30 pages, 11 figures, submitted to Phys. Rev. D. Code available at this https URL
Parity-violating physics in the early Universe can leave detectable traces in late-time observables. Whilst vector- and tensor-type parity-violation can be observed in the $B$-modes of the cosmic microwave background, scalar-type signatures are visible only in the four-point correlation function (4PCF) and beyond. This work presents a blind test for parity-violation in the 4PCF of the BOSS CMASS sample, considering galaxy separations in the range $[20,160]h^{-1}\mathrm{Mpc}$. The parity-odd 4PCF contains no contributions from standard $\Lambda$CDM physics and can be efficiently measured using recently developed estimators. Data are analyzed using both a non-parametric rank test (comparing the BOSS 4PCFs to those of realistic simulations) and a compressed $\chi^2$ analysis, with the former avoiding the assumption of a Gaussian likelihood. These find similar results, with the rank test giving a detection probability of $99.6\%$ ($2.9\sigma$). This provides significant evidence for parity-violation, either from cosmological sources or systematics. We perform a number of systematic tests: although these do not reveal any observational artefacts, we cannot exclude the possibility that our detection is caused by the simulations not faithfully representing the statistical properties of the BOSS data. Our measurements can be used to constrain physical models of parity-violation. As an example, we consider a coupling between the inflaton and a $U(1)$ gauge field and place bounds on the latter's energy density, which are several orders of magnitude stronger than those previously reported. Upcoming probes such as DESI and Euclid will reveal whether our detection of parity-violation is due to new physics, and strengthen the bounds on a variety of models.
13 pages, 8 figures
We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approximating the Green's function expansion that relates the initial conditions of the simulations to its outcome at later times in the deeply nonlinear regime. We test the accuracy of this approximation by assessing its performance on well understood simple cases that have either known exact solutions or well understood expansions. These scenarios include spherical configurations, isolated plane waves, and two interacting plane waves: initial conditions that are very different from the Gaussian random fields used for training. We find our model generalizes well to these well understood scenarios, demonstrating that the networks have inferred general physical principles and learned the nonlinear mode couplings from the complex, random Gaussian training data. These tests also provide a useful diagnostic for finding the model's strengths and weaknesses, and identifying strategies for model improvement. We also test the model on initial conditions that contain only transverse modes, a family of modes that differ not only in their phases but also in their evolution from the longitudinal growing modes used in the training set. When the network encounters these initial conditions that are orthogonal to the training set, the model fails completely. In addition to these simple configurations, we evaluate the model's predictions for the density, displacement, and momentum power spectra with standard initial conditions for N-body simulations. We compare these summary statistics against N-body results and an approximate, fast simulation method called COLA. Our model achieves percent level accuracy at nonlinear scales of $k\sim 1\ \mathrm{Mpc}^{-1}\, h$, representing a significant improvement over COLA.
11 pages, 4 figures
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to output the nonlinear displacements and velocities of N-body simulation particles based on their linear inputs. Cosmology dependence is encoded in the form of style parameters at each layer of the neural network, enabling the emulator to effectively interpolate the outcomes of structure formation between different flat $\Lambda$CDM cosmologies over a wide range of background matter densities. The neural network architecture makes the model differentiable by construction, providing a powerful tool for fast field level inference. We test the accuracy of our method by considering several summary statistics, including the density power spectrum with and without redshift space distortions, the displacement power spectrum, the momentum power spectrum, the density bispectrum, halo abundances, and halo profiles with and without redshift space distortions. We compare these statistics from our emulator with the full N-body results, the COLA method, and a fiducial neural network with no cosmological dependence. We find our emulator gives accurate results down to scales of $k \sim 1\ \mathrm{Mpc}^{-1}\, h$, representing a considerable improvement over both COLA and the fiducial neural network. We also demonstrate that our emulator generalizes well to initial conditions containing primordial non-Gaussianity, without the need for any additional style parameters or retraining.
