The Central Dark Matter Fraction of Massive Early-Type Galaxies

Tortora, C.; Napolitano, N. R.

doi:10.3389/fspas.2021.704419

Cited by 7 publications

(2 citation statements)

References 126 publications

(285 reference statements)

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“…On the other hand, regular massive ETGs like NGC 1278 are effective at cannibalising small dark matter rich-galaxies, which impacts both R e and f DM (e.g. Hilz et al 2012;Tortora & Napolitano 2022). If we assume that the lack of an envelope in NGC 1277 is due to a dark matter deficiency, it must have either lost its dark matter early (before significant accretion occurred) or was dark matter deficient ab initio.…”

Section: The Dark Matter Fraction Within One and Five Effective Radii...mentioning

confidence: 99%

The massive relic galaxy NGC 1277 is dark matter deficient

Comerón

Trujillo

Cappellari

et al. 2023

A&A

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According to the Λ cold dark matter (ΛCDM) cosmology, present-day galaxies with stellar masses M⋆ > 1011 M⊙ should contain a sizable fraction of dark matter within their stellar body. Models indicate that in massive early-type galaxies (ETGs) with M⋆ ≈ 1.5 × 1011 M⊙, dark matter should account for ∼15% of the dynamical mass within one effective radius (1 Re) and for ∼60% within 5 Re. Most massive ETGs have been shaped through a two-phase process: the rapid growth of a compact core was followed by the accretion of an extended envelope through mergers. The exceedingly rare galaxies that have avoided the second phase, the so-called relic galaxies, are thought to be the frozen remains of the massive ETG population at z ≳ 2. The best relic galaxy candidate discovered to date is NGC 1277, in the Perseus cluster. We used deep integral field George and Cynthia Mitchel Spectrograph (GCMS) data to revisit NGC 1277 out to an unprecedented radius of 6 kpc (corresponding to 5 Re). By using Jeans anisotropic modelling, we find a negligible dark matter fraction within 5 Re (fDM(5 Re) < 0.05; two-sigma confidence level), which is in tension with the ΛCDM expectation. Since the lack of an extended envelope would reduce dynamical friction and prevent the accretion of an envelope, we propose that NGC 1277 lost its dark matter very early or that it was dark matter deficient ab initio. We discuss our discovery in the framework of recent proposals, suggesting that some relic galaxies may result from dark matter stripping as they fell in and interacted within galaxy clusters. Alternatively, NGC 1277 might have been born in a high-velocity collision of gas-rich proto-galactic fragments, where dark matter left behind a disc of dissipative baryons. We speculate that the relative velocities of ≈2000 km s−1 required for the latter process to happen were possible in the progenitors of the present-day rich galaxy clusters.

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Section: The Dark Matter Fraction Within One and Five Effective Radii...mentioning

confidence: 99%

The massive relic galaxy NGC 1277 is dark matter deficient

Comerón

Trujillo

Cappellari

et al. 2023

A&A

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“…This unprecedented amount of data will help us to get a deeper insight into cosmology and galaxy evolution. For instance, we will be able to gain a more detailed understanding of the dark matter distribution in the universe, constrain the equation of state of the dark energy with weak lensing (e.g., Laureijs et al 2011b;Hildebrandt et al 2017;Abbott et al 2018;Gong et al 2019;Joachimi et al 2021;Heymans et al 2021), study the mass-size relation of galaxies at higher redshift (z > 1.0), and explore the stellar and dark matter assembly in galaxies and clusters (e.g., Yang et al 2012;Moster et al 2013;Behroozi et al 2019, Tortora & Napolitano 2022 over enormous statistical samples.…”

Section: Introductionmentioning

confidence: 99%

Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry

Li¹,

Napolitano²,

Feng³

et al. 2022

Preprint

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Aims. In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only. Methods. As a first application of this tool, we estimate photo-z of a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on ∼ 140 000 galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG_AUTO< 21) and low redshift (z < 0.8) systems, however, we could use ∼ 6500 galaxies in the range 0.8 < z < 3 to effectively extend the training to higher redshift. The inputs are the r-band galaxy images plus the 9-band magnitudes and colours, from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey. Results. By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD=0.014 for lower redshift and NMAD=0.041 for higher redshift galaxies) and low fraction of outliers (0.4% for lower and 1.27% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a ∼ 10 − 35% improvement in precision at different redshifts and a ∼ 45% reduction in the fraction of outliers. We finally discuss that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-z outlier fraction of galaxies can be cut down to 0.3%.

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