Session HH00: V: Poster Session II (5:30PM - 7:30PM PT)

Principal Component Analysis of X-ray Variations in the Hard X-ray Emitting Symbiotic Binary RT Cru

Hard X-ray-emitting Symbiotic binaries consist of an accreting degenerate core and a cool giant star, which produce hard X-ray emission above 3 keV. The Suzaku telescope has observed a spectrum transition in the hard X-ray-emitting symbiotic binary RT Cru from 2007 to a fainter/harder state in 2012, as well as sporadic hourly fluctuations in X-rays seen by both the Suzaku and XMM-Newton missions. To investigate the characteristics of these X-ray variations, we employ principal component analysis (PCA) to evaluate the multi-mission X-ray data. This also allows us to identify the spectral components that contribute to the occurrence of hourly X-ray brightening. The Suzaku PCA study reveals three main components: an absorbing column (50%), a continuum (20%), and a likely soft thermal emission (9%). Moreover, the XMM-Newton data analysis yields the PCA component, which may include some emission features, particularly in the soft excess. Our findings suggest that the change in X-ray state seen by the Suzaku telescope between 2007 and 2012 was mostly caused by changes in the absorbing materials and some in the X-ray continuum. In the soft excess, there may also be a faint collisionally-ionized thermal emission with a temperature of 1 keV, which is likely from a colliding wind.   

Simulation-Based Optimization of IceCube-Gen2 Modules for Enhanced Sensitivity to Neutrinos from Galactic Core-Collapse Supernovae

The IceCube Neutrino Observatory is capable of detecting high-energy astrophysical neutrinos as well as bursts of MeV neutrinos from core-collapse supernovae (CCSNe). The IceCube-Gen2 will encompass nearly tenfold the volume of its predecessor, incorporating cutting-edge multi-PMT Digital Optical Modules (mDOMs) and Wavelength-shifting Optical Modules (WOMs) presently undergoing development and testing. The design of the new modules will have a significant impact on the sensitivity of IceCube to supernova neutrinos. To gauge sensitivity and refine sensor design, we devised a high-fidelity simulation in GEANT4, focusing on mDOMs and WOMs, accounting for depth-dependent ice properties enveloping the modules. The simulation allows for the direct injection of signal events, encompassing supernova neutrino flux with varying progenitor masses, as well as background events stemming from the radioactive decay of trace elements. Leveraging the mDOM simulation, we studied local coincidence in detected neutrinos and used "coincidence cuts" to attenuate background events and minimize the false detection rate of galactic CCSNe. Additionally, the WOM simulation yielded promising results by shifting Cherenkov radiation from the UV to the visible range, where the detectors exhibit peak sensitivity. Consequently, we anticipate WOMs to be more sensitive to supernova neutrinos compared to existing detectors in IceCube. These simulations can play a pivotal role in the optimization of neutrino detectors in IceCube-Gen2, enhancing their sensitivity to MeV neutrino bursts from CCSNe.   

Dust in High-Redshift Galaxies: Reconciling UV Attenuation and IR Emission

Dust is a key component of galaxies, but its properties during the earliest eras of structure formation remain elusive. Here we present a simple semi-analytic model of the dust distribution in galaxies at $z \gtrsim 5$. We calibrate the free parameters of this model to estimates of the UV attenuation (using the IRX-$\beta$ relation between infrared emission and the UV spectral slope) and to ALMA measurements of dust emission. We find that the observed dust emission requires that most of the dust expected in these galaxies is retained (assuming a similar yield to lower-redshift sources), but if the dust is spherically distributed, the modest attenuation requires that it be significantly more extended than the stars. Interestingly, the retention fraction is larger for less massive galaxies in our model. However, the required radius is a significant fraction of the host's virial radius and is larger than the estimated extent of dust emission from stacked high-$z$ galaxies. These can be reconciled if the dust is distributed anisotropically, with typical covering fractions of $\sim 0.2$--0.7 in bright galaxies and $\lesssim 0.1$ in fainter ones.   

Gravitational Flux Line Redistribution Across a Critical Field Strength to Interpret the Structural-Dynamical Relations of Galaxies

The structural-dynamical relations of disk galaxies revealed by the radial acceleration relation of SPARC data (Spitzer Photometry and Accurate Rotation Curves) have been interpreted by a "gravitational field flux re-distribution" within a generalized integral Gauss's law of gravity (Reference). In such a non-relativistic picture, the flat rotation curves and the Baryonic Tully Fisher Relation have been shown as due to the re-distribution of the conserved gravitational lines of force from a spherical symmetry to cylindrical one, across a universal critical density. It is discussed in the present report that the "M ∝ v^4" Baryonic Tully-Fisher relation of disk galaxies as well as the Faber-Jackson relation for the central velocity dispersion of elliptical galaxies can be simply realized by a transition off the Newtonian 1/r^2 gravity across a universal field strength, approximately 10^-10 N/Kg, equivalent to the universal acceleration 10^-10 m/s/s. This transition occurred spatially between the core and the outskirts of disk galaxies. Such a spatial re-distribution of gravitational flux may give a geometric mapping between the generalized Gaussian surface of the field flux distribution and the real patterns of objects in various astronomical scales. Furthermore, the close relation of gravitational matter and fields, Newtonian as well as non-Newtonian, may provide an extendable mechanism to explain the observation of close relation between normal matter and dark matter and to bridge the gap between MOND and dark matter modeling. Discussions are to be make in the disk thickness variable in the cylindrical Gaussian surface, extending the flux distribution model to the elliptical galaxies and other structure and scales. Implications of the temporal evolution of the cylindrical to spherical flux line distribution is also discussed.   

