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The dark matter flux in a direct detection experiment depends on its local speed distribution. This distribution has been inferred from simulations of Milky Way–like galaxies, but such models serve only as proxies, given that no simulation directly captures the detailed evolution of our own Galaxy. This motivates alternative approaches that obtain this distribution directly from observations. In this work, we utilize 98 Milky Way analogues from the TNG50 simulation to develop and validate a procedure for inferring the dark matter speed distribution using the kinematics of nearby stars. We find that the dark matter that originated from old mergers, plus that from recent nonluminous accretions, is well described by a Maxwell–Boltzmann speed distribution centered at the local standard-of-rest velocity. Meanwhile, recently accreted dark matter from massive mergers has speeds that can be traced from the associated stellar debris of these events. The stellar populations systematically underestimate the velocity dispersion of their dark matter counterparts, but a simple kinematic boost brings the two into good alignment. Using the TNG50 host galaxies, we demonstrate that combining these two contributions provides an accurate reconstruction of the local dark matter speeds. As an application of the procedure to our own Galaxy, we utilize stellar kinematic data from Gaia to quantify how the dark matter remnants from the Milky Way’s last major merger impact its speed distribution in the solar neighborhood.

Observations of the Milky Way’s stellar halo find that it is predominantly comprised of a radially biased population of stars, dubbed the Gaia Sausage–Enceladus, or GSE. These stars are thought to be debris from dwarf galaxy accretion early in the Milky Way’s history. Though typically considered to be from a single merger, it is possible that the GSE debris has multiple sources. To investigate this possibility, we use the IllustrisTNG50 simulation to identify stellar accretion histories in 98 Milky Way analogs—the largest sample for which such an identification has been performed—and find GSE-like debris in 32, with two-merger GSEs accounting for a third of these cases. Distinguishing single-merger GSEs from two-merger GSEs is difficult in common kinematic spaces, but differences are more evident through chemical abundances and star formation histories. This is because single-merger GSEs are typically accreted more recently than the galaxies in two-merger GSEs: the median infall times (with 16th and 84th percentiles) are 5.9−2.0+3.3 and 10.7−3.7+1.2 Gyr ago for single- and two-merger scenarios, respectively. The systematic shifts in abundances and ages that occur as a result suggest that efforts in modeling these aspects of the stellar halo prove ever-important in understanding its assembly.

Semi-analytic modeling furnishes an efficient avenue for characterizing the properties of dark matter halos associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator -- which incorporates both empirical models of the galaxy-halo connection in the field as well as analytic prescriptions for the orbital evolution of these satellites after they enter a host galaxy -- to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match the observed properties of a particular dwarf galaxy, we can then infer arbitrary properties of the satellite galaxy within the Cold Dark Matter paradigm. For the Milky Way's classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity \\(v_max\\) and the radius \\(r_max\\) at which it occurs, varying over two choices of feedback model and two prescriptions for the SMHM relation that populate dark matter halos with physically distinct galaxies. While simple empirical scaling relations can recover the median inferred value for \\(v_max\\) and \\(r_max\\), this approach provides realistic correlated uncertainties and aids interpretability through variation of the model. For these different models, we also demonstrate how the internal properties of a satellite's dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. The technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter halos and population statistics.

The dark matter flux in a direct detection experiment depends on its local speed distribution. This distribution has been inferred from simulations of Milky Way-like galaxies, but such models serve only as proxies, given that no simulation directly captures the detailed evolution of our own Galaxy. This motivates alternative approaches that obtain this distribution directly from observations. In this work, we utilize 98 Milky Way analogues from the TNG50 simulation to develop and validate a procedure for inferring the dark matter speed distribution using the kinematics of nearby stars. We find that the dark matter that originated from old mergers, plus that from recent nonluminous accretions, is well described by a Maxwell-Boltzmann speed distribution centered at the local standard-of-rest velocity. Meanwhile, recently accreted dark matter from massive mergers has speeds that can be traced from the associated stellar debris of these events. The stellar populations systematically underestimate the velocity dispersion of their dark matter counterparts, but a simple kinematic boost brings the two into good alignment. Using the TNG50 host galaxies, we demonstrate that combining these two contributions provides an accurate reconstruction of the local dark matter speeds. As an application of the procedure to our own Galaxy, we utilize stellar kinematic data from Gaia to quantify how the dark matter remnants from the Milky Way's last major merger impact its speed distribution in the solar neighborhood.

