arXiv subjects

Matthew R. Buckley

Publications and source records attributed to Matthew R. Buckley.

At least 19 recordsLinked to original sources

arXiv2026

Dark Radiation Sticks Together: Dark QCD and the Hubble Tension

We introduce a model of dark matter charged under a new confining gauge group akin to the Standard Model's quantum chromodynamics (QCD). The fermionic sector contains both heavy and light quark-like fermions charged under the dark QCD. As the Universe cools, the gauge force confines and dark radiation transitions from free particles to low-mass dark pions. This form of dark radiation behaves as an imperfect fluid with nonzero viscosity. If the confinement occurs at the MeV scale, the model can alleviate the tension between early- and late-time measurements of the Hubble parameter, with a Bayesian posterior of $H_0 = 71.60\pm 0.63$ km/s/Mpc and a $3σ$ residual tension across Planck, ACT, BBN, Pantheon+, DESI DR2, and SH0ES. In the late Universe, the heavy dark matter can evade existing constraints on dark matter self-interactions while containing a rich phenomenology that may be accessible in future observations.

hep-ph↗

arXiv2026

A Universal Distribution of Dark Matter in Milky Way-like galaxies and How to Infer It

The phase-space density of dark matter within the Milky Way is a key quantity that encodes information about the nature of the dark sector. The local phase-space density is also required to properly interpret the results of dark matter direct detection experiments. However, there are at present few observational constraints. In this paper, we show that a simple coordinate transformation reveals a near-universal DM phase-space distribution function among three independent suites of cosmological simulations of Milky Way-mass galaxies. We provide evidence for this with plots of kinematic features as well as machine learning-based classifiers that are sensitive to all of the correlations in the full multivariate phase space. Deviations from universality are found only at extremes of galactic radius and/or velocity, and in one simulation that has a prominent accreted dark disc. We further show that the parameters for the coordinate transformation can be inferred from metal poor stars ($\log_{10}[{\rm Fe}/{\rm H}]< -2$). These stars also contain signatures of the dark disk, allowing the existence of such a structure to be inferred from observation. Finally, we construct a model of this universal phase-space distribution using a normalizing flow, trained on the standardized phase-space across simulations. We will apply our method to survey data from Gaia and SDSS in a forthcoming work.

astro-ph.GA↗

arXiv2026

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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

physics.ins-det↗

arXiv2025

Via Machinae 3.0: A search for stellar streams in Gaia with the CATHODE algorithm

We apply the model-agnostic anomaly detection method Cathode - originally developed for particle physics - to search for stellar streams in Gaia data. We combine Cathode with Via Machinae 3.0: a re-optimized version of the stellar stream detection method that was previously applied to Gaia data together with the related anomaly detection technique Anode. We demonstrate that the combination of Via Machinae 3.0 with Cathode, called VM3-C, not only re-discovers previously known streams, but also confirms many candidate streams identified in combination with Anode (denoted VM3-A). Compared to VM3-A, the number of stream candidates detected by VM3-C increases by around 10%. Moreover, both of the methods discover the same two large clusters of stream candidates in the Northern Galactic hemisphere. We dub these highly significant anomalous structures the Raritan stream and the Passaic stream. These two structures may indicate the presence of larger objects, such as dwarf galaxy streams, or non-trivial orbital dynamics resulting in bifurcation or fanning, and are promising and high-priority targets for further analysis.

astro-ph.GA↗

arXiv2025

General Constraints on Isocurvature from the CMB and Ly-$α$ Forest

Current cosmological data are well-described by the Lambda-Cold Dark Matter ($Λ$CDM) model, which assumes adiabatic initial conditions for the primordial density perturbations. This agreement between data and theory enables strong constraints on new physics that generates isocurvature perturbations. Existing constraints typically assume a simple power law form for the isocurvature power spectrum. However, many new physics scenarios -- such as cosmological phase transitions and gravitational particle production -- can deviate from this assumption. To derive general constraints which apply to a wide variety of new physics scenarios, we consider four types of isocurvature modes (dark matter, baryon, dark radiation and neutrino density isocurvature) and parametrize the isocurvature power spectrum using two general forms: a delta function and a broken power law. Using data from the cosmic microwave background (CMB), baryon acoustic oscillations, the Lyman-$α$ forest, and CMB spectral distortions, we place constraints on the isocurvature power spectrum across a wide range of scales, from $10^{-4}\,\textrm{Mpc}^{-1}$ to $10^{4}\,\textrm{Mpc}^{-1}$.

