Publications

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83 Publications visible to you, out of a total of 83

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ABSTRACT Understanding what shapes the cold gas component of galaxies, which both provides the fuel for star formation and is strongly affected by the subsequent stellar feedback, is a crucial stepeedback, is a crucial step towards a better understanding of galaxy evolution. Here, we analyse the H i properties of a sample of 46 Milky Way halo-mass galaxies, drawn from cosmological simulations (EMP-Pathfinder and Firebox). This set of simulations comprises galaxies evolved self-consistently across cosmic time with different baryonic sub-grid physics: three different star formation models [constant star formation efficiency (SFE) with different star formation eligibility criteria, and an environmentally dependent, turbulence-based SFE] and two different feedback prescriptions, where only one sub-sample includes early stellar feedback. We use these simulations to assess the impact of different baryonic physics on the H i content of galaxies. We find that the galaxy-wide H i properties agree with each other and with observations. However, differences appear for small-scale properties. The thin H i discs observed in the local universe are only reproduced with a turbulence-dependent SFE and/or early stellar feedback. Furthermore, we find that the morphology of H i discs is particularly sensitive to the different physics models: galaxies simulated with a turbulence-based SFE have discs that are smoother and more rotationally symmetric, compared to those simulated with a constant SFE; galaxies simulated with early stellar feedback have more regular discs than supernova-feedback-only galaxies. We find that the rotational asymmetry of the H i discs depends most strongly on the underlying physics model, making this a promising observable for understanding the physics responsible for shaping the interstellar medium of galaxies.

Authors: Jindra Gensior, Robert Feldmann, Marta Reina-Campos, Sebastian Trujillo-Gomez, Lucio Mayer, Benjamin W Keller, Andrew Wetzel, J M Diederik Kruijssen, Philip F Hopkins, Jorge Moreno

Date Published: 1st Jun 2024

Publication Type: Journal

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ABSTRACT We present an end-to-end description of the formation of globular clusters (GCs) combining a treatment for their formation and dynamical evolution within galaxy haloes with a state-of-the-artes with a state-of-the-art semi-analytic simulation of galaxy formation. Our approach allows us to obtain exquisite statistics to study the effect of the environment and assembly history of galaxies, while still allowing a very efficient exploration of the parameter space. Our reference model, including both efficient cluster disruption during galaxy mergers and dynamical friction of GCs within the galactic potential, accurately reproduces the observed correlation between the total mass in GCs and the parent halo mass. A deviation from linearity is predicted at low-halo masses, which is driven by a strong dependence on morphological type: bulge-dominated galaxies tend to host larger masses of GCs than their later-type counterparts. While the significance of the difference might be affected by resolution at the lowest halo masses considered, this is a robust prediction of our model and a natural consequence of the assumption that cluster migration into the halo is triggered by galaxy mergers. Our model requires an environmental dependence of GC radii to reproduce the observed low-mass mass distribution of GCs in our Galaxy. At GC masses $\gt 10^6\, {\rm M}_\odot$, our model predicts fewer GCs than observed, due to an overly aggressive treatment of dynamical friction. Our model reproduces well the metallicity distribution measured for Galactic GCs, even though we predict systematically younger GCs than observed. We argue that this adds further evidence for an anomalously early formation of the stars in our Galaxy.

Authors: Gabriella De Lucia, J M Diederik Kruijssen, Sebastian Trujillo-Gomez, Michaela Hirschmann, Lizhi Xie

