Abstracts of Interest
Selected by:
Fedor Tairli
Abstract: 2502.14990
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Title:The Dynamical State of the Didymos System Before and After the DART Impact
View PDF HTML (experimental)Abstract:NASA's Double Asteroid Redirection Test (DART) spacecraft impacted Dimorphos, the natural satellite of (65803) Didymos, on 2022 September 26, as a first successful test of kinetic impactor technology for deflecting a potentially hazardous object in space. The experiment resulted in a small change to the dynamical state of the Didymos system consistent with expectations and Level 1 mission requirements. In the pre-encounter paper Richardson (2022), predictions were put forward regarding the pre- and post-impact dynamical state of the Didymos system. Here we assess these predictions, update preliminary findings published after the impact, report on new findings related to dynamics, and provide implications for ESA's Hera mission to Didymos, scheduled for launch in 2024 with arrival in late December 2026. Pre-encounter predictions tested to date are largely in line with observations, despite the unexpected, flattened appearance of Didymos compared to the radar model and the apparent pre-impact oblate shape of Dimorphos (with implications for the origin of the system that remain under investigation). New findings include that Dimorphos likely became prolate due to the impact and may have entered a tumbling rotation state. A possible detection of a post-impact transient secular decrease in the binary orbital period suggests possible dynamical coupling with persistent ejecta. Timescales for damping of any tumbling and clearing of any debris are uncertain. The largest uncertainty in the momentum transfer enhancement factor of the DART impact remains the mass of Dimorphos, which will be resolved by the Hera mission.
Abstract: 2502.15875
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Title:EMBER-2: Emulating baryons from dark matter across cosmic time with deep modulation networks
View PDF HTML (experimental)Abstract:Galaxy formation is a complex problem that connects large scale cosmology with small scale astrophysics over cosmic timescales. Hydrodynamical simulations are the most principled approach to model galaxy formation, but have large computational costs. Recently, emulation techniques based on Convolutional Neural Networks (CNNs) have been proposed to predict baryonic properties directly from dark matter simulations. The advantage of these emulators is their ability to capture relevant correlations, but at a fraction of the computational cost compared to simulations. However, training basic CNNs over large redshift ranges is challenging, due to the increasing non-linear interplay between dark matter and baryons paired with the memory inefficiency of CNNs. This work introduces EMBER-2, an improved version of the EMBER (EMulating Baryonic EnRichment) framework, to simultaneously emulate multiple baryon channels including gas density, velocity, temperature and HI density over a large redshift range, from z=6 to z=0. EMBER-2 incorporates a context-based styling network paired with Modulated Convolutions for fast, accurate and memory efficient emulation capable of interpolating the entire redshift range with a single CNN. Although EMBER-2 uses fewer than 1/6 the number of trainable parameters than the previous version, the model improves in every tested summary metric including gas mass conservation and cross-correlation coefficients. The EMBER-2 framework builds the foundation to produce mock catalogues of field level data and derived summary statistics that can directly be incorporated in future analysis pipelines. We release the source code at the official website this https URL.
Abstract: 2502.15876
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Title:The Galactic Magnetic Field and UHECR Deflections
View PDF HTML (experimental)Abstract:Ultrahigh-energy cosmic rays (UHECRs) experience deflections as they traverse the Galactic magnetic field (GMF), which must be accounted for when tracing them back to their sources. After briefly summarizing our results on uncertainties in cosmic-ray deflections from the UF23 ensemble of GMF models (Unger & Farrar, 2024), we report a new preliminary fit of the GMF including foreground emission from the Local Bubble. This fit uses the analytic model of Pelgrims et al. (2024) for the magnetic field in the thick shell of Galactic bubbles. We also discuss how variations in toroidal halo field modeling account for the key differences between the Jansson & Farrar (2012) GMF model and the UF23 ensemble.
Furthermore, we extend our previous analysis of the origin of the highest-energy "Amaterasu" event observed by the Telescope Array to include the four highest-energy events detected by the Pierre Auger Observatory. Amaterasu and PAO070114 are the UHECR events with the smallest localization uncertainties of 4.7% and 2.4%, respectively. Neither of their back-tracked directions aligns with any compelling candidate for a continuous UHECR accelerator. This strengthens the evidence that at least a fraction of the highest energy events originate from transient sources.