astro-ph.CO↗

arXiv2024

Inferring the Morphology of the Galactic Center Excess with Gaussian Processes

Descriptions of the Galactic Center using Fermi gamma-ray data have so far modeled the Galactic Center Excess (GCE) as a template with fixed spatial morphology or as a linear combination of such templates. Although these templates are informed by various physical expectations, the morphology of the excess is a priori unknown. For the first time, we describe the GCE using a flexible, non-parametric machine learning model -- the Gaussian process (GP). We assess our model's performance on synthetic data, demonstrating that the model can recover the templates used to generate the data. We then fit the \Fermi data with our model in a single energy bin from 2-20 GeV (leaving a spectral GP analysis of the GCE for future work) using a variety of template models of diffuse gamma-ray emission to quantify our fits' systematic uncertainties associated with diffuse emission modeling. We interpret our best-fit GP in terms of GCE templates consisting of an NFW squared template and a bulge component to determine which bulge models can best describe the fitted GP and to what extent the best-fit GP is described better by an NFW squared template versus a bulge template. The best-fit GP contains morphological features that are typically not associated with traditional GCE studies. These include a localized bright source at around $(\ell,b) = (20^{\circ}, 0^{\circ})$ and a diagonal arm extending Northwest from the Galactic Center. In spite of these novel features, the fitted GP is explained best by a template-based model consisting of the bulge presented in Coleman et al. (2020) and a squared NFW component. Our results suggest that the physical interpretation of the GCE in terms of stellar bulge and NFW-like components is highly sensitive to the assumed morphologies, background models, and the region of the sky used for inference.

astro-ph.HE↗

arXiv2024

Force-feeding Supermassive Black Holes with Dissipative Dark Matter

Supermassive black holes with masses $\gtrsim 10^9\,M_\odot$ have been discovered by JWST at high redshifts ($z\sim 7$). It is difficult to explain such objects as the result of accretive growth of stellar-mass seeds, as the rate at which baryons can be fed to the black hole is limited by the radiation pressure of the infalling matter. In this paper, we propose a new mechanism to create the early progenitors: the collapse of small dark matter halos through a dissipative cooling mechanism in the dark sector. These small black holes can then be efficiently fed with additional dissipative dark matter due to the comparatively weak interactions between the dark radiation and dark matter, which results in a very short Eddington time and high accretion rates.

hep-ph↗

arXiv2024

GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Mock Stellar Catalogs

Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires "upsampling" the star particles into individual stars following the same phase-space density. In this paper, we introduce two new upsampling methods. First, we describe GalaxyFlow, a sophisticated upsampling method that utilizes normalizing flows to both estimate the stellar phase space density and sample from it. Second, we improve on existing upsamplers based on adaptive kernel density estimation, using maximum likelihood estimation to fine-tune the bandwidth for such algorithms in a way that improves both the density estimation accuracy and upsampling results. We demonstrate our upsampling techniques on a neighborhood of the Solar location in two simulated galaxies: Auriga 6 and h277. Both yield smooth stellar distributions that closely resemble the stellar densities seen in the Gaia DR3 catalog. Furthermore, we introduce a novel multi-model classifier test to compare the accuracy of different upsampling methods quantitatively. This test confirms that GalaxyFlow estimates the density of the underlying star particles more accurately than methods based on kernel density estimation, at the cost of being more computationally intensive.

astro-ph.GA↗

arXiv2024

Discovery and Characterization of Two Ultra Faint-Dwarfs Outside the Halo of the Milky Way: Leo M and Leo K

