| 1. |
- Jenkins, Alexander C., et al.
(författare)
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Analog vacuum decay from vacuum initial conditions
- 2024
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Ingår i: Physical Review D. - 2470-0010 .- 2470-0029. ; 109:2
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Tidskriftsartikel (refereegranskat)abstract
- Ultracold atomic gases can undergo phase transitions that mimic relativistic vacuum decay, allowing us to empirically test early Universe physics in tabletop experiments. We investigate the physics of these analog systems, going beyond previous analyses of the classical equations of motion to study quantum fluctuations in the cold-atom false vacuum. We show that the fluctuation spectrum of this vacuum state agrees with the usual relativistic result in the regime where the classical analogy holds, providing further evidence for the suitability of these systems for studying vacuum decay. Using a suite of semiclassical lattice simulations, we simulate bubble nucleation from this analog vacuum state in a 1D homonuclear potassium-41 mixture, finding qualitative agreement with instanton predictions. We identify realistic parameters for this system that will allow us to study vacuum decay with current experimental capabilities, including a prescription for efficiently scanning over decay rates, and show that this setup will probe the quantum (rather than thermal) decay regime at temperatures T≲10 nK. Our results help lay the groundwork for using upcoming cold-atom experiments as a new probe of nonperturbative early Universe physics.
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| 2. |
- Lucie -Smith, Luisa, et al.
(författare)
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Deep learning insights into cosmological structure formation
- 2024
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Ingår i: Physical Review D. - 2470-0010 .- 2470-0029. ; 109:6
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Tidskriftsartikel (refereegranskat)abstract
- The evolution of linear initial conditions present in the early Universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass. However, the overall scatter in the final mass predictions does not change qualitatively with this additional information, only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our results demonstrate that isotropic aspects of the initial density field essentially saturate the relevant information about final halo mass. Therefore, instead of searching for information directly encoded in initial conditions anisotropies, a more promising route to accurate, fast halo mass predictions is to add approximate dynamical information based e.g. on perturbation theory. More broadly, our results indicate that deep learning frameworks can provide a powerful tool for extracting physical insight into cosmological structure formation.
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| 3. |
- Lucie-Smith, Luisa, et al.
(författare)
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Explaining Dark Matter Halo Density Profiles with Neural Networks
- 2024
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Ingår i: Physical Review Letters. - 0031-9007 .- 1079-7114. ; 132:3
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Tidskriftsartikel (refereegranskat)abstract
- We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos’ evolution, the network recovers the known relation between the early time assembly and the inner profile and discovers that the profile beyond the virial radius is described by a single parameter capturing the most recent mass accretion rate. The results illustrate the potential for machine-assisted scientific discovery in complicated astrophysical datasets.
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| 4. |
- Setzer, Christian N., 1990-
(författare)
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Modelling and Detecting Kilonovae in the Rubin Observatory Era
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Survey astronomy is a powerful tool for discoveries in astrophysics and cosmology. In the coming years, this field will be revolutionised with the start of the ten-year Legacy Survey of Space and Time (LSST), to be conducted at the Vera C. Rubin Observatory. This survey, with its unique capabilities in temporal sampling, single-image depth and covered sky-area, will explore a new discovery space for astrophysical transients in the Universe. The 2017 discovery of an electromagnetic and gravitational-wave transient presents a unique opportunity to influence the design of the LSST observing strategy for the detection of binary neutron star (BNS) mergers. This will be scientifically beneficial, not only for studies of the astrophysics of these sources, but also for developing new cosmological probes. Given the sensitivity of the Rubin Observatory, it is expected that this instrument will detect these binary neutron star mergers to greater distances than detectable by current and near-term gravitational wave detectors. This presents further opportunities to study the characteristics of the BNS population that will be selected into these surveys. If we understand the underlying BNS merger population and associated electromagnetic emission, it may also be possible to recover the previously undetected counterpart gravitational wave signals.In this thesis I discuss kilonovae (kNe) from BNS mergers with a focus on detection of kNe in the LSST survey. I will discuss the physics and modelling of kNe, including my work incorporating a viewing-angle dependence in the optical light curve modelling of BNS kNe. After setting the context for the Rubin Observatory and the LSST, I will describe work on optimising the observing strategy of the LSST to detect kNe from BNS mergers and the observing strategy features that impact detection. This work also indicates that a portion of the BNS mergers associated with kN detections in the LSST will be below the threshold for detection of their gravitational wave emission. Furthermore, I will discuss modelling a population of kNe from BNS mergers that is consistent with each merger’s associated gravitational-wave signal. This modelling includes a dependence of the kN on nuclear physics calibrated with detailed emulation of radiation-transport simulations. I conclude by summarising the scientific impact of this research and discussing future directions, such as: studying the BNS multi-messenger observational selection function for the LSST and concurrent gravitational wave detectors, detection of subthreshold signals, and the problem of classifying kN light curves.
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| 5. |
- Stopyra, Stephen, et al.
(författare)
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An antihalo void catalogue of the Local Super-Volume
- 2024
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Ingår i: Monthly notices of the Royal Astronomical Society. - 0035-8711 .- 1365-2966. ; 531:2, s. 2213-2222
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Tidskriftsartikel (refereegranskat)abstract
- We construct an antihalo void catalogue of 150 voids with radii ?>10ℎ−1Mpc in the Local Super-Volume (<135ℎ−1Mpc from the Milky Way), using posterior resimulation of initial conditions inferred by field-level inference with Bayesian Origin Reconstruction from Galaxies (BORG). We describe and make use of a new algorithm for creating a single, unified void catalogue by combining different samples from the posterior. The catalogue is complete out to 135ℎ−1Mpc, with void abundances matching theoretical predictions. Finally, we compute stacked density profiles of those voids which are reliably identified across posterior samples, and show that these are compatible with Λ cold dark matter expectations once environmental selection (e.g. the estimated ∼4 per cent underdensity of the Local Super-Volume) is accounted for.
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