Nucleosynthesis yields from sub-Chandrasekhar (sub M-ch) and Chandrasekhar (M-ch) SN Ia progenitors have been discussed and debated for decades on their contributions to iron peak elements in the cosmos. Investigating SNe Ia in ultra-faint dwarf galaxies (UFDs) and dwarf spheroidal galaxies (dSphs) with different star formation and chemical enrichment histories may shed light on the progenitors in different environments. To this end, we incorporate metallicity dependent SN Ia yields from different progenitors within our novel inhomogeneous chemical evolution model, i-GEtool, and compare the predicted chemical abundances to observations in different UFD and dSph galaxies. While the observed [Mn/Mg] ratios increase towards higher metallicities both within single galaxies and when considering galaxies with different metallicity distributions, the observed [Ni/Mg] ratios show a weaker correlation. In my talk, I will show that our models for UFD and dSph can reproduce the observed trends along with their scatter without invoking any contribution from sub M-ch SN Ia progenitors, at variance with previous studies in the literature. I will discuss the implications of our findings for the observed iron peak elemental abundances in the Milky Way halo and disks, outlining our future plan.
The population of isomeric (metastable) excited states in nuclei within astrophysical environments associated with R-process freezeout can affect the final abundance of stable isotopes; these astrophysically relevant isomers are known as `astromers’ [1][2]. Astromers can be populated/depopulated via various electromagnetic mechanisms, generally via low excitation energy, short-lived states above the isomer. The population of these `astromers’ has been studied theoretically using the Planckian photon bath [1] in which a network of photo-nuclear excited states and subsequent relaxations is considered during the population of an astromer from its associated nuclear ground state.
Similarly, an isomer can be depopulated [2] with a much lower, yet albeit comparable electron flux via inelastic electron-scattering processes, which will necessarily also deplete the astrophysical isomer [3]. One such electromagnetic process is `nuclear excitation by electron capture’ (NEEC), which is the inverse of internal conversion, recently reported to deplete isomers terrestrially with a high [4], yet still refuted [5], excitation probability in a radioactive ion-beam scenario.
This presentation focuses around encouraging the development hot-dense-plasma experiments using the photon and electron flux available at current or in-development peta-Watt (PW) laser facilities, which allow experimentation into separating the electromagnetic mechanisms at play in depleting isomers. This will allow us to readily challenge the relevance of astromers in calculating the final abundance of isotopes in the cosmos.
References
[1] G. Wendell Misch et al. “Astromers: Nuclear Isomers in Astrophysics”. In: The Astrophysical Journal Supplement Series 252.1 (Dec. 2020), p. 2. doi: 10.3847/ 1538-4365/abc41d
[2] G. Wendell Misch, T. M. Sprouse, and M. R. Mumpower. “Astromers in the Radioactive Decay of r-process Nuclei”. In: The Astrophysical Journal Letters 913.1 (May 2021), p. L2. doi: 10.3847/2041- 8213/abfb74
[3] J. Carroll and C. Chiara. “Isomer depletion”. In: The European Physical Journal Special Topics (Apr. 2024). doi: 10.1140/epjs/s11734-024-01149-8
[4] C. Chiara et al. “Isomer depletion as experimental evidence of nuclear excitation by electron capture”. In: Nature Publishing Group 554.7691 (2018), pp. 216–218. doi: 10.1038/nature25483.
[5] Y. Wu, C. H. Keitel, and A. P´alffy. “93mMo isomer depletion via beam-based nuclear excitation by electron capture”.