We report the discovery of two ultra-faint dwarf galaxies, Leo M and Leo K, that lie outside the halo of the Milky Way. Using Hubble Space Telescope imaging of the resolved stars, we create color-magnitude diagrams reaching the old main sequence turn-off of each system and (i) fit for structural parameters of the galaxies; (ii) measure their distances using the luminosity of the Horizontal Branch stars; (iii) estimate integrated magnitudes and stellar masses; and (iv) reconstruct the star formation histories. Based on their location in the Local Group, neither galaxy is currently a satellite of the Milky Way, although Leo K is located ~26 kpc from the low-mass galaxy Leo T and these two systems may have had a past interaction. Leo M and Leo K have stellar masses of 1.8 (+0.3/-0.2) x 10^4 Msun and 1.2+/-0.2 x 10^4 Msun, and were quenched 10.6 (+2.2/-1.1) Gyr and 12.8 (+0.1/-4.2) Gyr ago, respectively. Given that the galaxies are not satellites of the MW, it is unlikely that they were quenched by environmental processing. Instead, given their low stellar masses, their early quenching timescales are consistent with the scenario that a combination of reionization and stellar feedback shut-down star formation at early cosmic times.

astro-ph.GA↗

arXiv2024

Limits on Dark Matter Annihilation from the Shape of Radio Emission in M31

Well-motivated models of dark matter often result in a population of electrons and positrons within galaxies produced through dark matter annihilation -- usually in association with gamma rays. As they diffuse through galactic magnetic fields, these $e^\pm$ produce synchrotron radio emission. The intensity and morphology of this signal depends on the properties of the interstellar medium through which the $e^\pm$ propagate. Using observations of the Andromeda Galaxy (M31) to construct a model of the gas, magnetic fields, and starlight, we set constraints on dark matter annihilation to $b\bar{b}$ using the morphology of 3.6 cm radio emission. As the emission signal at the center of M31 is very sensitive to the diffusion coefficient and dark matter profile, we base our limits on the differential flux in the region between $0.9-6.9$ kpc from the center. We exclude annihilation cross sections $\gtrsim 3 \times 10^{-25}$ cm$^3$/s in the mass range $10-500$ GeV, with a maximum sensitivity of $7\times 10^{-26}$ cm$^3$/s at $20-40$ GeV. Though these limits are weaker than those found in previous studies of M31, they are robust to variations of the diffusion coefficient.

hep-ph↗

arXiv2024

Dark Radiation Isocurvature from Cosmological Phase Transitions

Cosmological first order phase transitions are typically associated with physics beyond the Standard Model, and thus of great theoretical and observational interest. Models of phase transitions where the energy is mostly converted to dark radiation can be constrained through limits on the dark radiation energy density (parameterized by $ΔN_{\rm eff}$). However, the current constraint ($ΔN_{\rm eff} < 0.3$) assumes the perturbations are adiabatic. We point out that a broad class of non-thermal first order phase transitions that start during inflation but do not complete until after reheating leave a distinct imprint in the scalar field from bubble nucleation. Dark radiation inherits the perturbation from the scalar field when the phase transition completes, leading to large-scale isocurvature that would be observable in the CMB. We perform a detailed calculation of the isocurvature power spectrum and derive constraints on $ΔN_{\rm eff}$ based on CMB+BAO data. For a reheating temperature of $T_{\rm rh}$ and a nucleation temperature $T_*$, the constraint is approximately $ΔN_{\rm eff}\lesssim 10^{-5} (T_*/T_{\rm rh})^{-4}$, which can be much stronger than the adiabatic result. We also point out that since perturbations of dark radiation have a non-Gaussian origin, searches for non-Gaussianity in the CMB could place a stringent bound on $ΔN_{\rm eff}$ as well.

