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Journal articles on the topic 'Halo bias'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Assassi, Valentin, Daniel Baumann, Daniel Green, and Matias Zaldarriaga. "Renormalized halo bias." Journal of Cosmology and Astroparticle Physics 2014, no. 08 (August 27, 2014): 056. http://dx.doi.org/10.1088/1475-7516/2014/08/056.

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2

Mead, A. J., and L. Verde. "Including beyond-linear halo bias in halo models." Monthly Notices of the Royal Astronomical Society 503, no. 2 (March 22, 2021): 3095–111. http://dx.doi.org/10.1093/mnras/stab748.

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ABSTRACT We derive a simple prescription for including beyond-linear halo bias within the standard, analytical halo-model power spectrum calculation. This results in a corrective term that is added to the usual two-halo term. We measure this correction using data from N-body simulations and demonstrate that it can boost power in the two-halo term by a factor of ∼2 at scales $k\sim 0.7\, h\mathrm{Mpc}^{-1}$, with the exact magnitude of the boost determined by the specific pair of fields in the two-point function. How this translates to the full power spectrum depends on the relative strength of the one-halo term, which can mask the importance of this correction to a greater or lesser degree, again depending on the fields. Generally, we find that our correction is more important for signals that arise from lower mass haloes. When comparing our calculation to simulated data, we find that the underprediction of power in the transition region between the two- and one-halo terms, which typically plagues halo-model calculations, is almost completely eliminated when including the full non-linear halo bias. We show improved results for the autospectra and cross-spectra of galaxies, haloes, and matter. In the specific case of matter–matter or matter–halo power, we note that a large fraction of the improvement comes from the non-linear biasing between low- and high-mass haloes. We envisage our model being useful in the analytical modelling of cross-correlation signals. Our non-linear bias halo-model code is available at https://github.com/alexander-mead/BNL.

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Reid, Beth A., Licia Verde, Klaus Dolag, Sabino Matarrese, and Lauro Moscardini. "Non-Gaussian halo assembly bias." Journal of Cosmology and Astroparticle Physics 2010, no. 07 (July 9, 2010): 013. http://dx.doi.org/10.1088/1475-7516/2010/07/013.

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Ramakrishnan, Sujatha, Aseem Paranjape, and Ravi K. Sheth. "Mock halo catalogues: assigning unresolved halo properties using correlations with local halo environment." Monthly Notices of the Royal Astronomical Society 503, no. 2 (February 26, 2021): 2053–64. http://dx.doi.org/10.1093/mnras/stab541.

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ABSTRACT Large-scale sky surveys require companion large volume simulated mock catalogues. To ensure precision cosmology studies are unbiased, the correlations in these mocks between galaxy properties and their large-scale environments must be realistic. Since galaxies are embedded in dark matter haloes, an important first step is to include such correlations – sometimes called assembly bias – for dark matter haloes. However, galaxy properties correlate with smaller scale physics in haloes which large simulations struggle to resolve. We describe an algorithm that addresses and largely mitigates this problem. Our algorithm exploits the fact that halo assembly bias is unchanged as long as correlations between halo property c and the intermediate-scale tidal environment α are preserved. Therefore, knowledge of α is sufficient to assign small-scale, otherwise unresolved properties to a halo in a way that preserves its large-scale assembly bias accurately. We demonstrate this explicitly for halo internal properties like formation history (concentration c200b), shape c/a, dynamics cv/av, velocity anisotropy β, and angular momentum (spin λ). Our algorithm increases a simulation’s reach in halo mass and number density by an order of magnitude, with improvements in the bias signal as large as 45 per cent for 30-particle haloes, thus significantly reducing the cost of mocks for future weak lensing and redshift space distortion studies.

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Nishizawa, Atsushi J., Masahiro Takada, and Takahiro Nishimichi. "Perturbation theory for the non-linear halo power spectrum: the renormalized bias and halo bias." Monthly Notices of the Royal Astronomical Society 433, no. 1 (May 27, 2013): 209–20. http://dx.doi.org/10.1093/mnras/stt716.

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6

Verza, Giovanni, Carmelita Carbone, and Alessandro Renzi. "The Halo Bias inside Cosmic Voids." Astrophysical Journal Letters 940, no. 1 (November 1, 2022): L16. http://dx.doi.org/10.3847/2041-8213/ac9d98.

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Abstract The bias of dark matter halos and galaxies is a crucial quantity in many cosmological analyses. In this work, using large cosmological simulations, we explore the halo mass function and halo bias within cosmic voids. For the first time to date, we show that they are scale dependent along the void profile, and provide a predictive theoretical model of both the halo mass function and halo bias inside voids, recovering for the latter a 1% accuracy against simulated data. These findings may help shed light on the dynamics of halo formation within voids and improve the analysis of several void statistics from ongoing and upcoming galaxy surveys.

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7

García, Rafael, and Eduardo Rozo. "Halo exclusion criteria impacts halo statistics." Monthly Notices of the Royal Astronomical Society 489, no. 3 (September 5, 2019): 4170–75. http://dx.doi.org/10.1093/mnras/stz2458.