Abstract: 2502.16603
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Title:Galaxy mergers classification using CNNs trained on Sérsic models, residuals and raw images
View PDF HTML (experimental)Abstract:Galaxy mergers are crucial for understanding galaxy evolution, and with large upcoming datasets, automated methods, such as Convolutional Neural Networks (CNNs), are needed for efficient detection. It is understood that these networks work by identifying deviations from the regular, expected shapes of galaxies, which are indicative of a merger event. Using images from the IllustrisTNG simulations, we aim to check the importance of faint features, source position and shape information present in galaxy merger images on the performance of a CNN merger vs. non-merger classifier. We fit Sérsic profiles to each galaxy in mock images from the IllustrisTNG simulations. We subtract the profiles from the original images to create residual images, and we train three identical CNNs on three different datasets -- original images (CNN1), model images (CNN2), and residual images (CNN3). We found that it is possible to conduct galaxy merger classification based only on faint features, source position and shape information present in residual images and model images, respectively. The results show that the CNN1 correctly classifies 74% of images, while CNN2 70%, and CNN3 68%. Source position and shape information is crucial for pre-merger classification, while residual features are important for post-merger classification. CNN3 classifies post-mergers in the latest merger stage the best out of all three classifiers.
Abstract: 2502.16807
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Title:Astronomical image denoising by self-supervised deep learning and restoration processes
View PDF HTML (experimental)Abstract:Image denoising based on deep learning has witnessed significant advancements in recent years. However, existing deep learning methods lack quantitative control of the deviation or error on denoised images. The neural networks Self2Self is designed for denoising single-image, training on it and denoising itself, during which training is costly. In this work we explore training Self2Self on an astronomical image and denoising other images of the same kind, which is suitable for quickly denoising massive images in astronomy. To address the deviation issue, the abnormal pixels whose deviation exceeds a predefined threshold are restored to their initial values. The noise reduction includes training, denoising, restoring and named TDR-method, by which the noise level of the solar magnetograms is improved from about 8 G to 2 G. Furthermore, the TDR-method is applied to galaxy images from the Hubble Space Telescope and makes weak galaxy structures become much clearer. This capability of enhancing weak signals makes the TDR-method applicable in various disciplines.
Abstract: 2502.19950
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Title:Science Potential and Technical Design of the IceCube-Gen2 Surface Array (UHECR 2024)
View PDF HTML (experimental)Abstract:IceCube-Gen2, the next generation extension of the IceCube Neutrino Observatory at the South Pole, offers a unique scientific potential for cosmic-ray physics at PeV to EeV energies complementing the main science case of neutrino astronomy. The cosmic-ray science case will be enabled by a surface array on top of an extended optical array deep in the polar ice. The optical array measures TeV muons of air showers, and the surface array primarily measures the electromagnetic shower component and low-energy muons. The design of the surface array foresees scintillation panels providing a full-efficiency threshold for near-vertical proton showers of 0.5 PeV and radio antennas increasing the measurement accuracy for the electromagnetic shower component in the energy range of the Galactic-to-extragalactic transition. Compared to IceCube, the aperture for air showers measured in coincidence with the surface and optical arrays will increase by a factor of 30, due to the larger area and angular acceptance in zenith angle. The science potential includes both, the particle physics of air showers, such as prompt muons, and the astrophysics of the highest energy Galactic cosmic-rays, enabled by the higher sensitivity for the mass composition and anisotropy of cosmic rays, and by the search for PeV photons. This proceeding summarizes the science case and design of the surface array as presented in the recently released IceCube-Gen2 Technical Design Report: this https URL
Abstract: 2502.19969
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Title:Radio Detection of ultra-high-energy Cosmic-Ray Air Showers
View PDF HTML (experimental)Abstract:Radio antennas have become a standard tool for the detection of cosmic-ray air showers in the energy range above $10^{16}\,$eV. The radio signal of these air showers is generated mostly due to the deflection of electrons and positrons in the geomagnetic field, and contains information about the energy and the depth of the maximum of the air showers. Unlike the traditional air-Cherenkov and air-fluorescence techniques for the electromagnetic shower component, radio detection is not restricted to clear nights, and recent experiments have demonstrated that the measurement accuracy can compete with these traditional techniques. Numerous particle detector arrays for air showers have thus been or will be complemented by radio antennas. In particular when combined with muon detectors, the complementary information provided by the radio antennas can enhance the total accuracy for the arrival direction, energy and mass of the primary cosmic rays. Digitization and computational techniques have been crucial for this recent progress, and radio detection will play an important role in next-generation experiments for ultra-high-energy cosmic rays. Moreover, stand-alone radio experiments are under development and will search for ultra-high-energy photons and neutrinos in addition to cosmic rays. This article provides a brief introduction to the physics of the radio emission of air showers, an overview of air-shower observatories using radio antennas, and highlights some of their recent results.
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