Beecroft Institute for Particle Astrophysics and Cosmology

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.

Abstract:

Utilizing optical and near-infrared broad-band photometry covering >5 deg2 in two of the most well-studied extragalactic legacy fields (COSMOS and XMM-LSS), we measure the galaxy stellar mass function (GSMF) between 0.1 < z < 2.0. We explore in detail the effect of two source extraction methods (SExtractor and ProFound) in addition to the inclusion/exclusion of Spitzer IRAC 3.6 and 4.5 μm photometry when measuring the GSMF. We find that including IRAC data reduces the number of massive (log10(M/M⊙) > 11.25) galaxies found due to improved photometric redshift accuracy, but has little effect on the more numerous lower-mass galaxies. We fit the resultant GSMFs with double Schechter functions down to log10(M/M⊙) = 7.75 (9.75) at z = 0.1 (2.0) and find that the choice of source extraction software has no significant effect on the derived best-fitting parameters. However, the choice of methodology used to correct for the Eddington bias has a larger impact on the high-mass end of the GSMF, which can partly explain the spread in derived M* values from previous studies. Using an empirical correction to model the intrinsic GSMF, we find evidence for an evolving characteristic stellar mass with δlog10(M*/M⊙)/δz = −0.16±0.05(−0.11±0.05)⁠, when using SExtractor (ProFound). We argue that with widely quenched star formation rates in massive galaxies at low redshift (z < 0.5), additional growth via mergers is required in order to sustain such an evolution to a higher characteristic mass.