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A Bayesian Exploration of the Mass of Ursa Major III: Kinematics, Rotation and their influence on the Mass to Light Ratio
Authors:
T. R. Adams,
B. J. Brewer,
G. F. Lewis
Abstract:
We investigate the kinematics of the potential ultra-faint dwarf galaxy (UFD) UMa III/U1 using Bayesian inference to search for the signal of any potential intrinsic rotation. The magnitude of rotation is relevant to estimating the total mass of UMa III/U1, which is critical in determining whether or not UMa III/U1 is in fact a UFD, or possibly a star cluster home to a significant binary fraction.…
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We investigate the kinematics of the potential ultra-faint dwarf galaxy (UFD) UMa III/U1 using Bayesian inference to search for the signal of any potential intrinsic rotation. The magnitude of rotation is relevant to estimating the total mass of UMa III/U1, which is critical in determining whether or not UMa III/U1 is in fact a UFD, or possibly a star cluster home to a significant binary fraction. A non-rotating model and a rotational model are fitted for the current total population of member stars of UMa III/U1, finding that a non-rotating model was preferred by a factor of $\sim 5-12 \times$. This was repeated on a reduced population of UMa III/U1, where potential contaminant stars were removed. A similar preference for non-rotation was found for these reduced populations. We calculate a lower-bound rotational mass estimate for UMa III/U1 and a corresponding lower bound mass-to-light ratio of $ 734.4^{+339.0}_{-176.2} \mathrm{M_\odot} / \mathrm{L_\odot} $ for the total population. We conclude that UMa III/U1 still remains an ambiguous object with viable arguments for both the UFD and self-gravitating star cluster scenarios, however under both, UMa III/U1 is unlikely to be supported by rotational pressure.
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Submitted 19 February, 2026;
originally announced February 2026.
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Numerical generation of vector potentials from specified magnetic fields
Authors:
Zachary J. Silberman,
Thomas R. Adams,
Joshua A. Faber,
Zachariah B. Etienne,
Ian Ruchlin
Abstract:
Many codes have been developed to study highly relativistic, magnetized flows around and inside compact objects. Depending on the adopted formalism, some of these codes evolve the vector potential $\mathbf{A}$, and others evolve the magnetic field $\mathbf{B}=\nabla\times\mathbf{A}$ directly. Given that these codes possess unique strengths, sometimes it is desirable to start a simulation using a c…
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Many codes have been developed to study highly relativistic, magnetized flows around and inside compact objects. Depending on the adopted formalism, some of these codes evolve the vector potential $\mathbf{A}$, and others evolve the magnetic field $\mathbf{B}=\nabla\times\mathbf{A}$ directly. Given that these codes possess unique strengths, sometimes it is desirable to start a simulation using a code that evolves $\mathbf{B}$ and complete it using a code that evolves $\mathbf{A}$. Thus transferring the data from one code to another would require an inverse curl algorithm. This paper describes two new inverse curl techniques in the context of Cartesian numerical grids: a cell-by-cell method, which scales approximately linearly with the numerical grid, and a global linear algebra approach, which has worse scaling properties but is generally more robust (e.g., in the context of a magnetic field possessing some nonzero divergence). We demonstrate these algorithms successfully generate smooth vector potential configurations in challenging special and general relativistic contexts.
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Submitted 27 March, 2018;
originally announced March 2018.
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Improving performance of SEOBNRv3 by $\sim$300x
Authors:
Tyler D. Knowles,
Caleb Devine,
David A. Buch,
Serdar A. Bilgili,
Thomas R. Adams,
Zachariah B. Etienne,
Sean T. McWilliams
Abstract:
When a gravitational wave is detected by Advanced LIGO/Virgo, sophisticated parameter estimation (PE) pipelines spring into action. These pipelines leverage approximants to generate large numbers of theoretical gravitational waveform predictions to characterize the detected signal. One of the most accurate and physically comprehensive classes of approximants in wide use is the "Spinning Effective…
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When a gravitational wave is detected by Advanced LIGO/Virgo, sophisticated parameter estimation (PE) pipelines spring into action. These pipelines leverage approximants to generate large numbers of theoretical gravitational waveform predictions to characterize the detected signal. One of the most accurate and physically comprehensive classes of approximants in wide use is the "Spinning Effective One Body--Numerical Relativity" (SEOBNR) family. Waveform generation with these approximants can be computationally expensive, which has limited their usefulness in multiple data analysis contexts. In prior work we improved the performance of the aligned-spin approximant SEOBNR version 2 (v2) by nearly 300x. In this work we focus on optimizing the full eight-dimensional, precessing approximant SEOBNR version 3 (v3). While several v2 optimizations were implemented during its development, v3 is far too slow for use in state-of-the-art source characterization efforts for long-inspiral detections. Completion of a PE run after such a detection could take centuries to complete using v3. Here we develop and implement a host of optimizations for v3, calling the optimized approximant v3_Opt. Our optimized approximant is about 340x faster than v3, and generates waveforms that are numerically indistinguishable.
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Submitted 2 July, 2018; v1 submitted 16 March, 2018;
originally announced March 2018.