From the chemodynamical properties of tidal debris in the Milky Way (MW), it has been inferred that disrupted dwarf satellites had different chemical abundances at their time of accretion compared with similar-mass dwarf satellites which survive at present day. Specifically, disrupted satellites appear to have had lower [Fe/H] and higher [Mg/Fe] at fixed stellar mass than the surviving ones. In a recent study (Grimozzi, Font & De Rossi 2024), we have used the ARTEMIS simulations to investigate this problem, and determine the evolution of chemical abundances (e.g., the stellar mass-metallicity relation, MZR) with redshift. We have found a strong correlation between the scatter in the MZR of the disrupted dwarfs and their accretion redshift (zacc), as well as with their cold gas fractions at accretion. The slopes of the MZRs of disrupted dwarf satellites are fairly similar at different accretion redshifts, and are comparable with the MZR slope of surviving satellites in the MW today (≈ 0.32). This findings constrain some of the physical processes that regulate the chemical enrichment of dwarf galaxies (for example, the stellar feedback). The simulations also predict strong correlations between averaged properties of the disrupted dwarf populations, such as between «zacc», «[Fe/H]» and «[Mg/Fe]»), which suggests that the chemical abundances of the entire disrupted dwarf population can be used to constrain the merger history of its host. More specifically for the MW, the ARTEMIS simulations predict that the bulk of the disrupted population was accreted at «zacc» ≈ 2, to match the averaged «[Fe/H]» and «[Mg/Fe]». More broadly, our results suggest that one can gain an insight into the formation histories of other MW ‘analogues’, such as M31 or other massive galaxies nearby, provided that chemical abundances ([Fe/H] and [alpha/Fe]) of their debris from disrupted satellites become available.
The workhorse of understanding stellar evolution has been in 1D stellar evolution modelling, where simplified prescriptions of physical processes are implemented to evolve a star over its entire lifetime. While stellar evolution modelling has improved over the decades, their results are still limited by uncertainties in the physics due to complex multi-dimensional processes in stellar interiors. To better understand, and hopefully improve some of these uncertainties, we have run 3D hydrodynamic simulations of the final hour of silicon shell convection of a 14M_solar star prior to core-collapse. I will present these results, and compare them to what was found in 1D stellar evolution calculations. I will discuss how the presence of realistic turbulent mixing affects nuclear burning and how choices of convective overshooting in 1D can affect the final structure of the massive star.
Massive stars are not understood well enough given the important role their evolution and fates play in Galactic Chemical Evolution (GCE). One key uncertainty is convective boundary mixing (CBM), which encompasses the processes by which materials mix across the edge of convective turbulent regions inside stars. As a result of its effects on stellar structure during evolution, CBM also affects nucleosynthesis and consequently stellar yields. To investigate the importance of CBM we have computed two grids of stellar models at Z={10}^{-3} and two different strengths of CBM using the MESA code. The first being the typical CBM value used in literature and the second is based on the results of 3D convection simulations. In this talk, we will present a comparison of the structure of massive stars both during their evolution and at the end of their lives for these two different strengths of CBM to assess the impact of CBM on stellar evolution, SN progenitors and nucleosynthesis with a particular emphasis on the mergers of different burning shells.
Thorne-Żytkow Objects are a class of hybrid stars (Thorne & Żytkow 1977), consisting of a neutron star, surrounded by a diffuse convective envelope.
The formation rates of TŻOs are not well constrained but the presence of a modest population of such objects in the Galaxy could have a significant effect on Galactic Chemical Evolution. Optimistically, a large fraction of X-ray binaries end up as TŻOs, contributing to the abundance of p-process elements.
Farmer et al. (2023) placed a central boundary condition at ~600km above the centre of the star whereas we instead opt to place this at the surface of the neutron star and make use of an accretion prescription set by the opacity at the base of the envelope to model the release of gravitational energy.
We construct a series of models that show differences from those of Cannon and Farmer. Our models’ structures show an analogue of the supergiant-like solutions from TŻ and Cannon et al., these solutions being found across a wide range of masses, including where TŻ found a different, giant-like structure.
We find that the deviation from the Cannon et al. series can be explained by our prescriptions for neutrino generation, while the more significant differences to Farmer et al. are likely a function of the differences in boundary conditions.
We discuss the implications of the possible existence of differing series of structures for TŻOs. We also discuss the implications of our structures for nucleosynthetic pathways, and the further effects on GCE.