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ABSTRACT Every halo-finding algorithm must make a critical yet relatively arbitrary choice: it must decide which structures are parent haloes, and which structures are subhaloes of larger haloes. We refer to this choice as percolation. We demonstrate that the choice of percolation impacts the statistical properties of the resulting halo catalogue. Specifically, we modify the halo-finding algorithm rockstar to construct three different halo catalogues from the same simulation data, each with identical mass definitions, but different choice of percolation. The resulting haloes exhibit significant differences in both halo abundance and clustering properties. Differences in the halo mass function reach 6 per cent for haloes of mass $10^{13}\ h^{-1}\ {\rm {\rm M}_{\odot }}$, larger than the few per cent precision necessary for current cluster abundance experiments such as the Dark Energy Survey. Comparable differences are observed in the large-scale clustering bias, while differences in the halo–matter correlation function reach 30 per cent on translinear scales. These effects can bias weak-lensing estimates of cluster masses at a level comparable to the statistical precision of current state-of-the-art experiments.

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8

Biagetti, M., V. Desjacques, and A. Riotto. "Testing multifield inflation with halo bias." Monthly Notices of the Royal Astronomical Society 429, no. 2 (December 21, 2012): 1774–80. http://dx.doi.org/10.1093/mnras/sts467.

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9

Peacock, J. A., and R. E. Smith. "Halo occupation numbers and galaxy bias." Monthly Notices of the Royal Astronomical Society 318, no. 4 (November 11, 2000): 1144–56. http://dx.doi.org/10.1046/j.1365-8711.2000.03779.x.

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10

Yang, Xiaohu, Youcai Zhang, Tianhuan Lu, Huiyuan Wang, Feng Shi, Dylan Tweed, Shijie Li, Wentao Luo, Yi Lu, and Lei Yang. "Revealing the Cosmic Web-dependent Halo Bias." Astrophysical Journal 848, no. 1 (October 12, 2017): 60. http://dx.doi.org/10.3847/1538-4357/aa8c7a.

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11

Sunayama, Tomomi, Andrew P. Hearin, Nikhil Padmanabhan, and Alexie Leauthaud. "The scale-dependence of halo assembly bias." Monthly Notices of the Royal Astronomical Society 458, no. 2 (March 5, 2016): 1510–16. http://dx.doi.org/10.1093/mnras/stw332.

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12

Hoffmann, K., J. Bel, and E. Gaztañaga. "Comparing halo bias from abundance and clustering." Monthly Notices of the Royal Astronomical Society 450, no. 2 (April 28, 2015): 1674–92. http://dx.doi.org/10.1093/mnras/stv702.

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13

Li, Yun, H. J. Mo, and L. Gao. "On halo formation times and assembly bias." Monthly Notices of the Royal Astronomical Society 389, no. 3 (September 21, 2008): 1419–26. http://dx.doi.org/10.1111/j.1365-2966.2008.13667.x.

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14

Paranjape, Aseem, and Nikhil Padmanabhan. "Halo assembly bias from Separate Universe simulations." Monthly Notices of the Royal Astronomical Society 468, no. 3 (March 17, 2017): 2984–99. http://dx.doi.org/10.1093/mnras/stx659.

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15

Dalal, Neal, Martin White, J. Richard Bond, and Alexander Shirokov. "Halo Assembly Bias in Hierarchical Structure Formation." Astrophysical Journal 687, no. 1 (November 2008): 12–21. http://dx.doi.org/10.1086/591512.

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16

Schulz, A. E., and Martin White. "Scale-dependent bias and the halo model." Astroparticle Physics 25, no. 2 (March 2006): 172–77. http://dx.doi.org/10.1016/j.astropartphys.2005.11.007.

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17

Nufer, Gerd. "“Say hello to Halo”: the halo effect in sports." Innovative Marketing 15, no. 3 (September 30, 2019): 116–29. http://dx.doi.org/10.21511/im.15(3).2019.09.

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In daily life, people tend to use mental shortcuts to simplify and speed up their decision-making processes. A halo effect exists if the impression created by a dominant attribute influences how other attributes of an object or subject are judged. It involves a cognitive bias that leads to distorted assessments. However, the halo effect has barely been researched in a sports-related context, although it can substantially contribute to understanding how sport fans think and behave. The objective of this paper is to answer the question that is of interest for both theory and practice of sports marketing: Is there a halo effect in sports? Does the sporting success or failure of a professional soccer team radiate or even outshine other sports-related and non-sports aspects and influence or distort how the club is perceived by its fans? Fans of six soccer clubs selected from the first German soccer league Bundesliga were interviewed. This paper presents the results of an empirical study based on a data set consisting of a total of 4,180 cases. The results of the analyses substantiate the distortion of the fans’ perception with regard to a very diverse range of aspects that is triggered by the sporting success or failure of their favorite club.