The sensitivity of direct detection experiments depends on the phase-space distribution of dark matter near the Sun, which can be modeled theoretically using cosmological hydrodynamical simulations of Milky Way-like galaxies. However, capturing the halo-to-halo variation in the local dark matter speeds -- a necessary step for quantifying the astrophysical uncertainties that feed into experimental results -- requires a sufficiently large sample of simulated galaxies, which has been a challenge. In this Letter, we quantify this variation with nearly 100 Milky Way-like galaxies from the TNG50 simulation, the largest sample to date at this resolution. Moreover, we introduce a novel phase-space scaling procedure that endows every system with a reference frame that accurately reproduces the local standard-of-rest speed of our Galaxy, providing a principled way of extrapolating the simulation results to real-world data. The ensemble of predicted speed distributions is well characterized by the standard halo model, a Maxwell-Boltzmann distribution truncated at the escape speed, though the individual distributions can deviate from it, especially at high speeds. The dark matter-nucleon cross section limits placed by these speed distributions vary by ~60% about the median. This places the 1-sigma astrophysical uncertainty at or below the level of the systematic uncertainty of current ton-scale detectors, even down to the energy threshold. The predicted uncertainty remains unchanged when subselecting on those TNG50 galaxies with merger histories similar to the Milky Way. Tabulated speed distributions, as well as Maxwell-Boltzmann fits, are provided for use in computing direct detection bounds or projecting sensitivities.

The merger history of a galaxy leaves imprints on its present-day stellar chemodynamics, yet dynamical processes progressively erase this record. We ask: how far back in time, and from which observables, can a galaxy's assembly history still be recovered? We provide a quantitative framework to address this question, using Mutual Information normalized by Shannon entropy to measure how much present-day stellar chemodynamics retains about each past merger's stellar mass \\(M_\\) and infall time \\(t_ infall\\). This framework is applied to TNG50 Milky Way -- like galaxies, with comparison to FIRE-2. We find that the gravitational potential and total energy are the most informative and longest-lived tracers of merger properties, highlighting the need for accurately measuring the Milky Way's potential. The information carried by the radial velocity decays to the noise floor within \\(\\)5 Gyr, angular momentum carries low information overall with a mass-dependent decay, and chemical abundances retain a flat, low information floor. Information washout depends on three key factors: (1) radial position -- stars in the inner galaxy lose information faster due to shorter orbital times; (2) infall time -- old mergers are largely phase-mixed; and (3) merger mass -- larger mergers sink to the bottom of the potential well via dynamical friction, inducing violent relaxation that erases dynamical information. At each galactocentric radius, we map the observational horizon in the \\((M_ \\; t_ infall)\\) plane beyond which past mergers can no longer be recovered from that observable. By recasting merger reconstruction into this quantitative, observable-by-observable map of what is and is not recoverable, our results provide a foundation for interpreting chemodynamical signatures of past mergers and for guiding surveys and modeling toward the observables that maximize merger information recovery.

Observations of the Milky Way's stellar halo find that it is predominantly comprised of a radially biased population of stars, dubbed the Gaia Sausage--Enceladus, or GSE. These stars are thought to be debris from dwarf galaxy accretion early in the Milky Way's history. Though typically considered to be from a single merger, it is possible that the GSE debris has multiple sources. To investigate this possibility, we use the TNG50 simulation to identify stellar accretion histories in 98 Milky Way analogues -- the largest sample for which such an identification has been performed -- and find GSE-like debris in 32, with two-merger GSEs accounting for a third of these cases. Distinguishing single-merger GSEs from two-merger GSEs is difficult in common kinematic spaces, but differences are more evident through chemical abundances and star formation histories. This is because single-merger GSEs are typically accreted more recently than the galaxies in two-merger GSEs: the median infall times (with 16th and 84th percentiles) are \\(5.9^+3.3_-2.0\\) and \\(10.7^+1.2_-3.7\\) Gyr ago for single- and two-merger scenarios, respectively. The systematic shifts in abundances and ages that occur as a result suggest that efforts in modeling these aspects of the stellar halo prove ever-important in understanding its assembly.

