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We present a novel approach to inspecting galaxy spectra using sound, via their direct audio representation (‘spectral audification’). We discuss the potential of this as a complement to (or stand-in for) visual approaches. We surveyed 58 respondents who use the audio representation alone to rate 30 optical galaxy spectra with strong emission lines. Across three tests, each focusing on different quantities measured from the spectra (signal-to-noise ratio, emission-line width, and flux ratios), we find that user ratings are well correlated with measured quantities. This demonstrates that physical information can be independently gleaned from listening to spectral audifications. We note the importance of context when rating these sonifications, where the order examples are heard can influence responses. Finally, we adapt the method used in this promising pilot study to spectral datacubes. We suggest that audification allows efficient exploration of complex, spatially resolved spectral data.

One of the most exciting recent results in the field of dark matter indirect searches has been the discovery of an excess emission in gamma rays from the Galactic centre above the standard astrophysical background. We show that current hydrodynamic simulations, namely simulated Milky Way-like galaxies within the \"Evolution and Assembly of GaLaxies and their Environments\" (EAGLE) project, challenge the possibility to interpret the GeV excess as due to annihilation of dark matter particles in the halo if the Milky Way.

Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim. Deep learning techniques are applied to images from the Sloan Digital Sky Survey to provide visual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this classification is to split the galaxy population into merger and non-merger systems and we are currently achieving an accuracy of 92.5%. Stellar masses and star formation rates are also estimated using panchromatic data for the entire galaxy population. With these preliminary data, the mergers are placed onto the full galaxy main sequence, where we find that merging systems lie across the entire star formation rate - stellar mass plane.

We explore an unconventional bridge between quantum mechanical density matrices and sound by mapping elements of the density matrix and their phases to auditory signals, thus introducing a framework for Open Quantum Sonification. Employing the eigenstates of the Hamiltonian operator as a basis, each quantum state contributes a frequency proportional to its energy level. The off-diagonal terms, which encode coherence and phase relationships between energy levels, are rendered as binaural signals presented separately to the left and right ears. We illustrate this method within the context of open quantum system dynamics governed by Lindblad equations, presenting first an example of quantum Brownian motion of a particle in a thermal bath, and second, a recoherence process induced by boundary driving that results in spin-helix states. This document serves as a companion to the corresponding audio visual simulations of these models available on the YouTube channel Open Quantum Sonification with the Python Codes on GitHub. The auditory analogy presented here provides an intuitive and experiential means of describing quantum phenomena such as tunnelling, thermalisation, decoherence, and recoherence.

We examine the evolution of intrinsic u-r colours of galaxies in the EAGLE cosmological hydrodynamical simulations, which has been shown to reproduce the observed redshift z=0.1 colour-magnitude distribution well. The median u-r of star-forming ('blue cloud') galaxies reddens by 1 mag from z=2 to 0 at fixed stellar mass, as their specific star formation rates decrease with time. A red sequence starts to build-up around z=1, due to the quenching of low-mass satellite galaxies at the faint end, and due to the quenching of more massive central galaxies by their active galactic nuclei (AGN) at the bright end. This leaves a dearth of intermediate-mass red sequence galaxies at z=1, which is mostly filled in by z=0. We quantify the time-scales of colour transition due to satellite and AGN quenching, finding that most galaxies spend less than 2 Gyr in the 'green valley'. On examining the trajectories of galaxies in a colour-stellar mass diagram, we identify three characteristic tracks that galaxies follow (quiescently star-forming, quenching and rejuvenating galaxies) and quantify the fraction of galaxies that follow each track.

We have created a show about the Solar System, freely available for both planetariums and home viewing, where objects in space are represented with sound as well as with visuals. For example, the audience listens to the stars appear above the European Southern Observatory's Very Large Telescope and they hear the planets orbit around their heads. The aim of this show is that it can be enjoyed and understood, irrespective of level of vision. Here we describe how we used our new computer code, STRAUSS, to convert data into sound for the show. We also discuss the lessons learnt during the design of the show, including how it was imperative to obtain a range of diverse perspectives from scientists, a composer and representatives of the blind and vision impaired community.