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18

Paranjape, Aseem, Oliver Hahn, and Ravi K. Sheth. "Halo assembly bias and the tidal anisotropy of the local halo environment." Monthly Notices of the Royal Astronomical Society 476, no. 3 (February 23, 2018): 3631–47. http://dx.doi.org/10.1093/mnras/sty496.

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19

Lazeyras, Titouan, Alexandre Barreira, Fabian Schmidt, and Vincent Desjacques. "Assembly bias in the local PNG halo bias and its implication for f _NL constraints." Journal of Cosmology and Astroparticle Physics 2023, no. 01 (January 1, 2023): 023. http://dx.doi.org/10.1088/1475-7516/2023/01/023.

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Abstract We use N-body simulations to study halo assembly bias (i.e., the dependence of halo clustering on properties beyond total mass) in the density and primordial non-Gaussianity (PNG) linear bias parameters b 1 and b ϕ, respectively. We consider concentration, spin and sphericity as secondary halo properties, for which we find a clear detection of assembly bias for b 1 and b ϕ. At fixed total mass, halo spin and sphericity impact b 1 and b ϕ in a similar manner, roughly preserving the shape of the linear b ϕ(b 1) relation satisfied by the global halo population. Halo concentration, however, drives b 1 and b ϕ in opposite directions. This induces significant changes to the b ϕ(b 1) relation, with higher concentration halos having higher amplitude of b ϕ(b 1). For z = 0.5 and b 1 ≈ 2 in particular, the population comprising either all halos, those with the 33% lowest or those with the 33% highest concentrations have a PNG bias of b ϕ ≈ 3, b ϕ ≈ -1 and b ϕ ≈ 9, respectively. Varying the halo concentration can make b ϕ very small and even change its sign. These results have important ramifications for galaxy clustering constraints of the local PNG parameter fNL that assume fixed forms for the b ϕ(b 1) relation. We illustrate the significant impact of halo assembly bias in actual data using the BOSS DR12 galaxy power spectrum: assuming that BOSS galaxies are representative of all halos, the 33% lowest or the 33% highest concentration halos yields σ f NL = 44, 165, 19, respectively. Our results suggest taking host halo concentration into account in galaxy selection strategies to maximize the signal-to-noise on f NL. They also motivate more simulation-based efforts to study the b ϕ(b 1) relation of halos and galaxies.

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20

Xu, Xiaoju, Idit Zehavi, and Sergio Contreras. "Dissecting and modelling galaxy assembly bias." Monthly Notices of the Royal Astronomical Society 502, no. 3 (January 14, 2021): 3242–63. http://dx.doi.org/10.1093/mnras/stab100.

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ABSTRACT Understanding the galaxy-halo connection is fundamental for contemporary models of galaxy clustering. The extent to which the haloes’ assembly history and environment impact galaxy clustering (a.k.a. galaxy assembly bias; GAB), remains a complex and challenging problem. Using a semi-analytic galaxy formation model, we study the individual contributions of different secondary halo properties to the GAB signal. These are obtained by comparing the clustering of stellar-mass selected samples to that of shuffled samples where the galaxies are randomly reassigned to haloes of fixed mass and a specified secondary halo property. We explore a large range of internal halo properties and environmental measures. We find that commonly used properties like halo age or concentration amount to only 20–30 per cent of the signal, while the smoothed matter density or the tidal anisotropy can account for the full level of GAB (though care should be given to the specific definition). For the ‘successful’ measures, we examine the occupancy variations and the associated changes in the halo occupation function parameters. These are used to create mock catalogues that reproduce the full level of GAB. Finally, we propose a practical modification of the standard halo occupation distribution model, which can be tuned to any level of assembly bias. Fitting the parameters to our semi-analytic model, we demonstrate that the corresponding mock catalogue recovers the target level of GAB as well as the occupancy variations. Our results enable producing realistic mock catalogues and directly inform theoretical modelling of assembly bias and attempts to detect it in the Universe.

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21

Montero-Dorta, Antonio D., M. Celeste Artale, L. Raul Abramo, Beatriz Tucci, Nelson Padilla, Gabriela Sato-Polito, Ivan Lacerna, Facundo Rodriguez, and Raul E. Angulo. "The manifestation of secondary bias on the galaxy population from IllustrisTNG300." Monthly Notices of the Royal Astronomical Society 496, no. 2 (June 16, 2020): 1182–96. http://dx.doi.org/10.1093/mnras/staa1624.

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ABSTRACT We use the improved IllustrisTNG300 magnetohydrodynamical cosmological simulation to revisit the effect that secondary halo bias has on the clustering of the central galaxy population. With a side length of 205 h−1 Mpc and significant improvements on the subgrid model with respect to previous Illustris simulations, IllustrisTNG300 allows us to explore the dependencies of galaxy clustering over a large cosmological volume and halo mass range. We show at high statistical significance that the halo assembly bias signal (i.e. the secondary dependence of halo bias on halo formation redshift) manifests itself on the clustering of the galaxy population when this is split by stellar mass, colour, specific star formation rate, and surface density. A significant signal is also found for galaxy size: at fixed halo mass, larger galaxies are more tightly clustered than smaller galaxies. This effect, in contrast to the rest of the dependencies, seems to be uncorrelated with halo formation time, with some small correlation only detected for halo spin. We also explore the transmission of the spin bias signal, i.e. the secondary dependence of halo bias on halo spin. Although galaxy spin retains little information about the total halo spin, the correlation is enough to produce a significant galaxy spin bias signal. We discuss possible ways to probe this effect with observations.

