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GAMBIT (Global And Modular BSM Inference Tool) performs statistical global fits of generic physics models using a wide range of particle physics and astrophysics data. Modules provide native simulations of collider and astrophysics experiments, a flexible system for interfacing external codes (the backend system), a fully featured statistical and parameter scanning framework, and additional tools for implementing and using hierarchical models.
AxionNS computes radio light curves resulting from the resonant conversion of Axion dark matter into photons within the magnetosphere of a neutron star. Photon trajectories are traced from the observer to the magnetosphere where a root finding algorithm identifies the regions of resonant conversion. Given the modeling of the axion dark matter distribution and conversion probability, one can compute the photon flux emitted from these regions. The individual contributions from all the trajectories is then summed to obtain the radiated photon power per unit solid angle.
DarkBit computes dark matter constraints on extensions to the Standard Model of particle physics. Written in the GAMBIT (ascl:1708.030) framework, it seamlessly integrates with other tools in the statistical fitting framework; it is also available as a standalone tool. It offers a signal yield calculator for gamma-ray observations, provides likelihoods for arbitrary combinations of spin-independent and spin-dependent scattering processes, and provides a general solution for studying complex particle physics models that predict dark matter annihilation to a multitude of final states.
DDCalc performs various dark matter direct detection calculations, including signal rate predictions, constraints on light DM, and likelihoods for several experiments. It offers eighteen non-relativistic effective operators to describe velocity and momentum transfer, and elastic scattering of DM particles off nucleons, and has an extended detector interface.
Swordfish studies the information yield of counting experiments. It implements at its core a rather general version of a Poisson point process with background uncertainties described by a Gaussian random field, and provides easy access to its information geometrical properties. Based on this information, a number of common and less common tasks can be performed. Swordfish allows quick and accurate forecasts of experimental sensitivities without time-intensive Monte Carlos, mock data generation and likelihood maximization. It can:
- calculate the expected upper limit or discovery reach of an instrument;
- derive expected confidence contours for parameter reconstruction;
- visualize confidence contours as well as the underlying information metric field;
- calculate the information flux, an effective signal-to-noise ratio that accounts for background systematics and component degeneracies; and
- calculate the Euclideanized signal which approximately maps the signal to a new vector which can be used to calculate the Euclidean distance between points.
swyft implements Truncated Marginal Neural Radio Estimation (TMNRE), a Bayesian parameter inference technique for complex simulation data. The code improves performance by estimating low-dimensional marginal posteriors rather than the joint posteriors of distributions, while also targeting simulations to targets of observational interest via an indicator function. The use of local amortization permits statistical checks, enabling validation of parameters that cannot be performed using sampling-based methods. swyft is also based on stochastic simulations, mapping parameters to observational data, and incorporates a simulator manager.
Albatross analyzes Milky Way stellar streams. This Simulation-Based Inference (SBI) library is built on top of swyft (ascl:2302.016), which implements neural ratio estimation to efficiently access marginal posteriors for all parameters of interest. Using swyft for its internal Truncated Marginal Neural Ratio Estimation (TMNRE) algorithm and sstrax (ascl:2306.008) for fast simulation and modeling, Albatross provides a modular inference pipeline to support parameter inference on all relevant parts of stellar stream models.
sstrax provides fast simulations of Milky Way stellar stream formation. Using JAX (ascl:2111.002) acceleration to support code compilation, sstrax forward models all aspects of stream formation, including evolution in gravitational potentials, tidal disruption and observational models, in a fully modular way. Although sstrax is a standalone python package, it was also developed to integrate directly with the Albatross (ascl:2306.009) inference pipeline, which performs inference on all relevant aspects of the stream model.