PRIMUS: Clustering of Star-forming and Quiescent Central Galaxies at 0.2 &lt; z &lt; 0.9

Berti, Angela M.; Coil, Alison L.; Hearin, Andrew P.; Moustakas, John

doi:10.3847/1538-4357/ab3b5d

Cited by 11 publications

(9 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(see e.g., Li et al 2006;Zehavi et al 2011;Coil et al 2017;Berti et al 2019Berti et al , 2021 and that more spheroid-like morphologies are more frequently in denser environments (e.g., Dressler 1980;Pearson et al 2021). Thus, a natural next step within the framework of would be to divide the reference sample by galaxy properties.…”

Section: Future Considerationsmentioning

confidence: 99%

Galaxy Correlation Function and Local Density from Photometric Redshifts Using the Stochastic Order Redshift Technique (SORT)

Kakos,

Primack,

Rodriguez-Puebla

et al. 2022

Preprint

View full text Add to dashboard Cite

The stochastic order redshift technique () is a simple, efficient, and robust method to improve cosmological redshift measurements. The method relies upon having a small (∼10 per cent) reference sample of high-quality redshifts. Within pencilbeam-like sub-volumes surrounding each galaxy, we use the precise d𝑁/d𝑧 distribution of the reference sample to recover new redshifts and assign them one-to-one to galaxies such that the original rank order of redshifts is preserved. Preserving the rank order is motivated by the fact that random variables drawn from Gaussian probability density functions with different means but equal standard deviations satisfy stochastic ordering. The process is repeated for sub-volumes surrounding each galaxy in the survey. This results in every galaxy with an uncertain redshift being assigned multiple 'recovered' redshifts from which a new redshift estimate can be determined. An earlier paper applied to a mock Sloan Digital Sky Survey at 𝑧 0.2 and accurately recovered the two-point correlation function on scales 4 ℎ −1 Mpc. In this paper, we test the performance of in surveys spanning the redshift range 0.75 < 𝑧 < 2.25. We used two mock surveys extracted from the Small MultiDark-Planck and Bolshoi-Planck 𝑁-body simulations with dark matter haloes that were populated by the Santa Cruz semi-analytic model. We find that is able to improve redshift estimates and recover distinctive large-scale features of the cosmic web. Further, it provides unbiased estimates of the redshift-space two-point correlation function 𝜉 (𝑠) on scales 2.5 ℎ −1 Mpc, as well as local densities in regions of average or higher density. This may allow improved understanding of how galaxy properties relate to their local environments.

show abstract

Section: Future Considerationsmentioning

confidence: 99%

Galaxy Correlation Function and Local Density from Photometric Redshifts Using the Stochastic Order Redshift Technique (SORT)

Kakos,

Primack,

Rodriguez-Puebla

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Comparing observations with simulations requires mock galaxy catalogs that have the same joint stellar mass and sSFR distributions, galaxy number density, and line-ofsight positional uncertainty (analogous to redshift error in observational data) as the dataset of interest. To achieve this we use a procedure similar to the one described in §3.1 of Berti et al (2019). Briefly, for each dataset we create a 2D kernel density estimate (KDE) of the joint distribution of galaxy stellar mass versus sSFR.…”

Section: Simulations and Mocksmentioning

confidence: 99%

“…The relative clustering strengths of galaxy samples within the same volume can have smaller uncertainty than the absolute biases because cosmic variance largely cancels out in relative bias measurements. Berti et al (2019) refer to the clustering strength dependence on sSFR within the star-forming or quiescent sequence as "intra-sequence relative bias" (ISRB). We adopt that term here to refer to the relative biases of our "main sequence split" galaxy samples in data and mocks.…”

Section: Relative Bias Of Galaxy Samples In Data Andmentioning

confidence: 99%

“…Using "isolated primary" galaxies in PRIMUS data as a proxy for centrals, Berti et al (2019) investigate the joint clustering dependence of central galaxies on stellar mass and sSFR at z ∼ 0.35 and z ∼ 0.7. They compare their results to mock galaxy catalogs based on the empirical UniverseMachine model of Behroozi et al (2019) and find that C17's results for all galaxies also hold for centrals: quiescent central galaxies are significantly more clustered than star-forming centrals at fixed stellar mass.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Main Sequence Scatter is Real: The Joint Dependence of Galaxy Clustering on Star Formation and Stellar Mass