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Xu, Xiaoju, Saurabh Kumar, Idit Zehavi, and Sergio Contreras. "Predicting halo occupation and galaxy assembly bias with machine learning." Monthly Notices of the Royal Astronomical Society 507, no. 4 (September 3, 2021): 4879–99. http://dx.doi.org/10.1093/mnras/stab2464.

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Abstract Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the halo occupations and recover galaxy clustering and assembly bias in a semi-analytic galaxy formation model. For stellar mass selected samples, we train a random forest algorithm on the number of central and satellite galaxies in each dark matter halo. With the predicted occupations, we create mock galaxy catalogues and measure the clustering and assembly bias. Using a range of halo and environment properties, we find that the machine learning predictions of the occupancy variations with secondary properties, galaxy clustering, and assembly bias are all in excellent agreement with those of our target galaxy formation model. Internal halo properties are most important for the central galaxies prediction, while environment plays a critical role for the satellites. Our machine learning models are all provided in a usable format. We demonstrate that machine learning is a powerful tool for modelling the galaxy–halo connection, and can be used to create realistic mock galaxy catalogues which accurately recover the expected occupancy variations, galaxy clustering, and galaxy assembly bias, imperative for cosmological analyses of upcoming surveys.

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Xu, Xiaoju, and Zheng Zheng. "Galaxy assembly bias of central galaxies in the Illustris simulation." Monthly Notices of the Royal Astronomical Society 492, no. 2 (January 11, 2020): 2739–54. http://dx.doi.org/10.1093/mnras/staa009.

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ABSTRACT Galaxy assembly bias, the correlation between galaxy properties and halo properties at fixed halo mass, could be an important ingredient in halo-based modelling of galaxy clustering. We investigate the central galaxy assembly bias by studying the relation between various galaxy and halo properties in the Illustris hydrodynamic galaxy formation simulation. Galaxy stellar mass M* is found to have a tighter correlation with peak maximum halo circular velocity Vpeak than with halo mass Mh. Once the correlation with Vpeak is accounted for, M* has nearly no dependence on any other halo assembly variables. The correlations between galaxy properties related to star formation history and halo assembly properties also show a cleaner form as a function of Vpeak than as a function of Mh, with the main correlation being with halo formation time and to a less extent halo concentration. Based on the galaxy–halo relation, we present a simple model to relate the bias factors of a central galaxy sample and the corresponding halo sample, both selected based on assembly-related properties. It is found that they are connected by the correlation coefficient of the galaxy and halo properties used to define the two samples, which provides a reasonable description for the samples in the simulation and suggests a simple prescription to incorporate galaxy assembly bias into the halo model. By applying the model to the local galaxy clustering measurements in Lin et al., we infer that the correlation between star formation history or specific star formation rate and halo formation time is consistent with being weak.

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Nasirudin, Ainulnabilah, Ilian T. Iliev, and Kyungjin Ahn. "Modelling the stochasticity of high-redshift halo bias." Monthly Notices of the Royal Astronomical Society 494, no. 3 (April 4, 2020): 3294–309. http://dx.doi.org/10.1093/mnras/staa853.

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ABSTRACT A very large dynamic range with simultaneous capture of both large and small scales in the simulations of cosmic structures is required for correct modelling of many cosmological phenomena, particularly at high redshift. This is not always available, or when it is, it makes such simulations very expensive. We present a novel subgrid method for modelling low-mass ($10^5\, {\rm M}_\odot \le M_{\rm halo}\le 10^9\, {\rm M}_\odot$) haloes, which are otherwise unresolved in large-volume cosmological simulations limited in numerical resolution. In addition to the deterministic halo bias that captures the average property, we model its stochasticity that is correlated in time. We find that the instantaneous binned distribution of the number of haloes is well approximated by a lognormal distribution, with overall amplitude modulated by this ‘temporal correlation bias’. The robustness of our new scheme is tested against various statistical measures, and we find that temporally correlated stochasticity generates mock halo data that is significantly more reliable than that from temporally uncorrelated stochasticity. Our method can be applied for simulating processes that depend on both the small- and large-scale structures, especially for those that are sensitive to the evolution history of structure formation such as the process of cosmic reionization. As a sample application, we generate a mock distribution of medium-mass (108 ≤ M/M⊙ ≤ 109) haloes inside a 500 Mpc $\, h^{-1}$, 3003 grid simulation box. This mock halo catalogue bears a reasonable statistical agreement with a halo catalogue from numerically resolved haloes in a smaller box, and therefore will allow a very self-consistent sets of cosmic reionization simulations in a box large enough to generate statistically reliable data.