Date Published: 1st May 2024

Publication Type: Journal

Abstract

Not specified

Authors: Nikos Gianniotis, Kai L. Polsterer, Iliana Isabel Cortés Pérez

Date Published: 2024

Publication Type: InProceedings

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ABSTRACT Globular clusters (GCs) are powerful tracers of the galaxy assembly process, and have already been used to obtain a detailed picture of the progenitors of the Milky Way (MW). Using the E-MOSAICS (MW). Using the E-MOSAICS cosmological simulation of a (34.4 Mpc)3 volume that follows the formation and co-evolution of galaxies and their star cluster populations, we develop a method to link the origin of GCs to their observable properties. We capture this complex link using a supervised deep learning algorithm trained on the simulations, and predict the origin of individual GCs (whether they formed in the main progenitor or were accreted from satellites) based solely on extragalactic observables. An artificial neural network classifier trained on ∼50 000 GCs hosted by ∼700 simulated galaxies successfully predicts the origin of GCs in the test set with a mean accuracy of 89 per cent for the objects with $\rm [Fe/H]\lt -0.5$ that have unambiguous classifications. The network relies mostly on the alpha-element abundances, metallicities, projected positions, and projected angular momenta of the clusters to predict their origin. A real-world test using the known progenitor associations of the MW GCs achieves up to 90 per cent accuracy, and successfully identifies as accreted most of the GCs in the inner Galaxy associated to the Kraken progenitor, as well as all the Gaia-Enceladus GCs. We demonstrate that the model is robust to observational uncertainties, and develop a method to predict the classification accuracy across observed galaxies. The classifier can be optimized for available observables (e.g. to improve the accuracy by including GC ages), making it a valuable tool to reconstruct the assembly histories of galaxies in upcoming wide-field surveys.

Authors: Sebastian Trujillo-Gomez, J M Diederik Kruijssen, Joel Pfeffer, Marta Reina-Campos, Robert A Crain, Nate Bastian, Ivan Cabrera-Ziri

Date Published: 1st Dec 2023

Publication Type: Journal

Abstract

Not specified

Authors: F. Pozo Nunez, N. Gianniotis, K.L. Polsterer

Date Published: 18th Apr 2023

Publication Type: Journal

Abstract (Expand)

We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early 1990s and the resulting Terascale data sets, which required automating of many data processing and analysis tasks, for example the star-galaxy separation, with billions of feature vectors in hundreds of dimensions. The exponential data growth continued, with the rise of synoptic sky surveys and the Time Domain Astronomy, with the resulting Petascale data streams and the need for a real-time processing, classification, and decision making. A broad variety of classification and clustering methods have been applied for these tasks, and this remains a very active area of research. Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications of an ever increasing complexity and sophistication. ML and AI are now a standard part of the astronomical toolkit. As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.

Authors: S. G. Djorgovski, Ashish Mahabal, M. J. Graham, Kai L. Polsterer, Alberto Krone-Martins

Date Published: 1st Apr 2023

Publication Type: InCollection

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Abstract Photometric reverberation mapping can detect the radial extent of the accretion disc (AD) in Active Galactic Nuclei by measuring the time delays between light curves observed in differentrves observed in different continuum bands. Quantifying the constraints on the efficiency and accuracy of the delay measurements is important for recovering the AD size-luminosity relation, and potentially using quasars as standard candles. We have explored the possibility of determining the AD size of quasars using next-generation Big Data surveys. We focus on the Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, which will observe several thousand quasars with the Deep Drilling Fields and up to 10 million quasars for the main survey in six broadband filter during its 10-year operational lifetime. We have developed extensive simulations that take into account the characteristics of the LSST survey and the intrinsic properties of the quasars. The simulations are used to characterise the light curves from which AD sizes are determined using various algorithms. We find that the time delays can be recovered with an accuracy of 5 and 15% for light curves with a time sampling of 2 and 5 days, respectively. The results depend strongly on the redshift of the source and the relative contribution of the emission lines to the bandpasses. Assuming an optically thick and geometrically thin AD, the recovered time-delay spectrum is consistent with black hole masses derived with 30% uncertainty.

Authors: F Pozo Nuñez, C Bruckmann, S Desamutara, B Czerny, S Panda, A P Lobban, G Pietrzyński, K L Polsterer

Date Published: 6th Feb 2023

Publication Type: Journal

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