arXiv subjects

Eric Putney

Publications and source records attributed to Eric Putney.

5 recordsLinked to original sources

arXiv2025

ClearPotential: Revealing Local Dark Matter in Three Dimensions

We present ClearPotential, a data-driven, three-dimensional measurement of the gravitational potential of the local Milky Way using unsupervised machine learning, without the symmetry assumptions, specific functional forms, and binning required in previous work. The potential is modeled as a neural network, optimized to solve the equilibrium collisionless Boltzmann equation for the observed phase space density of Gaia DR3 Red Clump stars within 4 kpc of the Sun. This density is obtained from data using normalizing flows, and our unsupervised solution to the Boltzmann equation automatically corrects for selection effects from crowding and the dust-driven extinction of starlight. Our fully-differentiable model of the gravitational potential allows us to map the acceleration and mass density of the Galaxy in the volume around the Sun, including in the dust-obscured disk towards the Galactic Center. We determine the dark matter density at the Solar radius to be $(0.84 \pm 0.08)\times 10^{-2}\,{M}_\odot/{\rm pc}^3$, and analyze the structure of the dark matter halo. We find strong evidence for a tilted oblate halo, weak preference for a cored inner profile, and the strongest constraints to date on a possible dark matter disk. We place a bound on the timescale of disequilibrium in the local Milky Way, and find mild evidence for disequilibrium using independent acceleration measurements from timings of binary pulsar systems. This work provides the clearest map of the local Galactic potential to date and marks an important step in the era of data-driven astrometry.

astro-ph.GA↗

arXiv2024

Sweeping the Dust Away -- Correcting the Phase Space Density of the Milky Way with Unsupervised Machine Learning

The Boltzmann equation relates the equilibrium phase space distribution of stars in the Milky Way to the Galaxy's gravitational potential. However, observations of stellar populations are biased by extinction from foreground dust, which complicates measurements of the potential in the disk and towards the Galactic center. Using the kinematics of Red Clump and Red Branch stars in Gaia DR3, we use machine learning to simultaneously estimate both the unbiased stellar phase space density and the gravitational potential. The unbiased phase space density is obtained through a learned "dust efficiency factor" -- an observational selection function that accounts for dust extinction. The potential and the dust efficiency are parameterized by fully connected neural networks and are completely data driven. We validate the dust efficiency using a recent three-dimensional dust map in this work, and examine the potential in a companion paper.

astro-ph.GA↗

arXiv2023

Mapping Dark Matter in the Milky Way using Normalizing Flows and Gaia DR3

We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the underlying phase space distribution of 6 million nearby stars from the Gaia DR3 catalog. Solving the equilibrium collisionless Boltzmann equation, we calculate -- for the first time ever -- a model-free, unbinned estimate of the local acceleration and mass density fields within a 3 kpc sphere around the Sun. As our approach makes no assumptions about symmetries, we can test for signs of disequilibrium in our results. We find our results are consistent with equilibrium at the 10% level, limited by the current precision of the normalizing flows. After subtracting the known contribution of stars and gas from the calculated mass density, we find clear evidence for dark matter throughout the analyzed volume. Assuming spherical symmetry and averaging mass density measurements, we find a local dark matter density of $0.47\pm 0.05$ GeV/cm$^3$. We compute the dark matter density at four radii in the stellar halo and fit to a generalized NFW profile. Although the uncertainties are large, we find a profile broadly consistent with recent analyses.

astro-ph.GA↗

arXiv2022

Measuring Galactic Dark Matter through Unsupervised Machine Learning

Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive flows to stars from a realistic simulation of a Milky Way-type galaxy to learn -- in an unsupervised way -- the stellar phase space density and its derivatives. With these as inputs, and under the assumption of dynamic equilibrium, the gravitational acceleration field and mass density can be calculated directly from the Boltzmann Equation without the need to assume either cylindrical symmetry or specific functional forms for the galaxy's mass density. We demonstrate our approach can accurately reconstruct the mass density and acceleration profiles of the simulated galaxy, even in the presence of Gaia-like errors in the kinematic measurements.

astro-ph.GA↗

arXiv2019

Measurement of the radial matrix elements for the $6s ^2S_{1/2} \rightarrow 7p ^2P_J$ transitions in cesium

We report measurements of the electric dipole matrix elements of the $^{133}$Cs $\ $ $6s\,^2S_{1/2} \rightarrow 7p\,^2P_{1/2}$ and $6s\,^2S_{1/2} \rightarrow 7p\,^2P_{3/2}$ transitions. Each of these determinations is based on direct, precise comparisons of the absorption coefficients between two absorption lines. For the $\langle 6s\,^2S_{1/2}||r|| 7p\,^2P_{3/2} \rangle$ matrix element, we measure the ratio of the absorption coefficient on this line with that of the D$_1$ transition, $6s\,^2S_{1/2} \rightarrow 6p\,^2P_{1/2}$. The matrix element of the D$_1$ line has been determined with high precision previously by many groups. For the $\langle 6s\,^2S_{1/2}||r|| 7p\,^2P_{1/2} \rangle$ matrix element, we measure the ratio of the absorption coefficient on this line with that of the $6s\,^2S_{1/2} \rightarrow 7p\,^2P_{3/2}$ transition. Our results for these matrix elements are $\langle 6s\,^2S_{1/2}||r|| 7p\,^2P_{3/2} \rangle = 0.57417 \: (57)~a_0$ and $\langle 6s\,^2S_{1/2}||r|| 7p\,^2P_{1/2} \rangle = 0.27810 \: (45)~a_0$. These measurements have implications for the interpretation of parity nonconservation in atoms.

physics.atom-ph↗