We use the (100 Mpc/h)3 Simba-C simulation to examine the chemical abundances in hot intragroup and intracluster gas, by extracting and fitting mock X-ray spectra using the MOXHA pipeline from Jennings & Dave’. As part of our initial testing phase, we used XSPEC to extract the inner-core chemical abundances of O, Ne, Mg, Al, Si, S, Ar, Fe, and Ni from our simulated MOXHA X-ray spectra for seven clusters, seven warm groups, and seven cooler groups. We found increasing chemical abundances for most elements as a function of the halo temperature above kT>1 keV (corresponding to the warmer groups), with exceptions observed for Al, S, and Ar. We also found decreasing [α/Fe] abundance ratios as a function of the halo temperatures. We are in the process of extending the number of haloes to include all simulated haloes within Simba-C with sufficient M500 halo mass and sufficient hot gas for Athena X-IFU 0.5-3.0 keV detections at R500, as well as using the Bayesian X-ray Analysis MCMC simulation to maximise the best-fit likelihood.
Within hierarchical triple stellar systems, there exists a tidal
process unique to them, known as tertiary tides. In this process,
the tidal deformation of a tertiary in a hierarchical triple drains
energy from the inner binary, causing the inner binary’s orbit to
shrink. Previous work has uncovered the rate at which tertiary
tides drain energy from inner binaries, as a function of orbital
and tidal parameters, for hierarchical triples in which the orbits
are all circular and coplanar. However, not all hierarchical triples
have orbits which are circular and coplanar, which requires an
understanding of what happens when this condition is relaxed.
In this paper, we study how eccentricities affect tertiary tides,
and their influence on the subsequent dynamical evolution of the
host hierarchical triple. We find that eccentricities in the outer
orbit undergo tidal circularisation quickly, and are therefore trivial,
but that eccentricities in the inner binary completely change the
behaviour of tertiary tides, draining energy from the outer orbit as
well as the inner orbit. Empirical functions that approximate this
behaviour are provided for ease of implementing this process in
other stellar evolution codes, and the implications of these results
are discussed.
Over the last three years, the rates of all the main nuclear reactions involving the destruction and production of 26Al in stars (26Al(n, p)26Mg, 26Al(n, α)23Na, 26Al(p, γ)27Si and 25Mg(p, γ)26Al) have been re-evaluated thanks to new high-precision experimental measurements of their cross sections at energies of astrophysical interest, considerably reducing the uncertainties in the nuclear physics affecting their nucleosynthesis. We computed the nucleosynthetic yields ejected by the explosion of a high-mass star (20 M⊙, Z = 0.0134) using the FRANEC stellar code, considering two explosion energies, 1.2 × 10^51 erg and 3 × 10^51 erg. We quantify the change in the ejected amount of 26Al and other key species that is predicted when the new rate selection is adopted instead of the reaction rates from the STARLIB nuclear library. Additionally, the ratio of our ejected yields of 26Al to those of 14 other short-lived radionuclides (36Cl, 41Ca, 53Mn, 60Fe, 92Nb, 97Tc, 98Tc, 107Pd, 126Sn, 129I, 36Cs, 146Sm, 182Hf, 205Pb) are compared to early solar system isotopic ratios, inferred from meteorite measurements. The total ejected 26Al yields vary by a factor of ~3 when adopting the new rates or the STARLIB rates. Additionally, the new nuclear reaction rates also impact the predicted abundances of short-lived radionuclides in the early solar system relative to 26Al. However, it is not possible to reproduce all the short lived radionuclide isotopic ratios with our massive star model alone, unless a second stellar source could be invoked, which must have been active in polluting the pristine solar nebula at a similar time of a core-collapse supernova.
The most metal-poor stars offer a unique opportunity to understand early chemical enrichment in galaxies, carrying imprints of the first supernovae (SNe). The Sagittarius (Sgr) dwarf galaxy is ideal for testing chemical evolution and hierarchical accretion models. However, its most metal-poor region remains unexplored.
I will summarize findings from the Pristine Inner Galaxy Survey (PIGS), which hunts the most metal-poor stars in the inner Galaxy and Sgr. I will present results from the largest detailed chemical abundance analysis of very metal-poor stars ([Fe/H]−2.0, as suggested by [Co/Fe] in this range.
