We present methods for emulating the matter power spectrum which effectively combine information from cosmological $N$-body simulations at different resolutions. An emulator allows estimation of simulation output by interpolating across the parameter
space of a handful of simulations. We present the first implementation of multi-fidelity emulation in cosmology, where many low-resolution simulations are combined with a few high-resolution simulations to achieve an increased emulation accuracy. The power spectrums dependence on cosmology is learned from the low-resolution simulations, which are in turn calibrated using high-resolution simulations. We show that our multi-fidelity emulator can achieve percent-level accuracy on average with only $3$ high-fidelity simulations and outperforms a single-fidelity emulator that uses $11$ simulations. With a fixed number of high-fidelity training simulations, we show that our multi-fidelity emulator is $simeq 100$ times better than a single-fidelity emulator at $k leq 2 ,htextrm{Mpc}{^{-1}}$, and $simeq 20$ times better at $3 leq k < 6.4 ,htextrm{Mpc}{^{-1}}$. Multi-fidelity emulation is fast to train, using only a simple modification to standard Gaussian processes. Our proposed emulator shows a new way to predict non-linear scales by fusing simulations from different fidelities.
We present a new catalogue of Damped Lyman-$alpha$ absorbers from SDSS DR16Q, as well as new estimates of their statistical properties. Our estimates are computed with the Gaussian process models presented in Garnett et al. (2017); Ho et al. (2020) w
ith an improved model for marginalising uncertainty in the mean optical depth of each quasar. We compute the column density distribution function (CDDF) at $2 < z < 5$, the line density ($textrm{d} N/ textrm{d} X$), and the neutral hydrogen density ($Omega_{textrm{DLA}}$). Our Gaussian process model provides a posterior probability distribution of the number of DLAs per spectrum, thus allowing unbiased probabilistic predictions of the statistics of DLA populations even with the noisiest data. We measure a non-zero column density distribution function for $N_{textrm{HI}} < 3 times 10^{22} ,textrm{cm}^{-2}$ with $95%$ confidence limits, and $N_{textrm{HI}} lesssim 10^{22} ,textrm{cm}^{-2}$ for spectra with signal-to-noise ratios $> 4$. Our results for DLA line density and total hydrogen density are consistent with previous measurements. Despite a small bias due to the poorly measured blue edges of the spectra, we demonstrate that our new model can measure the DLA population statistics when the DLA is in the Lyman-$beta$ forest region. We verify our results are not sensitive to the signal-to-noise ratios and redshifts of the background quasars although a residual correlation remains for detections from $z_{textrm{QSO}} < 2.5$, indicating some residual systematics when applying our models on very short spectra, where the SDSS spectral observing window only covers part of the Lyman-$alpha$ forest.
We make forecasts for the impact a future midband space-based gravitational wave experiment, most sensitive to $10^{-2}- 10$ Hz, could have on potential detections of cosmological stochastic gravitational wave backgrounds (SGWBs). Specific proposed m
idband experiments considered are TianGo, B-DECIGO and AEDGE. We propose a combined power-law integrated sensitivity (CPLS) curve combining GW experiments over different frequency bands, which shows the midband improves sensitivity to SGWBs by up to two orders of magnitude at $10^{-2} - 10$ Hz. We consider GW emission from cosmic strings and phase transitions as benchmark examples of cosmological SGWBs. We explicitly model various astrophysical SGWB sources, most importantly from unresolved black hole mergers. Using Markov Chain Monte Carlo, we demonstrated that midband experiments can, when combined with LIGO A+ and LISA, significantly improve sensitivities to cosmological SGWBs and better separate them from astrophysical SGWBs. In particular, we forecast that a midband experiment improves sensitivity to cosmic string tension $Gmu$ by up to a factor of $10$, driven by improved component separation from astrophysical sources. For phase transitions, a midband experiment can detect signals peaking at $0.1 - 1$ Hz, which for our fiducial model corresponds to early Universe temperatures of $T_*sim 10^4 - 10^6$ GeV, generally beyond the reach of LIGO and LISA. The midband closes an energy gap and better captures characteristic spectral shape information. It thus substantially improves measurement of the properties of phase transitions at lower energies of $T_* sim O(10^3)$ GeV, potentially relevant to new physics at the electroweak scale, whereas in this energy range LISA alone will detect an excess but not effectively measure the phase transition parameters. Our modelling code and chains are publicly available.