Using the Principle of Equivalence in Thrust Capacitor Technology

In 1921, Theodor Kaluza discovered that Maxwell’s equations could be derived from Einstein’s equations if a fifth dimension was added to the usual four, provided that the size of the fifth dimension was on the order of the Planck length. In effect, Kaluza related electromagnetic and gravitational forces at the Planck length. There is another way to relate electromagnetic and gravitational forces using the principle-of-equivalence in thrust capacitor technology. Inertia becomes a local interaction with a huge vacuum energy that causes dark energy. This requires zero-active-mass cosmology to separate momentum from inertia.

Thrust capacitor technology is described by the manufacturer as using an interaction with the thermal Unruh vacuum to produce thrust. The ratio of Larmor photons, to Unruh photon rate in a thermal bath, is 4πΔτ. Where Δτ is the local proper time that electrons are accelerated. The technology creates hotter Unruh photons in front of the accelerating electrons, that forward scatter into Larmor photons, that further accelerate the already accelerating electrons. The Unruh bath has a Tolman temperature gradient.

Here we describe gravitational potential binding-energy in quantum SU(2)R pre-gravity. Electron wavepackets are in 8-dimensional octonion space. The thrust capacitor technology Landauer scatters electron wavepackets in the capacitor insulator. A backreacting Planck mass produces gravitational freefall kinetic energy by emitting a Lorentz boson into the quantum vacuum.   

Inferring Multicomponent Properties Through Thermal Lens

Experimental detection of molecular diffusion in multi-component mixtures is of prime importance for much of the physical sciences. Light matter interactions are always associated with residual heating in the sample. We demonstrate that using non-linear optical processes, we can infer diffusive properties of molecules in complexes. In turn, with this information coupled with a suitable machine learning model and additional (known) molecular data, we are able to form correlational models for phenomenological properties. As a concrete example, we show how the elusive human interpretation of "scent" can be computed through this novel theoretical model with our experimental data.   

Topological analysis of scroll wave dynamics in higher-dimensional stochastic excitable systems

Noise is inevitable in excitable reaction-diffusion systems and could affect the instability of spiral or scroll waves in low (two or three) space dimensions. This computational study explores how noise affects the scroll wave dynamics in excitable media with higher space dimensions. Numerical simulations are performed using the stochastic FitzHugh-Nagumo system with N-space dimensions (N≥4). To examine the scroll wave dynamics, algebraic topological features of excitation patterns are analyzed using the homology method. In N-dimensional excitable media, high-dimensional scroll waves rotating around (N-2)-dimensional manifolds exist. As the noise intensity level increases, the frequency of scroll-wave breakup increases, and the Betti numbers for excitation regions increase. The noise-induced scroll-wave breakup is reduced in the presence of localized non-conductive regions. The numerical simulations and homology analysis could successfully reveal noise-induced scroll wave patterns in high-dimensional reaction-diffusion systems.   

Algorithmic Complexity as the Most Appropriate Metric to Estimate the Randomness of Objects

The act of explaining what our eyes see has always been a human need. The act of trying to understand reality, for which some have called science, common sense, etc., is nothing more than classifying randomness. That is, when one discovers the mechanism of operation of a phenomenon, what we have done is finding that this phenomenon is not random, that it has a cause and we have found it. To do this, the phenomenon is abstracted to a chain of symbols and we call that "the object."

However, measuring randomness is a challenge, there are several metrics used in the scientific community such as Shannon entropy (in the field of Information Theory), compression methods, etc.; but the one that has proven to be the best metric is the one used in Algorithmic Complexity (AC), and in particular the methodology used by Professor Hector Zenil that is used in this work. The idea is that to evaluate the content of an object -which can be identified with a generating algorithm and a causal mechanism, CA provides us with a precise criterion to decide whether an object is random or not. And as in science, we can find a shorter description than the phenomenon itself.

In this work, the estimation of the randomness of several objects is compared, such as the Thue-Morse sequence, structured binary chains and some representations of physical phenomena, where the effectiveness of AC against other metrics to classify the randomness of an object is demonstrated. This work also offers a new paradigm to the scientific community by presenting the underlying formalism for using the tools that Dr. Zenil provides, and applying the CA methodology to any area of science that requires measuring randomness.   

Optimizing Computationally-Intensive Simulations Using a Biologically-Inspired Acquisition Function and a Fourier Neural Operator Surrogate

Computational modeling of physical phenomena has enabled researchers to acquire insight that was previously only observable from costly real-world experiments. The adoption of physics simulations has resulted in numerous advancements in fusion energy, seismic inversion/monitoring, and national defense. However, optimizing simulation studies requires many realizations of the intensive simulations. Manually tweaking control parameters and searching for optimal results can be tedious and inefficient. To tackle such obstacles in simulation studies, we found that differential evolution combined with the covariance matrix adaptation strategy could effectively optimize simulations while simultaneously behaving as an acquisition function to collect samples. The samples collected may be used to train intelligent surrogate models such as a Fourier neural operator (FNO). Once a surrogate is constructed, it could be used to accelerate the sampling of the optimization search space further. This methodology effectively optimized a hydrodynamic simulation modeled by systems of partial differential equations; it may also be extended to simulation optimization in other disciplines.   

Submission ID 2242235

Submission ID 2242235

Motion Faster than Speed of Light by Superbeings