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25

Abidi, Muntazir Mehdi, and Tobias Baldauf. "Cubic halo bias in Eulerian and Lagrangian space." Journal of Cosmology and Astroparticle Physics 2018, no. 07 (July 16, 2018): 029. http://dx.doi.org/10.1088/1475-7516/2018/07/029.

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26

Zhang, Pengjie. "A Possible Explanation of Vanishing Halo Velocity Bias." Astrophysical Journal 869, no. 1 (December 13, 2018): 74. http://dx.doi.org/10.3847/1538-4357/aaec72.

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27

Shi, Jingjing, and Ravi K. Sheth. "Dependence of halo bias on mass and environment." Monthly Notices of the Royal Astronomical Society 473, no. 2 (September 4, 2017): 2486–92. http://dx.doi.org/10.1093/mnras/stx2277.

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Obuljen, Andrej, Neal Dalal, and Will J. Percival. "Anisotropic halo assembly bias and redshift-space distortions." Journal of Cosmology and Astroparticle Physics 2019, no. 10 (October 8, 2019): 020. http://dx.doi.org/10.1088/1475-7516/2019/10/020.

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29

Seljak, U. "Redshift-space bias and from the halo model." Monthly Notices of the Royal Astronomical Society 325, no. 4 (August 21, 2001): 1359–64. http://dx.doi.org/10.1046/j.1365-8711.2001.04508.x.

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Lin, Yen-Ting, Rachel Mandelbaum, Yun-Hsin Huang, Hung-Jin Huang, Neal Dalal, Benedikt Diemer, Hung-Yu Jian, and Andrey Kravtsov. "ON DETECTING HALO ASSEMBLY BIAS WITH GALAXY POPULATIONS." Astrophysical Journal 819, no. 2 (March 7, 2016): 119. http://dx.doi.org/10.3847/0004-637x/819/2/119.

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Peacock, Brian. "Bias in Human Judgment: Is Your Halo Slipping?" Ergonomics in Design: The Quarterly of Human Factors Applications 10, no. 4 (October 2002): 4–31. http://dx.doi.org/10.1177/106480460201000402.

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Pollack, Jennifer E., Robert E. Smith, and Cristiano Porciani. "Modelling large-scale halo bias using the bispectrum." Monthly Notices of the Royal Astronomical Society 420, no. 4 (January 30, 2012): 3469–89. http://dx.doi.org/10.1111/j.1365-2966.2011.20279.x.

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Ariel Keselman, J., and A. Nusser. "Halo assembly bias in the quasi-linear regime." Monthly Notices of the Royal Astronomical Society 382, no. 4 (December 21, 2007): 1853–58. http://dx.doi.org/10.1111/j.1365-2966.2007.12495.x.

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Lacerna, Ivan, and Nelson Padilla. "The nature of assembly bias - II. Halo spin." Monthly Notices of the Royal Astronomical Society: Letters 426, no. 1 (August 21, 2012): L26—L30. http://dx.doi.org/10.1111/j.1745-3933.2012.01316.x.

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Carbone, Carmelita, Licia Verde, and Sabino Matarrese. "Non-Gaussian Halo Bias and Future Galaxy Surveys." Astrophysical Journal 684, no. 1 (August 13, 2008): L1—L4. http://dx.doi.org/10.1086/592020.

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Abramo, L. Raul, Irène Balmès, Fabien Lacasa, and Marcos Lima. "Scaling of the 1-halo terms with bias." Monthly Notices of the Royal Astronomical Society 454, no. 3 (October 14, 2015): 2844–54. http://dx.doi.org/10.1093/mnras/stv2193.

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Carbone, Carmelita, Olga Mena, and Licia Verde. "Cosmological parameters degeneracies and non-Gaussian halo bias." Journal of Cosmology and Astroparticle Physics 2010, no. 07 (July 19, 2010): 020. http://dx.doi.org/10.1088/1475-7516/2010/07/020.

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Mansfield, Philip, and Andrey V. Kravtsov. "The three causes of low-mass assembly bias." Monthly Notices of the Royal Astronomical Society 493, no. 4 (February 14, 2020): 4763–82. http://dx.doi.org/10.1093/mnras/staa430.

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ABSTRACT We present a detailed analysis of the physical processes that cause halo assembly bias – the dependence of halo clustering on proxies of halo formation time. We focus on the origin of assembly bias in the mass range corresponding to the hosts of typical galaxies and use halo concentration as our chief proxy of halo formation time. We also repeat our key analyses across a broad range of halo masses and for alternative formation time definitions. We show that splashback subhaloes are responsible for two-thirds of the assembly bias signal, but do not account for the entire effect. After splashback subhaloes have been removed, we find that the remaining assembly bias signal is due to a relatively small fraction ($\lesssim \!10{{\ \rm per\ cent}}$) of haloes in dense regions. We test a number of additional physical processes thought to contribute to assembly bias and demonstrate that the two key processes are the slowing of mass growth by large-scale tidal fields and by the high velocities of ambient matter in sheets and filaments. We also rule out several other proposed physical causes of halo assembly bias. Based on our results, we argue that there are three processes that modify the assembly bias of small-mass haloes arising from the properties of the primordial Gaussian field: large-scale tidal fields, gravitational heating due to the collapse of large-scale structures, and splashback subhaloes located outside the virial radius.

