Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

Hydrodynamical cosmological simulations are increasing their level of realism by considering more physical processes and having greater resolution or larger statistics. However, usually either the statistical power of such simulations or the resolution reached within galaxies are sacrificed. Here, we introduce the NEWHORIZON project in which we simulate at high resolution a zoom-in region of ∼(16 Mpc)3 that is larger than a standard zoom-in region around a single halo and is embedded in a larger box. A resolution of up to 34 pc, which is typical of individual zoom-in, up-to-date resimulated halos, is reached within galaxies; this allows the simulation to capture the multi-phase nature of the interstellar medium and the clumpy nature of the star formation process in galaxies. In this introductory paper, we present several key fundamental properties of galaxies and their black holes, including the galaxy mass function, cosmic star formation rate, galactic metallicities, the Kennicutt–Schmidt relation, the stellar-to-halo mass relation, galaxy sizes, stellar kinematics and morphology, gas content within galaxies and its kinematics, and the black hole mass and spin properties over time. The various scaling relations are broadly reproduced by NEWHORIZON with some differences with the standard observables. Owing to its exquisite spatial resolution, NEWHORIZON captures the inefficient process of star formation in galaxies, which evolve over time from being more turbulent, gas rich, and star bursting at high redshift. These high-redshift galaxies are also more compact, and they are more elliptical and clumpier until the level of internal gas turbulence decays enough to allow for the formation of discs. The NEWHORIZON simulation gives access to a broad range of galaxy formation and evolution physics at low-to-intermediate stellar masses, which is a regime that will become accessible in the near future through surveys such as the LSST.

Abstract:

We present the first results from SPHINX-MHD, a suite of cosmological radiation-magnetohydrodynamics simulations designed to study the impact of primordial magnetic fields (PMFs) on galaxy formation and the evolution of the intergalactic medium during the epoch of reionization. The simulations are among the first to employ on-the-fly radiation transfer and constrained transport ideal MHD in a cosmological context to simultaneously model the inhomogeneous process of reionization and the growth of PMFs. We run a series of $(5{\rm Mpc})^3$ cosmological volumes, varying both the strength of the seed magnetic field and its spectral index. We find that PMFs with a spectral index ($n_B$) and a comoving amplitude ($B_0$) that have $n_B>-0.562\log_{10}(B_0/1{\rm n}G) - 3.35$ produce electron optical depths ($\tau_e$) that are inconsistent with CMB constraints due to the unrealistically early collapse of low-mass dwarf galaxies. For $n_B\geq-2.9$, our constraints are considerably tighter than the $\sim{\rm n}G$ constraints from Planck. PMFs that do not satisfy our constraints have little impact on the reionization history or the shape of the UV luminosity function. Likewise, detecting changes in the Ly$\alpha$ forest due to PMFs will be challenging because photoionisation and photoheating efficiently smooth the density field. However, we find that the first absorption feature in the global 21cm signal is a particularly sensitive indicator of the properties of the PMFs, even for those that satisfy our $\tau_e$ constraint. Furthermore, strong PMFs can increase the escape of LyC photons by up to 25% and shrink the effective radii of galaxies by 44% which could increase the completeness fraction of galaxy surveys. Finally, our simulations show that surveys with a magnitude limit of ${\rm M_{UV,1500{\rm A}}=-13}$ can probe the sources that provide the 50% of photons for reionization out to $z=12$.

Abstract:

Photometric redshifts (photo-zs) are one of the main ingredients in the analysis of cosmological probes. Their accuracy particularly affects the results of the analyses of galaxy clustering with photometrically selected galaxies (GCph) and weak lensing. In the next decade, space missions such as Euclid will collect precise and accurate photometric measurements for millions of galaxies. These data should be complemented with upcoming ground-based observations to derive precise and accurate photo-zs. In this article we explore how the tomographic redshift binning and depth of ground-based observations will affect the cosmological constraints expected from the Euclid mission. We focus on GCph and extend the study to include galaxy-galaxy lensing (GGL). We add a layer of complexity to the analysis by simulating several realistic photo-z distributions based on the Euclid Consortium Flagship simulation and using a machine learning photo-z algorithm. We then use the Fisher matrix formalism together with these galaxy samples to study the cosmological constraining power as a function of redshift binning, survey depth, and photo-z accuracy. We find that bins with an equal width in redshift provide a higher figure of merit (FoM) than equipopulated bins and that increasing the number of redshift bins from ten to 13 improves the FoM by 35% and 15% for GCph and its combination with GGL, respectively. For GCph, an increase in the survey depth provides a higher FoM. However, when we include faint galaxies beyond the limit of the spectroscopic training data, the resulting FoM decreases because of the spurious photo-zs. When combining GCph and GGL, the number density of the sample, which is set by the survey depth, is the main factor driving the variations in the FoM. Adding galaxies at faint magnitudes and high redshift increases the FoM, even when they are beyond the spectroscopic limit, since the number density increase compensates for the photo-z degradation in this case. We conclude that there is more information that can be extracted beyond the nominal ten tomographic redshift bins of Euclid and that we should be cautious when adding faint galaxies into our sample since they can degrade the cosmological constraints

Abstract:

We present the serendipitous discovery of a galaxy group in the XMM-LSS field with MIGHTEE Early Science observations. 20 galaxies are detected in H I in this z ∼ 0.044 group, with a 3σ column density sensitivity of NHI=1.6×1020cm−2⁠. This group has not been previously identified, despite residing in a well-studied extragalactic legacy field. We present spatially resolved H I total intensity and velocity maps for each of the objects which reveal environmental influence through disturbed morphologies. The group has a dynamical mass of log10(Mdyn/M⊙)=12.32⁠, and is unusually gas-rich, with an H I-to-stellar mass ratio of log10(f∗HI)=−0.2⁠, which is 0.7 dex greater than expected. The group’s high H I content, spatial, velocity, and identified galaxy type distributions strongly suggest that it is in the early stages of its assembly. The discovery of this galaxy group is an example of the importance of mapping spatially resolved H I in a wide range of environments, including galaxy groups. This scientific goal has been dramatically enhanced by the high sensitivity, large field-of-view, and wide instantaneous bandwidth of the MeerKAT telescope.

Abstract:

Many of the species in decline around the world are subject to different environmental stressors across their range, so replicated large-scale monitoring programmes, are necessary to disentangle the relative impacts of these threats. At the same time as funding for long-term monitoring is being cut, studies are increasingly being criticised for lacking statistical power. For those taxa or environments where a single vantage point can observe individuals or ecological processes, time-lapse cameras can provide a cost-effective way of collecting time series data replicated at large spatial scales that would otherwise be impossible. However, networks of time-lapse cameras needed to cover the range of species or processes create a problem in that the scale of data collection easily exceeds our ability to process the raw imagery manually. Citizen science and machine learning provide solutions to scaling up data extraction (such as locating all animals in an image). Crucially, citizen science, machine learning-derived classifiers, and the intersection between them, are key to understanding how to establish monitoring systems that are sensitive to – and sufficiently powerful to detect –changes in the study system. Citizen science works relatively ‘out of the box’, and we regard it as a first step for many systems until machine learning algorithms are sufficiently trained to automate the process. Using Penguin Watch (www.penguinwatch.org) data as a case study, we discuss a complete workflow from images to parameter estimation and interpretation: the use of citizen science and computer vision for image processing, and parameter estimation and individual recognition for investigating biological questions. We discuss which techniques are easily generalizable to a range of questions, and where more work is needed to supplement ‘out of the box’ tools. We conclude with a horizon scan of the advances in camera technology, such as on-board computer vision and decision making.