Berti,

Coil,

Hearin

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

We present new measurements of the clustering of stellar mass-complete samples of ∼ 40, 000 SDSS galaxies at z ∼ 0.03 as a joint function of stellar mass and specific star formation rate (sSFR). Our results confirm what Coil et al. (2017) find at z ∼ 0.7: galaxy clustering is a stronger function of sSFR at fixed stellar mass than of stellar mass at fixed sSFR. We also find that galaxies above the star-forming main sequence (SFMS) with higher sSFR are less clustered than galaxies below the SFMS with lower sSFR, at a given stellar mass. A similar trend is present for quiescent galaxies. This confirms that main sequence scatter, and scatter within the quiescent sequence, is physically connected to the large-scale cosmic density field. We compare the resulting galaxy bias versus sSFR, and relative bias versus sSFR ratio, for different galaxy samples across 0 < z < 1.2 to mock galaxy catalogs based on the empirical galaxy evolution model of Behroozi et al. (2019). This model fits PRIMUS and DEEP2 clustering data well at intermediate redshift, but agreement with SDSS is not as strong. We show that increasing the correlation between galaxy SFR and halo accretion rate at z ∼ 0 in the model substantially improves agreement with SDSS data. Mock catalogs suggest that central galaxies contribute substantially to the dependence of clustering on sSFR at a given stellar mass and that the signal is not simply an effect of satellite galaxy fraction differences with sSFR. Our results are highly constraining for galaxy evolution models and show that the stellar-to-halo mass relation (SHMR) depends on sSFR.

show abstract

“…They have highlighted the need for a variety of physical quenching processes acting well beyond the cluster virial radii. Larger surveys have followed such as the CLASH-VLT survey (Biviano et al 2013), ORELSE (Lubin et al 2009), PRIMUS (Berti et al 2019), and the IMACS cluster building survey (Dressler et al 2013), improving sampling of datasets and analyses.…”

Section: Introductionmentioning

confidence: 99%

SEEDisCS I. Molecular gas in galaxy clusters and their large scale structure: the case of CL1411.1$-$1148 at $z\sim0.5$

Spérone-Longin,

Jablonka,

Combes

et al. 2020

Preprint

View full text Add to dashboard Cite

We investigate how the galaxy reservoirs of molecular gas fuelling star formation are transformed while the host galaxies infall onto galaxy cluster cores. As part of the Spatially Extended ESO Distant Cluster Survey (SEEDisCS), we present CO(3-2) observations of 27 star forming galaxies obtained with the Atacama Large Millimeter Array (ALMA). These sources are located inside and around CL1411.1−1148 at z = 0.5195, within 5 times the cluster virial radius. These targets were selected to have similar stellar masses (M star ), colours, and magnitudes as a field comparison sample at similar redshift drawn from the Plateau de Bure high-z Blue Sequence Survey (PHIBSS2). We compare the cold gas fraction (µ H 2 = M H 2 /M star ), specific star formation rates (SFR/M star ) and depletion timescales (t depl = M H 2 /SFR) of our main sequence galaxies to the PHIBSS2 sub-sample. While the majority of our galaxies, 63%, are consistent with PHIBSS2, the remainder falls below the relation between µ H 2 and M star of the PHIBSS2 galaxies at z ∼ 0.5. These low-µ H 2 galaxies are not compatible with the tail of a Gaussian distribution, hence they correspond to a new population of galaxies, with normal SFRs but low gas content and low depletion times 1 Gyr, that was absent from previous surveys. We suggest that the star formation activity of these galaxies has not yet been lowered by the status of their gas reservoir.

show abstract

PRIMUS: Clustering of Star-forming and Quiescent Central Galaxies at 0.2 < z < 0.9

Cited by 11 publications

References 92 publications

Galaxy Correlation Function and Local Density from Photometric Redshifts Using the Stochastic Order Redshift Technique (SORT)

Galaxy Correlation Function and Local Density from Photometric Redshifts Using the Stochastic Order Redshift Technique (SORT)

Main Sequence Scatter is Real: The Joint Dependence of Galaxy Clustering on Star Formation and Stellar Mass

SEEDisCS I. Molecular gas in galaxy clusters and their large scale structure: the case of CL1411.1$-$1148 at $z\sim0.5$

Contact Info

Product

Resources

About