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Paranjape, Aseem, Emiliano Sefusatti, Kwan Chuen Chan, Vincent Desjacques, Pierluigi Monaco, and Ravi K. Sheth. "Bias deconstructed: unravelling the scale dependence of halo bias using real-space measurements." Monthly Notices of the Royal Astronomical Society 436, no. 1 (September 18, 2013): 449–59. http://dx.doi.org/10.1093/mnras/stt1578.

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Barreira, Alexandre, Baojiu Li, Carlton M. Baugh, and Silvia Pascoli. "Spherical collapse in Galileon gravity: fifth force solutions, halo mass function and halo bias." Journal of Cosmology and Astroparticle Physics 2013, no. 11 (November 27, 2013): 056. http://dx.doi.org/10.1088/1475-7516/2013/11/056.

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41

Darwis, Herwan, Suwito Suwito, and Zainuddin Jhay. "Testing of behavior bias." International Journal of Research in Business and Social Science (2147- 4478) 10, no. 8 (January 1, 2022): 275–83. http://dx.doi.org/10.20525/ijrbs.v10i8.1482.

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This study aims to (i) test the behavior bias of gamblers fallacy occurs at the time of uptrend and downtrend conditions; (ii) test the behavior bias of halo effect occurs at the time of uptrend and downtrend conditions; and (iii) test the behavior bias of familiarity effect occurs at the time of uptrend and downtrend conditions. The number of samples in the study was as many as 41 people. The test equipment used is One-Sample t-Test and Paired t-Test by using statistical package for social scientists (SPSS) as a static test tool. The results of this study show that: (i) Gamblers' fallacy that occurs when the uptrend condition is greater than when the condition is downtrend; (ii) Halo effect that occurs when the uptrend condition is greater than when the downtrend condition; (iii) Familiarity effect that occurs when the uptrend condition is greater than when the downtrend condition.

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Wu, Xiaohan, Julian B. Muñoz, and Daniel Eisenstein. "A fully Lagrangian, non-parametric bias model for dark matter halos." Journal of Cosmology and Astroparticle Physics 2022, no. 02 (February 1, 2022): 002. http://dx.doi.org/10.1088/1475-7516/2022/02/002.

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Abstract We present a non-parametric Lagrangian biasing model and fit the ratio of the halo and mass densities at the field level using the mass-weighted halo field in the AbacusSummit simulations at z=0.5. Unlike the perturbative halo bias model that has been widely used in interpreting the observed large-scale structure traced by galaxies, we find a non-negative halo-to-mass ratio that increases monotonically with the linear overdensity δ1 in the initial Lagrangian space. The bias expansion, however, does not guarantee non-negativity of the halo counts, and may lead to rising halo number counts at negative overdensities. The shape of the halo-to-mass ratio is unlikely to be described by a polynomial function of δ1 and other quantities. Especially for massive halos with 6×1012 h-1 M⊙, the halo-to-mass ratio starts soaring up at δ1>0, substantially different from the predictions of the bias expansion. We show that for the halo masses we consider (M>3×1011 h-1 M⊙) a non-parametric halo-to-mass ratio as a function of δ1 and its local derivative ∇^2δ1 can recover the halo power spectra to sub-percent level accuracy for wavenumbers k=0.01-0.1 h Mpc-1 given a proper smoothing scale to filter the initial density field, even though we do not fit the power spectrum directly. However, there is mild dependence of the recovery of the halo power spectrum on the smoothing scale and other input parameters. At k<0.01 h Mpc-1 and for massive halos with M>6×1012 h-1 M⊙, our non-parametric model leads to a few percent overestimation of the halo power spectrum, indicating the need for larger or multiple smoothing scales. The halo-to-mass ratios obtained qualitatively agree with intuitions from extended Press-Schechter theory. We compare our framework to the bias expansion and discuss possible extensions.

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Werner, Kim F., and Cristiano Porciani. "Renormalization of linear halo bias in N-body simulations." Monthly Notices of the Royal Astronomical Society 492, no. 2 (December 11, 2019): 1614–33. http://dx.doi.org/10.1093/mnras/stz3469.