hep-ph↗

arXiv2024

Inductive Simulation of Calorimeter Showers with Normalizing Flows

Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accelerate this process while achieving unprecedented levels of accuracy, but scaling this approach up to higher resolutions relevant for future detector upgrades leads to prohibitive memory constraints. To overcome this problem, we introduce Inductive CaloFlow (iCaloFlow), a framework for fast detector simulation based on an inductive series of normalizing flows trained on the pattern of energy depositions in pairs of consecutive calorimeter layers. We further use a teacher-student distillation to increase sampling speed without loss of expressivity. As we demonstrate with Datasets 2 and 3 of the CaloChallenge2022, iCaloFlow can realize the potential of normalizing flows in performing fast, high-fidelity simulation on detector geometries that are ~ 10 - 100 times higher granularity than previously considered.

physics.ins-det↗

arXiv2023

Weakly-Supervised Anomaly Detection in the Milky Way

Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification Without Labels (CWoLa), a weakly-supervised anomaly detection method, to identify cold stellar streams within the more than one billion Milky Way stars observed by the Gaia satellite. CWoLa operates without the use of labeled streams or knowledge of astrophysical principles. Instead, we train a classifier to distinguish between mixed samples for which the proportions of signal and background samples are unknown. This computationally lightweight strategy is able to detect both simulated streams and the known stream GD-1 in data. Originally designed for high-energy collider physics, this technique may have broad applicability within astrophysics as well as other domains interested in identifying localized anomalies.

astro-ph.GA↗

arXiv2023

Via Machinae 2.0: Full-Sky, Model-Agnostic Search for Stellar Streams in Gaia DR2

We present an update to Via Machinae, an automated stellar stream-finding algorithm based on the deep learning anomaly detector ANODE. Via Machinae identifies stellar streams within Gaia, using only angular positions, proper motions, and photometry, without reference to a model of the Milky Way potential for orbit integration or stellar distances. This new version, Via Machinae 2.0, includes many improvements and refinements to nearly every step of the algorithm, that altogether result in more robust and visually distinct stream candidates than our original formulation. In this work, we also provide a quantitative estimate of the false positive rate of Via Machinae 2.0 by applying it to a simulated Gaia-mock catalog based on Galaxia, a smooth model of the Milky Way that does not contain substructure or stellar streams. Finally, we perform the first full-sky search for stellar streams with Via Machinae 2.0, identifying 102 streams at high significance within the Gaia Data Release 2, of which only 10 have been previously identified. While follow-up observations for further confirmation are required, taking into account the false positive rate presented in this work, we expect approximately 90 of these stream candidates to correspond to real stellar structures.

astro-ph.GA↗

arXiv2023

Pegasus W: An Ultra-Faint Dwarf Galaxy Outside the Halo of M31 Not Quenched by Reionization

We report the discovery of an ultrafaint dwarf (UFD) galaxy, Pegasus W, located on the far side of the Milky Way-M31 system and outside the virial radius of M31. The distance to the galaxy is 915 (+60/-91) kpc, measured using the luminosity of horizontal branch (HB) stars identified in Hubble Space Telescope optical imaging. The galaxy has a half-light radius (r_h) of 100 (+11/-13) pc, M_V = -7.20 (+0.17/-0.16) mag, and a present-day stellar mass of 6.5 (+1.1/-1.4) x 10^4 Msun. We identify sources in the color-magnitude diagram (CMD) that may be younger than ~500 Myr suggesting late-time star formation in the UFD galaxy, although further study is needed to confirm these are bona fide young stars in the galaxy. Based on fitting the CMD with stellar evolution libraries, Pegasus W shows an extended star formation history (SFH). Using the tau_90 metric (defined as the timescale by which the galaxy formed 90% of its stellar mass), the galaxy was quenched only 7.4 (+2.2/-2.6) Gyr ago, which is similar to the quenching timescale of a number of UFD satellites of M31 but significantly more recent than the UFD satellites of the Milky Way. Such late-time quenching is inconsistent with the more rapid timescale expected by reionization and suggests that, while not currently a satellite of M31, Pegasus W was nonetheless slowly quenched by environmental processes.

astro-ph.GA↗