The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Gravity and large-scale nonlocal bias
Open accessAbstract
For Gaussian primordial fluctuations the relationship between galaxy and matter overdensities, bias, is most often assumed to be local at the time of observation in the large-scale limit. This hypothesis is however unstable under time evolution, we provide proofs under several (increasingly more realistic) sets of assumptions. In the simplest toy model galaxies are created locally and linearly biased at a single formation time, and subsequently move with the dark matter (no velocity bias) conserving their comoving number density (no merging). We show that, after this formation time, the bias becomes unavoidably nonlocal and nonlinear at large scales. We identify the nonlocal gravitationally induced fields in which the galaxy overdensity can be expanded, showing that they can be constructed out of the invariants of the deformation tensor (Galileons), the main signature of which is a quadrupole field in second-order perturbation theory. In addition, we show that this result persists if we include an arbitrary evolution of the comoving number density of tracers. We then include velocity bias, and show that new contributions appear; these are related to the breaking of Galilean invariance of the bias relation, a dipole field being the signature at second order. We test these predictions by studying the dependence of halo overdensities in cells of fixed dark matter density: measurements in simulations show that departures from the mean bias relation are strongly correlated with the nonlocal gravitationally induced fields identified by our formalism, suggesting that the halo distribution at the present time is indeed more closely related to the mass distribution at an earlier rather than present time. However, the nonlocality seen in the simulations is not fully captured by assuming local bias in Lagrangian space. The effects on nonlocal bias seen in the simulations are most important for the most biased halos, as expected from our predictions. Accounting for these effects when modeling galaxy bias is essential for correctly describing the dependence on triangle shape of the galaxy bispectrum, and hence constraining cosmological parameters and primordial non-Gaussianity. We show that using our formalism we remove an important systematic in the determination of bias parameters from the galaxy bispectrum, particularly for luminous galaxies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The cosmological simulation code gadget-2
Springel, V. (2005) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2005.09655.x · Cited by 6,368
Galileon as a local modification of gravity
Nicolis, A., Rattazzi, R., & Trincherini, E. (2009) · Physical Review D · doi:10.1103/physrevd.79.064036 · Cited by 1,863
Cited by
Desjacques, V., Jeong, D., & Schmidt, F. (2018) · Physics Reports · doi:10.1016/j.physrep.2017.12.002 · Cited by 817
Heymans, C., Tröster, T., Asgari, M., Blake, C., Hildebrandt, H., Joachimi, B., Kuijken, K., Lin, C.-A., Sánchez, A. G., van den Busch, J. L., et al. (2021) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202039063 · Cited by 772
Hou, J., Sánchez, A. G., Ross, A. J., Smith, A., Neveux, R., Bautista, J., Burtin, E., Zhao, C., Scoccimarro, R., Dawson, K. S., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3234 · Cited by 328
Self-citationGil-Marín, H., Bautista, J. E., Paviot, R., Vargas-Magaña, M., de la Torre, S., Fromenteau, S., Alam, S., Ávila, S., Burtin, E., Chuang, C.-H., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2455 · Cited by 292
Abbott, T. M. C., Aguena, M., Alarcon, A., Alves, O., Amon, A., Andrade-Oliveira, F., Annis, J., Avila, S., Bacon, D., Baxter, E., et al. (2023) · Physical Review D · doi:10.1103/physrevd.107.083504 · Cited by 131
Metadata
- Authors
- Chan, K. C., Scoccimarro, R., & Sheth, R. K.
- Venue
- Physical Review D
- Publisher
- American Physical Society (APS)
- Published
- 2012-04-05
- Volume
- 85
- Issue
- 8
- ISSN
- 0556-2821 (ISSN-L)
- 1550-7998
- 1550-2368
- OpenAlex
- W2052394465
- Cited by
- 298
- References
- 2
- FWCI
- 12.40
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Gravity and large-scale nonlocal bias. https://app.cochat.ai/papers/10.1103/physrevd.85.083509
- The paper
- Chan, K. C., Scoccimarro, R., & Sheth, R. K. (2012). Gravity and large-scale nonlocal bias. Physical Review D. https://doi.org/10.1103/physrevd.85.083509
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.