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ABSTRACT The interpretation of redshift surveys requires modelling the relationship between large-scale fluctuations in the observed number density of tracers, δh, and the underlying matter density, δ. Bias models often express δh as a truncated series of integro-differential operators acting on δ, each weighted by a bias parameter. Due to the presence of ‘composite operators’ (obtained by multiplying fields evaluated at the same spatial location), the linear bias parameter measured from clustering statistics does not coincide with that appearing in the bias expansion. This issue can be cured by re-writing the expansion in terms of ‘renormalized’ operators. After providing a pedagogical and comprehensive review of bias renormalization in perturbation theory, we generalize the concept to non-perturbative dynamics and successfully apply it to dark-matter haloes extracted from a large suite of N-body simulations. When comparing numerical and perturbative results, we highlight the effect of the window function employed to smooth the random fields. We then measure the bias parameters as a function of halo mass by fitting a non-perturbative bias model (both before and after applying renormalization) to the cross spectrum $P_{\delta _\mathrm{h}\delta }(k)$. Finally, we employ Bayesian model selection to determine the optimal operator set to describe $P_{\delta _\mathrm{h}\delta }(k)$ for $k\lt 0.2\, h$ Mpc−1 at redshift z = 0. We find that it includes δ, ∇2δ, δ2 and the square of the traceless tidal tensor, s2. Considering higher order terms (in δ) leads to overfitting as they cannot be precisely constrained by our data. We also notice that next-to-leading-order perturbative solutions are inaccurate for k ≳ 0.1 h Mpc−1.

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Montero-Dorta, Antonio D., M. Celeste Artale, L. Raul Abramo, and Beatriz Tucci. "On the kinetic Sunyaev–Zel’dovich effect as an observational probe for halo spin bias." Monthly Notices of the Royal Astronomical Society 504, no. 3 (April 19, 2021): 4568–82. http://dx.doi.org/10.1093/mnras/stab1026.

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ABSTRACT We explore the potential of the kinetic Sunyaev–Zel’dovich (kSZ) effect as the cornerstone of a future observational probe for halo spin bias, the secondary dependence of halo clustering on halo spin at fixed halo mass. Using the IllustrisTNG magnetohydrodynamical cosmological simulation, we measure both the rotational component of the kSZ and the thermal SZ (tSZ) effects produced by the baryonic content of more than 50 000 haloes within the halo mass range $11 \lt \log _{10} ({\rm M_{vir}}/ h^{-1} \, {\rm M_{\odot }}) \lesssim 14.5$. First, we confirm that the magnitude of both effects depends strongly on the total gas and virial mass of the haloes, and that the integrated kSZ signal displays a significant correlation with the angular momentum of the intra-halo gas, particularly for massive haloes. Second, we show that both the integrated kSZ signal and the ratio of the integrated kSZ and tSZ signals trace total halo spin, even though significant scatter exists. Finally, we demonstrate, with high statistical significance, that, in the absence of observational and instrumental uncertainties, these SZ-related statistics can be used to recover most of the underlying IllustrisTNG halo spin bias signal. Our analysis represents the first attempt to develop a future observational probe for halo spin bias, bringing forward alternative routes for measuring the secondary bias effects.

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Ingoglia, Lorenzo, Giovanni Covone, Mauro Sereno, Carlo Giocoli, Sandro Bardelli, Fabio Bellagamba, Gianluca Castignani, et al. "AMICO galaxy clusters in KiDS-DR3: measurement of the halo bias and power spectrum normalization from a stacked weak lensing analysis." Monthly Notices of the Royal Astronomical Society 511, no. 1 (January 12, 2022): 1484–501. http://dx.doi.org/10.1093/mnras/stac046.

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ABSTRACT Galaxy clusters are biased tracers of the underlying matter density field. At very large radii beyond about 10 Mpc h−1, the shear profile shows evidence of a second-halo term. This is related to the correlated matter distribution around galaxy clusters and proportional to the so-called halo bias. We present an observational analysis of the halo bias–mass relation based on the AMICO galaxy cluster catalogue, comprising around 7000 candidates detected in the third release of the KiDS survey. We split the cluster sample into 14 redshift-richness bins and derive the halo bias and the virial mass in each bin by means of a stacked weak lensing analysis. The observed halo bias–mass relation and the theoretical predictions based on the Lambda cold dark matter standard cosmological model show an agreement within 2σ. The mean measurements of bias and mass over the full catalogue give $M_{200c} = (4.9 \pm 0.3) \times 10^{13}\, {\rm M}_{\odot }/{\it h}$ and $b_h \sigma _8^2 = 1.2 \pm 0.1$. With the additional prior of a bias–mass relation from numerical simulations, we constrain the normalization of the power spectrum with a fixed matter density Ωm = 0.3, finding σ8 = 0.63 ± 0.10.

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Zu, Ying, Rachel Mandelbaum, Melanie Simet, Eduardo Rozo, and Eli S. Rykoff. "On the level of cluster assembly bias in SDSS." Monthly Notices of the Royal Astronomical Society 470, no. 1 (May 23, 2017): 551–60. http://dx.doi.org/10.1093/mnras/stx1264.