The dark matter flux in a direct detection experiment depends on its local speed distribution. This distribution has been inferred from simulations of Milky Way-like galaxies, but such models serve only as proxies given that no simulation directly captures the detailed evolution of our own Galaxy. This motivates alternative approaches which obtain this distribution directly from observations. In this work, we utilize 98 Milky Way analogues from the IllustrisTNG50 simulation to develop and validate a procedure for inferring the dark matter speed distribution using the kinematics of nearby stars. We find that the dark matter that originated from old mergers, plus that from recent non-luminous accretions, is well described by a Maxwell-Boltzmann speed distribution centered at the local standard-of-rest velocity. Meanwhile, recently accreted dark matter from massive mergers has speeds that can be traced from the associated stellar debris of these events. The stellar populations systematically underestimate the velocity dispersion of their dark matter counterparts, but a simple kinematic boost brings the two into good alignment. Using the TNG50 host galaxies, we demonstrate that combining these two contributions provides an accurate reconstruction of the local dark matter speeds. As an application of the procedure to our own Galaxy, we utilize stellar kinematic data from Gaia to quantify how the dark matter remnants from the Milky Way's last major merger impact its speed distribution in the Solar neighborhood.

Semi-analytic modeling furnishes an efficient avenue for characterizing the properties of dark matter halos associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator -- which incorporates both empirical models of the galaxy-halo connection in the field as well as analytic prescriptions for the orbital evolution of these satellites after they enter a host galaxy -- to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match the observed properties of a particular dwarf galaxy, we can then infer arbitrary properties of the satellite galaxy within the Cold Dark Matter paradigm. For the Milky Way's classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity \\(v_max\\) and the radius \\(r_max\\) at which it occurs, varying over two choices of feedback model and two prescriptions for the SMHM relation that populate dark matter halos with physically distinct galaxies. While simple empirical scaling relations can recover the median inferred value for \\(v_max\\) and \\(r_max\\), this approach provides realistic correlated uncertainties and aids interpretability through variation of the model. For these different models, we also demonstrate how the internal properties of a satellite's dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. The technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter halos and population statistics.

The sensitivity of direct detection experiments depends on the phase-space distribution of dark matter near the Sun, which can be modeled theoretically using cosmological hydrodynamical simulations of Milky Way-like galaxies. However, capturing the halo-to-halo variation in the local dark matter speeds -- a necessary step for quantifying the astrophysical uncertainties that feed into experimental results -- requires a sufficiently large sample of simulated galaxies, which has been a challenge. In this work, we quantify this variation with nearly one hundred Milky Way-like galaxies from the IllustrisTNG50 simulation, the largest sample to date at this resolution. Moreover, we introduce a novel phase-space scaling procedure that endows every system with a reference frame that accurately reproduces the local standard-of-rest speed of our Galaxy, providing a principled way of extrapolating the simulation results to real-world data. The predicted speed distributions are consistent with the Standard Halo Model, a Maxwell-Boltzmann distribution peaked at the local circular speed and truncated at the escape speed. The dark matter-nucleon cross section limits placed by these speed distributions vary by ~60% about the median. This places the 1-sigma astrophysical uncertainty at or below the level of the systematic uncertainty of current ton-scale detectors, even down to the energy threshold. The predicted uncertainty remains unchanged when sub-selecting on those TNG galaxies with merger histories similar to the Milky Way. Tabulated speed distributions, as well as Maxwell-Boltzmann fits, are provided for use in computing direct detection bounds or projecting sensitivities.