In the second part, I will discuss the chemo-dynamical properties of Sgr using a sample of ~350 metal-poor ([Fe/H]+0.7) underestimates the fraction of these stars in dwarf galaxies. We propose adjusting this threshold to [C/Fe]∼+0.35 for Sgr, leading to a similar fraction of CEMP stars between the Milky Way and Sgr.
In this talk, I will present results from the latest version of L-GALAXIES, a galaxy evolution simulation which now includes binary stellar evolution (using binary_c) and dust production & destruction. Through its sophisticated galactic chemical evolution modelling, L-GALAXIES can provide a comprehensive overview of the total metal and dust content in the Universe, separated into various astrophysical “phases” (e.g. stars, molecular clouds, neutral interstellar medium, and diffuse circumgalactic medium). By comparing this to observations, we can help provide constraints on the small-scale models used in simulations for stellar nucleosynthesis, stellar feedback, and dust production (including supernovae, binary-star phenomena, and grain growth).
First, I will present the evolution of the cosmic metal density in L-GALAXIES back to z~6, compared to observations of the neutral ISM from damped Lyman-alpha (DLA) systems. Second, I will show the complimentary evolution of the cosmic dust mass density in L-GALAXIES across the same period, compared to dust observations from SED fitting and DLA absorption-line spectra. Third, I will combine these to provide an overall census of the dust and metal content in various phases of galaxies as a function of their stellar mass.
These analyses reveal three key findings: (a) simulations must allow significant ejection of metals and dust out of galaxies via supernova-driven winds, (b) simulations may also need to be recalibrated at high redshift to account for the dust-obscured star formation now observed, and (c) DLA observations may over-estimate the metal and dust budget of the Universe due to biased sampling.
In massive-star binary systems, upon reaching later stages of stellar evolution one star can expand as a giant and envelope its companion in what is called a common envelope phase. The enveloped companion, here a neutron star, begins to accrete matter. The angular momentum of the accreting material results in the formation of an accretion disk. Accretion of hydrogen rich onto common-envelope-phase neutron stars can result in material ejected from the accretion disk having undergone burning near the neutron star’s surface [1]. Not much is understood about what nucleosynthesis occurs in this system. However, Keegans (2019) found that accreting neutron star common envelopes have the potential to impact galactic chemical evolution (GCE) [1].
Our preliminary results show that this astrophysical scenario can produce large amounts of light p-nuclides 92Mo, 96Ru and 98Ru – upwards of one order of magnitude more than their initial abundances in our simulations. This is significant as these isotopes are all underproduced in current p-process models and their origins are not known [2, 3].
The presented work builds on Keegans et al. (2019), which modelled accreting neutron star common envelopes without the inclusion of angular momentum, and Abrahams et al. (2023), which presented initial results on updated models which included the impact of angular momentum [1,4]. We will present yields from our common envelope simulations and discuss the nucleosynthesis which leads to high production of particular light p-nuclides.
[1] Keegans J., Fryer C.L., Jones S.W., Côte B., Belczynski K., Herwig F., Pignatari M., et al., 2019, MNRAS, 485, 620. doi:10.1093/mnras/stz368
[2] Roberti L., Pignatari M., Psaltis A., Sieverding A., Mohr P., Fulop Z., Lugaro M., 2023, A&A, 677, A22. doi:10.1051/0004-6361/202346556
[3] Role of Core-collapse Supernovae in Explaining Solar System Abundances of p Nuclides, C. Travaglio, T. Rauscher, A. Heger, M. Pignatari, C. West
[4] Abrahams S.E.D., Fryer C., Hall-Smith A., Laird A., Diget C., 2023, EPJWC, 279, 10002. doi:10.1051/epjconf/202327910002
The first stars can be constrained by the chemical composition of distant galaxies. It is crucial to understand how and when the first stars formed to understand the formation and evolution of our Universe. The latest observational data reveal unprecedented information about the chemical enrichment of the early Universe, which seems to behave differently from the local Universe. The first stars, being very massive, enrich their metal-poor environment in an uncertain way. To predict the abundances of the first galaxies, we include nucleosynthesis yields from Population III stars up to 300Msun, including faint supernovae, Wolf Rayet and Pair Instability Supernovae into our state-of-the-art hydrodynamical cosmological simulations. Our code (based on Gadget-3) also includes the latest nucleosynthesis yields from population II stars (from Kobayashi et al. 2020) for all stellar mass ranges. We predict the chemical abundance evolution of galaxies for different elements from the early Universe to the local Universe. For example, we find that the N/O abundance gives a systematically larger value with nucleosynthesis yields from Population III stars, which is comparable with observational data of the GN-z11 galaxy. I also discuss the evolution of metallicity gradients and elemental abundances of the intergalactic medium. We constrain our model by comparing it with observational data from the James Web Space Telescope (JWST) and the Atacama Large Millimeter/submillimeter Array (ALMA).