We introduce STRAUSS (Sonification Tools and Resources for Analysis Using Sound Synthesis) a modular, self-contained and flexible Python sonification package, operating in a free and open source (FOSS) capacity. STRAUSS is intended to be a flexible tool suitable for both scientific data exploration and analysis as well as for producing sonifications that are suitable for public outreach and artistic contexts. We explain the motivations behind STRAUSS, and how these lead to our design choices. We also describe the basic code structure and concepts. We then present output sonification examples, specifically: (1) multiple representations of univariate data (i.e., single data series) for data exploration; (2) how multi-variate data can be mapped onto sound to help interpret how those data variables are related and; (3) a full spatial audio example for immersive Virtual Reality. We summarise, alluding to some of the future functionality as STRAUSS development accelerates.

The iMaNGA project uses a forward-modelling approach to compare the predictions of cosmological simulations with observations from SDSS-IV/MaNGA. We investigate the dependency of age and metallicity radial gradients on galaxy morphology, stellar mass, stellar surface mass density (\\(\\Sigma_*\\)), and environment. The key of our analysis is that observational biases affecting the interpretation of MaNGA data are emulated in the theoretical iMaNGA sample. The simulations reproduce the observed global stellar population scaling relations with positive correlations between galaxy mass and age/metallicity quite well and also produce younger stellar populations in late-type in agreement with observations. We do find interesting discrepancies, though, that can inform the physics and further development of the simulations. Ages of spiral galaxies and low-mass ellipticals are overestimated by about 2-4 Gyr. Radial metallicity gradients are steeper in iMaNGA than in MaNGA, a discrepancy most prominent in spiral and lenticular galaxies. Also, the observed steepening of metallicity gradients with increasing galaxy mass is not well matched by the simulations. We find that the theoretical radial profiles of surface mass density \\(\\Sigma_*\\) are steeper than in observations except for the most massive galaxies. In both MaNGA and iMaNGA [Z/H] correlates with \\(\\Sigma_*\\), however, the simulations systematically predict lower [Z/H] by almost a factor of 2 at any \\(\\Sigma_*\\). Most interestingly, for galaxies with stellar mass \\(\\log M_*\\leq 10.80 M_\\odot\\) the MaNGA data reveal a positive correlation between galaxy radius and [Z/H] at fixed \\(\\Sigma_*\\), which is not recovered in iMaNGA. Finally, the dependence on environmental density is negligible in both the theoretical iMaNGA and the observed MaNGA data.

We investigate the formation and properties of low surface brightness galaxies (LSBGs) with \\(M_{*} > 10^{9.5} \\mathrm{M_{\\odot}}\\) in the EAGLE hydrodynamical cosmological simulation. Galaxy surface brightness depends on a combination of stellar mass surface density and mass-to-light ratio (\\(M/L\\)), such that low surface brightness is strongly correlated with both galaxy angular momentum (low surface density) and low specific star formation rate (high \\(M/L\\)). This drives most of the other observed correlations between surface brightness and galaxy properties, such as the fact that most LSBGs have low metallicity. We find that LSBGs are more isolated than high surface brightness galaxies (HSBGs), in agreement with observations, but that this trend is driven entirely by the fact that LSBGs are unlikely to be close-in satellites. The majority of LSBGs are consistent with a formation scenario in which the galaxies with the highest angular momentum are those that formed most of their stars recently from a gas reservoir co-rotating with a high-spin dark matter halo. However, the most extended LSBG disks in EAGLE, which are comparable in size to observed giant LSBGs, are built up via mergers. These galaxies are found to inhabit dark matter halos with a higher spin in their inner regions (\\(<0.1r_{200c}\\)), even when excluding the effects of baryonic physics by considering matching halos from a dark matter only simulation with identical initial conditions.