Neutrino masses and beyond-Λ\LambdaCDM cosmology with LSST and future CMB experiments

Cosmological measurements over the next decade will enable us to shed light on the content and evolution of the Universe. Complementary measurements of the Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations are expected to allow an indirect determination of the sum of neutrino masses, within the framework of the flat $\Lambda$CDM model. However, possible deviations from $\Lambda$CDM such as a non-zero cosmological curvature or a dark energy equation of state with $w\neq -1$ would leave similar imprints on the expansion rate of the Universe and clustering of matter. We show how future CMB measurements can be combined with late-time measurements of galaxy clustering and cosmic shear from the Large Synoptic Survey Telescope to alleviate this degeneracy. Together, they are projected to reduce the uncertainty on the neutrino mass sum to 30 meV within this more general cosmological model. Achieving a 3$\sigma$ measurement of the minimal 60 meV mass (or 4$\sigma$ assuming $w=-1$) will require a five-fold improved measurement of the optical depth to reionization, obtainable through a large-scale CMB polarization measurement.Comment: 10 pages, 6 figures; v2, updated to PRD version, references adde

LHC Searches for Dark Sector Showers

This paper proposes a new search program for dark sector parton showers at the Large Hadron Collider (LHC). These signatures arise in theories characterized by strong dynamics in a hidden sector, such as Hidden Valley models. A dark parton shower can be composed of both invisible dark matter particles as well as dark sector states that decay to Standard Model particles via a portal. The focus here is on the specific case of 'semi-visible jets,' jet-like collider objects where the visible states in the shower are Standard Model hadrons. We present a Simplified Model-like parametrization for the LHC observables and propose targeted search strategies for regions of parameter space that are not covered by existing analyses. Following the 'mono-$X$' literature, the portal is modeled using either an effective field theoretic contact operator approach or with one of two ultraviolet completions; sensitivity projections are provided for all three cases. We additionally highlight that the LHC has a unique advantage over direct detection experiments in the search for this class of dark matter theories.Comment: 36 pages, 14 figures; v2, JHEP versio

A Search for Dark Matter Annihilation in Galaxy Groups

We use 413 weeks of publicly-available $\textit{Fermi}$ Pass 8 gamma-ray data, combined with recently-developed galaxy group catalogs, to search for evidence of dark matter annihilation in extragalactic halos. In our study, we use luminosity-based mass estimates and mass-to-concentration relations to infer the $J$-factors and associated uncertainties for hundreds of galaxy groups within a redshift range $z \lesssim 0.03$. We employ a conservative substructure boost-factor model, which only enhances the sensitivity by an $\mathcal{O}(1)$ factor. No significant evidence for dark matter annihilation is found and we exclude thermal relic cross sections for dark matter masses below $\sim$30 GeV to 95% confidence in the $b\bar{b}$ annihilation channel. These bounds are comparable to those from Milky Way dwarf spheroidal satellite galaxies. The results of our analysis increase the tension, but do not rule out, the dark matter interpretation of the Galactic Center excess. We provide a catalog of the galaxy groups used in this study and their inferred properties, which can be broadly applied to searches for extragalactic dark matter.Comment: 5+18 pages, 1+14 figures, catalog available at: https://github.com/bsafdi/DMCat; v2 updated to journal version with several updates, results and conclusions unchange

Light Weakly Coupled Axial Forces: Models, Constraints, and Projections

We investigate the landscape of constraints on MeV-GeV scale, hidden U(1) forces with nonzero axial-vector couplings to Standard Model fermions. While the purely vector-coupled dark photon, which may arise from kinetic mixing, is a well-motivated scenario, several MeV-scale anomalies motivate a theory with axial couplings which can be UV-completed consistent with Standard Model gauge invariance. Moreover, existing constraints on dark photons depend on products of various combinations of axial and vector couplings, making it difficult to isolate the effects of axial couplings for particular flavors of SM fermions. We present a representative renormalizable, UV-complete model of a dark photon with adjustable axial and vector couplings, discuss its general features, and show how some UV constraints may be relaxed in a model with nonrenormalizable Yukawa couplings at the expense of fine-tuning. We survey the existing parameter space and the projected reach of planned experiments, briefly commenting on the relevance of the allowed parameter space to low-energy anomalies in pi^0 and 8-Be* decay.Comment: 30 pages, 5 figures, 4 tables. v2: format changed to JHEP, typos fixed, references adde

Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation

Strong gravitational lensing has emerged as a promising approach for probing dark matter models on sub-galactic scales. Recent work has proposed the subhalo effective density slope as a more reliable observable than the commonly used subhalo mass function. The subhalo effective density slope is a measurement independent of assumptions about the underlying density profile and can be inferred for individual subhalos through traditional sampling methods. To go beyond individual subhalo measurements, we leverage recent advances in machine learning and introduce a neural likelihood-ratio estimator to infer an effective density slope for populations of subhalos. We demonstrate that our method is capable of harnessing the statistical power of multiple subhalos (within and across multiple images) to distinguish between characteristics of different subhalo populations. The computational efficiency warranted by the neural likelihood-ratio estimator over traditional sampling enables statistical studies of dark matter perturbers and is particularly useful as we expect an influx of strong lensing systems from upcoming surveys.Comment: 11 pages, 5 figures; matches the published version with a corrected plot, conclusions unchange

Mapping Extragalactic Dark Matter Annihilation with Galaxy Surveys: A Systematic Study of Stacked Group Searches

Dark matter in the halos surrounding galaxy groups and clusters can annihilate to high-energy photons. Recent advancements in the construction of galaxy group catalogs provide many thousands of potential extragalactic targets for dark matter. In this paper, we outline a procedure to infer the dark matter signal associated with a given galaxy group. Applying this procedure to a catalog of sources, one can create a full-sky map of the brightest extragalactic dark matter targets in the nearby Universe ($z\lesssim 0.03$), supplementing sources of dark matter annihilation from within the Local Group. As with searches for dark matter in dwarf galaxies, these extragalactic targets can be stacked together to enhance the signals associated with dark matter. We validate this procedure on mock $\textit{Fermi}$ gamma-ray data sets using a galaxy catalog constructed from the $\texttt{DarkSky}$ $N$-body cosmological simulation and demonstrate that the limits are robust, at $\mathcal{O}(1)$ levels, to systematic uncertainties on halo mass and concentration. We also quantify other sources of systematic uncertainty arising from the analysis and modeling assumptions. Our results suggest that a stacking analysis using galaxy group catalogs provides a powerful opportunity to discover extragalactic dark matter and complements existing studies of Milky Way dwarf galaxies.Comment: 17+7 pages, 9+4 figures; v2, updated to PRD version with several updates, results and conclusions unchange

Hierarchical Neural Simulation-Based Inference Over Event Ensembles

When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of interest. Such models often have a hierarchical structure, where "local" parameters impact individual events and "global" parameters influence the entire dataset. We introduce practical approaches for optimal dataset-wide probabilistic inference in cases where the likelihood is intractable, but simulations can be realized via forward modeling. We construct neural estimators for the likelihood(-ratio) or posterior and show that explicitly accounting for the model's hierarchical structure can lead to tighter parameter constraints. We ground our discussion using case studies from the physical sciences, focusing on examples from particle physics (particle collider data) and astrophysics (strong gravitational lensing observations).Comment: 10+4 pages, 5 figure