The properties of the first (Pop. III) stars remain a mystery. The chemistry of relic environments, enriched only by the supernovae of these first stars, offer an exciting avenue to study this population. Stellar relics are often found in the local Universe while gaseous relics probe the chemistry of low density structures at earlier epochs (z>2). I will discuss the complementary nature of these searches and how they can be used together to understand early chemical evolution and structure formation. Particularly, I will focus on the most metal-poor DLAs found at z~3 and the associated high-precision abundance determinations. This will include an updated view, provided by new data, on both the [O/Fe] enhancement seen at the lowest metallicities and the 12C/13C isotope ratio. Uniquely, this isotope ratio can be used to probe the existence of low-mass (i.e. 1M_sun) Pop III stars and the enrichment timescale of these near-pristine DLAs.
When asked to make therapeutic antimicrobial decisions on clinical rotations, veterinary students will often hesitate. Given that there are frequently several possible options and not always a defined “correct or incorrect” choice when it comes to antimicrobial selections, we sought to offer students more opportunities to practice decision making in a continuum. We developed a series of small animal case vignettes with a continuum of pharmacologic choices. Rather than making a discrete drug selection on a multiple-choice exam, students use a slider bar tool to indicate the relative safety and/or efficacy of the drug in question; they also provide a written justification for their selection. Slider bar selections and justifications are anonymized and downloaded into a corresponding file for instructor review. Student participation engenders real time feedback for instructors, which shapes key learning goals for class sessions and addresses misconceptions. An example of the case used to modify a teaching approach in the classroom highlights how the tool provides insight into students’ mindset. The case detailed an intact male dog with clinical signs of a urinary tract infection and supportive urinalysis data. Students evaluated the safety and efficacy of five different drugs. Most of the students deemed the aminopenicillin or potentiated aminopenicillin as safe and effective for use in this patient; most missed the signalment in the case and ignored the role of the prostate. The case sharpened the accompanying class discussion on the significance of antimicrobial selection in an intact male dog with a urinary tract infection.
In the pursuit of environmental sustainability, the development of energy-efficient artificial intelligence (AI) models becomes imperative. This work presents a novel approach to bioacoustic monitoring using self-supervised learning (SSL) of bird sound representations. We employ MobileNetV3, a lightweight deep learning model designed to focus on low computational cost while maintaining high performance. Our method leverages the capability of SSL to exploit unlabeled data, significantly reducing the dependency on extensive labelled datasets that is costly. By choosing appropriate data augmentation techniques to train MobileNetV3 in a self-supervised manner, we extract informative features from the data. These features achieve robust few-shot learning capabilities, enabling accurate bird species recognition with one labeled example per class. The application of such a lightweight model not only mitigates the environmental impact associated with the training of large-scale AI models but also enhances the feasibility of deploying AI solutions in resource-constrained environments. In future work, we aim to extend the application of our method to the large bird sound collection Xeno-Canto, which contains recordings from over 10,000 species. This scalability will allow us to learn more robust features, potentially enhancing the generalizability and effectiveness of our model across a broader spectrum of bioacoustic challenges. Additionally, by employing model compression techniques such as distillation, we can further reduce the model size, enabling the development of an ultra-compact model that is well-suited for deployment on small devices.