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Abstract Recently, several studies have discovered a strong discrepancy between the large-scale clustering biases of two subsamples of galaxy clusters at the same halo mass, split by their average projected membership distances 〈Rmem〉. The level of this discrepancy significantly exceeds the maximum halo assembly bias predicted by Λ cold dark matter (ΛCDM). We explore whether some of the large-scale bias differences could be caused by projection effects in 〈Rmem〉 due to other systems along the line of sight. We thoroughly investigate the assembly bias of the redMaPPer clusters in Sloan Digital Sky Survey (SDSS), by defining a new variant of the average membership distance estimator $\tilde{R}_{\mathrm{mem}}$ that is robust against projection effects in the cluster membership identification. Using the angular mark correlation functions, we show that the large-scale bias differences when splitting by 〈Rmem〉 can be mostly attributed to projection effects. After splitting by $\tilde{R}_{\mathrm{mem}}$, the anomalously large signal is reduced, giving a ratio of 1.02 ± 0.14 between the two clustering biases as measured from weak lensing. Using a realistic mock cluster catalogue, we predict that the bias ratio between two $\tilde{R}_{\mathrm{mem}}$-split subsamples should be ≃1.10, which is >60 per cent weaker than the maximum halo assembly bias (1.24) when split by halo concentration. Therefore, our results demonstrate that the level of halo assembly bias exhibited by clusters in SDSS is consistent with the ΛCDM prediction. With a 10-fold increase in cluster numbers, deeper ongoing surveys will enable a more robust detection of halo assembly bias. Our findings also have important implications for quantifying the impact of projection effects on cosmological constraints using photometrically selected clusters.

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Yuan, Sihan, Boryana Hadzhiyska, Sownak Bose, Daniel J. Eisenstein, and Hong Guo. "Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function." Monthly Notices of the Royal Astronomical Society 502, no. 3 (January 28, 2021): 3582–98. http://dx.doi.org/10.1093/mnras/stab235.

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ABSTRACT Building accurate and flexible galaxy–halo connection models is crucial in modelling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by itself cannot capture the full galaxy assembly bias effect and that the local environment of the halo can be an excellent indicator of galaxy assembly bias. In this paper, we propose an extended halo occupation distribution (HOD) model that includes both a concentration-based assembly bias term and an environment-based assembly bias term. We use this model to achieve a good fit (χ2/degrees of freedom = 1.35) on the 2D redshift-space two-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample. We find that the inclusion of both assembly bias terms is strongly favoured by the data and the standard five-parameter HOD model is strongly rejected. More interestingly, the redshift-space 2PCF drives the assembly bias parameters in a way that preferentially assigns galaxies to lower mass haloes. This results in galaxy–galaxy lensing predictions that are within 1σ agreement with the observation, alleviating the perceived tension between galaxy clustering and lensing. We also showcase a consistent 3σ–5σ preference for a positive environment-based assembly bias that persists over variations in the fit. We speculate that the environmental dependence might be driven by underlying processes such as mergers and feedback, but might also be indicative of a larger halo boundaries such as the splashback radius. Regardless, this work highlights the importance of building flexible galaxy–halo connection models and demonstrates the extra constraining power of the redshift-space 2PCF.

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Contreras, S., J. Chaves-Montero, M. Zennaro, and R. E. Angulo. "The cosmological dependence of halo and galaxy assembly bias." Monthly Notices of the Royal Astronomical Society 507, no. 3 (August 23, 2021): 3412–22. http://dx.doi.org/10.1093/mnras/stab2367.

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ABSTRACT One of the main predictions of excursion set theory is that the clustering of dark matter haloes only depends on halo mass. However, it has been long established that the clustering of haloes also depends on other properties, including formation time, concentration, and spin; this effect is commonly known as halo assembly bias (HAB). We use a suite of gravity-only simulations to study the dependence of HAB on cosmology; these simulations cover cosmological parameters spanning 10σ around state-of-the-art best-fitting values, including standard extensions of the ΛCDM paradigm such as neutrino mass and dynamical dark energy. We find that, when studying the peak height-bias relation, the strength of HAB presents variations smaller than 0.05 dex across all cosmologies studied for concentration- and spin-selected haloes, letting us conclude that the dependence of HAB upon cosmology is negligible. We then study the dependence of galaxy assembly bias (i.e. the manifestation of HAB in galaxy clustering) on cosmology using subhalo abundance matching. We find that galaxy assembly bias also presents very small dependence upon cosmology (∼ 2 per cent–4 per cent of the total clustering); on the other hand, we find that the dependence of this signal on the galaxy formation parameters of our galaxy model is much stronger. Taken together, these results let us conclude that the dependence of halo and galaxy assembly bias on cosmology is practically negligible.

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Lazeyras, Titouan, Francisco Villaescusa-Navarro, and Matteo Viel. "The impact of massive neutrinos on halo assembly bias." Journal of Cosmology and Astroparticle Physics 2021, no. 03 (March 9, 2021): 022. http://dx.doi.org/10.1088/1475-7516/2021/03/022.

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Dizgah, Azadeh Moradinezhad, Kwan Chuen Chan, Jorge Noreña, Matteo Biagetti, and Vincent Desjacques. "Squeezing the halo bispectrum: a test of bias models." Journal of Cosmology and Astroparticle Physics 2016, no. 09 (September 19, 2016): 030. http://dx.doi.org/10.1088/1475-7516/2016/09/030.

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