Using Milkyway@home to measure the mass of the orphan-chenab stream progenitor dwarf galaxy

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Mendelsohn, Eric J.
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We fit the mass and radial profile of the Orphan-Chenab Stream's (OCS) dwarf galaxy progenitor by using turnoff stars in the Sloan Digital Sky Survey (SDSS) and the Dark Energy Camera (DEC) to constrain N-body simulations of the OCS progenitor falling into the Milky Way on the 1.5 PetaFLOPS MilkyWay@home distributed supercomputer. We infer the internal structure of the OCS's progenitor under the assumption that it was a spherically symmetric dwarf galaxy comprised of a stellar system embedded in an extended dark matter halo. We optimize the evolution time, the baryonic and dark matter scale radii, and the baryonic and dark matter masses of the progenitor using a differential evolution algorithm. The likelihood score for each set of parameters is determined by comparing the simulated tidal stream to the angular distribution of OCS stars observed in the sky. We fit the total mass of the OCS's progenitor to ($2.0\pm0.3$) $\times 10^7 M_\odot$ with a mass-to-light ratio of $\gamma=73.5\pm10.6$ and ($1.1\pm0.2$)$\times10^6M_{\odot}$ within 300 pc of its center. Within the progenitor's half-light radius, we estimate a total mass of ($4.0\pm1.0$)$\times10^5M_{\odot}$. We also fit the current sky position of the progenitor's remnant to be $(\alpha,\delta)=((166.0\pm0.9)^\circ,(-11.1\pm2.5)^\circ)$ and show that it is gravitationally unbound at the present time. The measured progenitor mass is on the low end of previous measurements, and if confirmed lowers the mass range of ultrafaint dwarf galaxies. Our optimization assumes a fixed Milky Way potential, OCS orbit, and radial profile for the progenitor, ignoring the impact of the Large Magellanic Cloud (LMC). Using second-order forward automatic differentiation, we also attempt to computationally determine the systematic errors introduced from the fixed orbit, gravitational potential, and lack of an LMC. This paper describes the methods employed to implement automatic differentiation in areas of our code where derivative information is not propagated, such as through random number generation and discrete histogram binning. We find that due to the turbulent and chaotic behavior of our searchable likelihood surface, the systematic errors derived through automatic differentiation are severely overestimated. Recommendations for future work to estimate the systematic errors are provided.
August 2022
School of Science
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Rensselaer Polytechnic Institute, Troy, NY
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