As the world transitions towards sustainable energy sources to combat climate change, wind power has emerged as a promising renewable alternative. The growing demand for wind energy generation has significant interest in developing accurate wind speed and energy forecasting models. Reliable forecasting is crucial for optimizing the planning and operation of wind power plants, enabling the seamless integration of this clean and sustainable energy source into the grid. While numerous models have been proposed in the past to predict wind speed and energy, their performance has been hindered by the inherent non-linearity and irregularity of wind patterns. To address this challenge and facilitate the widespread adoption of sustainable wind power, this research introduces a Novel Modular Red Deer Neural System (MRDNS) designed to effectively forecast wind speed and energy. The MRDNS harnesses data from wind turbine SCADA databases, which undergoes pre-processing to eliminate training flaws. Relevant features are then extracted, reducing the complexity of the prediction process. By analysing these features, the MRDNS employs a fitness function to predict wind speed and energy with enhanced accuracy, supporting the efficient utilization of this renewable resource. The model achieved remarkable performance, boasting a prediction accuracy of 99.99% for wind power forecasting, with an MSE of 0.0017 and an RMSE of 0.0422. For wind speed forecasting, the model yielded an MSE of 0.0003 and an RMSE of 0.0174.
Sensor-based remote healthcare monitoring offers a sustainable solution for detecting adverse health events in individuals with long-term conditions, directly in their homes. Traditional anomaly detection methods in noisy, multivariate real-world data often require large labelled datasets, complex AI models, extensive hyperparameter tuning, and frequent retraining to address data drift, limiting their scalability and explainability. Inspired by the simplicity and success of negative sample-free contrastive learning in computer vision, we propose a resource-efficient, self-supervised model that adapts to noise to improve anomaly detection. Our model has outperformed similar algorithms in detecting agitation and fall events in a real-world study of dementia patients. We enhanced model transparency through a ‘spatiotemporal attention map’ that pinpoints anomalies, fostering user trust and encouraging broader adoption. Our scalable, domain-agnostic solution can be applied across diverse healthcare, industrial, and urban environments, aligning with sustainable development goals, particularly in low-resource settings.
Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on resource-constrained devices such as embedded systems. In this paper, we analyze how the performance of large-scale pre-trained audio neural networks designed for audio pattern recognition changes when deployed on a hardware such as a Raspberry Pi. We empirically study the role of CPU temperature, microphone quality and audio signal volume on performance. Our experiments reveal that the continuous CPU usage results in an increased temperature that can trigger an automated slowdown mechanism in the Raspberry Pi, impacting inference latency. The quality of a microphone, specifically with affordable devices such as the Google AIY Voice Kit, and audio signal volume, all affect the system performance. In the course of our investigation, we encounter substantial complications linked to library compatibility and the unique processor architecture requirements of the Raspberry Pi, making the process less straightforward compared to conventional computers (PCs). Our observations, while presenting challenges, pave the way for future researchers to develop more compact machine learning models, design heat-dissipative hardware, and select appropriate microphones when AI models are deployed for real-time applications on edge devices.
Convolutional neural networks (CNNs) are commonplace in
high-performing solutions to many real-world problems, such as
audio classification. CNNs have many parameters and filters,
with some having a larger impact on the performance than others.
This means that networks may contain many unnecessary filters,
increasing a CNN’s computation and memory requirements while
providing limited performance benefits. To make CNNs more
efficient, we propose a pruning framework that eliminates filters
with the highest “commonality”. We measure this commonality
using the graph-theoretic concept of centrality. We hypothesise
that a filter with a high centrality should be eliminated as it
represents commonality and can be replaced by other filters without
affecting the performance of a network much. An experimental
evaluation of the proposed framework is performed on acoustic
scene classification and audio tagging. On the DCASE 2021 Task
1A baseline network, our proposed method reduces computations
per inference by 71% with 50% fewer parameters at less than a
two percentage point drop in accuracy compared to the original
network. For large-scale CNNs such as PANNs designed for audio
tagging, our method reduces 24% computations per inference with
41% fewer parameters at a slight improvement in performance.