We develop an automated technique to measure quasar redshifts in the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey (SDSS). Our technique is an extension of an earlier Gaussian process method for detecting damped Lyman
-alpha absorbers (DLAs) in quasar spectra with known redshifts. We apply this technique to a subsample of SDSS DR12 with BAL quasars removed and redshift larger than 2.15. We show that we are broadly competitive to existing quasar redshift estimators, disagreeing with the PCA redshift by more than 0.5 in only 0.38% of spectra. Our method produces a probabilistic density function for the quasar redshift, allowing quasar redshift uncertainty to be propagated to downstream users. We apply this method to detecting DLAs, accounting in a Bayesian fashion for redshift uncertainty. Compared to our earlier method with a known quasar redshift, we have a moderate decrease in our ability to detect DLAs, predominantly in the noisiest spectra. The area under curve drops from 0.96 to 0.91. Our code is publicly available.
Cosmic strings are generically predicted in many extensions of the Standard Model of particle physics. We propose a new avenue for detecting cosmic strings through their effect on the filamentary structure in the cosmic web. Using cosmological simula
tions of the density wake from a cosmic string, we examine a variety of filament structure probes. We show that the largest effect of the cosmic string is an overdensity in the filament distribution around the string wake. The signal from the overdensity is stronger at higher redshift, and more robust with a wider field. We analyze the spatial distribution of filaments from a publicly available catalog of filaments built from SDSS galaxies. With existing data, we find no evidence for the presence of a cosmic string wake with string tension parameter $Gmu$ above $5times 10^{-6}$. However, we project WFIRST will be able to detect a signal from such a wake at the $99%$ confidence level at redshift $z=2$, with significantly higher confidence and the possibility of probing lower tensions ($Gmu sim 10^{-6}$), at $z=10$. The sensitivity of this method is not competitive with constraints derived from the CMB. However, it provides an independent discovery channel at low redshift, which could be a smoking-gun in scenarios where the CMB bound can be weakened.
We present a revised version of our automated technique using Gaussian processes (GPs) to detect Damped Lyman-$alpha$ absorbers (DLAs) along quasar (QSO) sightlines. The main improvement is to allow our Gaussian process pipeline to detect multiple DL
As along a single sightline. Our DLA detections are regularised by an improved model for the absorption from the Lyman-$alpha$ forest which improves performance at high redshift. We also introduce a model for unresolved sub-DLAs which reduces mis-classifications of absorbers without detectable damping wings. We compare our results to those of two different large-scale DLA catalogues and provide a catalogue of the processed results of our Gaussian process pipeline using 158 825 Lyman-$alpha$ spectra from SDSS data release 12. We present updated estimates for the statistical properties of DLAs, including the column density distribution function (CDDF), line density ($dN/dX$), and neutral hydrogen density ($Omega_{textrm{DLA}}$).
The non-zero mass of neutrinos suppresses the growth of cosmic structure on small scales. Since the level of suppression depends on the sum of the masses of the three active neutrino species, the evolution of large-scale structure is a promising tool
to constrain the total mass of neutrinos and possibly shed light on the mass hierarchy. In this work, we investigate these effects via a large suite of N-body simulations that include massive neutrinos using an analytic linear-response approximation: the Cosmological Massive Neutrino Simulations (MassiveNuS). The simulations include the effects of radiation on the background expansion, as well as the clustering of neutrinos in response to the nonlinear dark matter evolution. We allow three cosmological parameters to vary: the neutrino mass sum M_nu in the range of 0-0.6 eV, the total matter density Omega_m, and the primordial power spectrum amplitude A_s. The rms density fluctuation in spheres of 8 comoving Mpc/h (sigma_8) is a derived parameter as a result. Our data products include N-body snapshots, halo catalogues, merger trees, ray- traced galaxy lensing convergence maps for four source redshift planes between z_s=1-2.5, and ray-traced cosmic microwave background lensing convergence maps. We describe the simulation procedures and code validation in this paper. The data are publicly available at http://columbialensing.org.