11 page, 7 figures. Intended as a living, easy-to-find document. No intention of submitting this to a journal
15 pages, submitted to MNRAS, comments welcome
18 pages, 15 figures, submitted to MNRAS
Source code for compiling the paper is publicly hosted on github: this https URL Data is available on zenodo: 10.5281/zenodo.6624911
5 pages, 1 figure
13 figures, 5 tables, submitted
Accepted for publication in MNRAS, 15 pages
13 pages, 8 figures. Will be submitted to AAS journals. Comments welcome
13 pages, 14 figures. Accepted for publication in MNRAS
13 pages, 7 figures, 2 tables
13 pages + appendices, 9 figures and 1 table. Submitted to MNRAS, comments welcome
18 pages, 4 figures, 6 tables, submitted to ApJ
15 pages, 6 figures. Submitted
Submitted to AJ. 22 pages, 9 figures, and 4 tables. Comments are welcome!
11 pages, 7 figures, Submitted to the AAS Journals
19 pages, 10 figures, 2 tables. Resubmitted to ApJ after first round of referee comments
15 pages, 10 figures
19 pages, 4 figures, 5 tables. Submitted, comments welcome!
6 pages, 3 figures
13 pages, 8 figures. Prepared for submission to MNRAS. Radial velocity prediction catalogues available at this https URL
12 pages, 13 figures, Accepted by ApJ
Accepted for publication in MNRAS. 14 pages, 8 figures
39 pages, 27 figures, 3 tables, accepted for publication in the Astrophysical Journal Supplement Series
11 pages. Accepted to ApJ
Accepted for publication in AAS journal
17 pages, 11 figures, regular article
12 pages. Submitted to PASA
22 pages, 18 figures, 4 tables, submitted to ApJ
25 pages, 40 figures, 4 tables. Accepted for publication in MNRAS
Accepted to ApJ. IR spectra available at this https URL
Accepted for publication in ApJ
13 pages, 12 figures, accepted for MNRAS
29 pages, 21 figures, accepted for publication in A&A
18 pages, 10 figures
10 pages, 7 figures, accepted for publication in MNRAS
15 pages, 14 figures, 3 appendices
18 pages, 13 figures, accepted in A&A. Watch a 3min summary by the first author in this https URL
12 Pages, 8 figures, Accepted for Publication in MNRAS
13 pages, 5 figures, 7 tables
9 pages, 5 figures, 1 table, accepted for publication in ApJL
22 pages, 10 figures; Accepted for publication in Research in Astronomy and Astrophysics
Paper I in a series of III, 16 pages, 15 figures, submitted to A&A
Paper II in a series of III, 21 pages, 16 figures, submitted to A&A
18 pages, 23 figures
5 pages, 4 figures, submitted to MNRAS
16 pages, 9 figures, 2 tables; submitted to ApJ
29 pages, 12 figures, RAA accepted. The source code can be downloaded from \url{ this https URL }
12 pages, 4 figures
Submitted to Astrophysical Journal, reviewed and resubmitted. 35 pages, 22 figures, 8 tables
18 pages, 4 figures, 4 tables; submitted to ApJ
8 pages, 4 figures, accepted for publication in MNRAS
40 pages, 6 figures, 18 tables, Accepted for publication in ApJ
Accepted for publication in Publications of the Astronomical Society of the Pacific (PASP)
10 pages, 10 figures
12 pages, 6 figures
18 pages, 6 figures, and 5 tables. Accepted for publication in MNRAS
19 pages, 18 figures, 2 tables, accepted into ApJ on 06/08/2022
8 pages, 5 figures, submitted to ApJ. Comments welcome
arXiv admin note: substantial text overlap with arXiv:2111.12512
Accepted for publication in MNRAS, 12 pages, 9 figures
15 pages, 5 figures. Mathematica notebook supplement is enclosed in the source file
The MNRAS way of doing a sideways table forces a new page but apparently this is a known behaviour
19 pages, 15 figures
11 pages, 1 figure