 shows that the null hypothesis of constancy is rejected at a very high confidence level. These results leave little room for the claimed universality of dark matter surface density. Since MOND has strong prediction on the surface density [2], we compared our result with those predictions, finding that MOND predictions are violated by data. To strengthen the previous result, we compared our results to another prediction of MOND [3], the existence of a maximum Newtonian dark matter acceleration in the halo. Also in this case, MOND predictions are in contradiction with data. The dark matter Newtonian acceleration correlates with all the previously presented galactic disc properties, and data are distributed outside the bound predicted by Milgrom & Sanders[3]. We also find that the null hypothesis (constancy of DM Newtonian acceleration) is rejected at a very high confidence level.

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A constant characteristic volume density of dark matter haloes from SPARC rotation curve fits

Cited by 46 publications

References 46 publications

A Comprehensive Catalog of Dark Matter Halo Models for SPARC Galaxies

A Comprehensive Catalog of Dark Matter Halo Models for SPARC Galaxies

Galaxy rotation curves using a non-parametric regression method: core, cuspy and fuzzy scalar field dark matter models

On the absence of a universal surface density, and a maximum Newtonian acceleration in dark matter haloes: Consequences for MOND

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