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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

$\mathrm{D}$ark$\mathrm{F}$lux: A new tool to analyze indirect-detection spectra of next-generation dark matter models

Here we present DarkFlux, a software tool designed to analyze indirect-detection signatures for next-generation models of dark matter (DM) with multiple annihilation channels. Version 1.0 of this tool accepts user-generated models with 2→2 tree-level dark matter annihilation to pairs of Standard Model (SM) particles and analyzes DM annihilation to γ rays. The tool consists of three modules, which can be run in a loop in order to scan over DM mass if desired: The annihilation fraction module calls an internal installation of MadDM, a dark matter phenomenology plugin for the Monte Carlo event generator MadGraph5 _ aMC@NLO, to compute the thermally averaged cross section $\langle σv\rangle_i$ for each annihilation channel $χχ$ ($\bar{χ}$,$χ^†$)→ i $\∈{SM, SM}. . The module then computes the fractional annihilation rate (annihilation fraction) into each channel. The flux module combines the flux spectrum from each annihilation channel, weighted by the appropriate annihilation fractions, to compute the total flux at Earth due to DM annihilation. In DarkFlux v1.0, this module specifically computes the γ-ray flux for each channel using the publicly available PPPC4DMID tables. The analysis module compares the total flux to observational data and computes the upper limit at 95% confidence level (CL) on the total thermally averaged DM annihilation cross section. In DarkFlux v1.0, this module compares the total γ-ray flux to a joint-likelihood analysis of fifteen dwarf spheroidal galaxies (dSphs) analyzed by the Fermi-LAT collaboration. DarkFlux v1.0 automatically provides data tables and can plot the output of these three modules. In this manual, we briefly motivate this indirect-detection computer tool and review the essential DM physics. We then describe the several modules of DarkFlux in greater detail. Finally, we show how to install and run DarkFlux and provide two worked examples demonstrating its capabilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Iron, magnesium, and titanium isotopic fractionations between garnet, ilmenite, f ayalite, biotite, and tourmaline: Results from NRIXS, ab initio , and study of mineral separates from the Moosilauke metapelite

Interpreting isotopic signatures documented in natural rocks requires knowledge of equilibrium isotopic fractionation factors. Here, we determine equilibrium Fe isotope fractionation factors between several common rock-forming minerals using a comparative approach involving three independent methods: (i) isotopic analyses of natural minerals from a metapelite from Mt. Moosilauke, New Hampshire, for which equilibration temperature and pressure are well constrained to be near the aluminosilicate triple point (T ≃ 500 °C, P ≃ 4 kbar), (ii) Nuclear Resonant Inelastic X-ray Scattering (NRIXS) measurements of Fe force constants of minerals, and (iii) Density Functional Theory (DFT) ab initio calculations of Fe force constants of minerals. The minerals studied for Fe isotopes include, in increasing order of their β-factors: garnet < ilmenite ≈ fayalite < biotite < tourmaline < muscovite ≈ plagioclase. Some of this ordering is affected by the presence of Fe 3+ in the minerals, which tends to form stiffer bonds and be associated with heavy Fe isotope enrichments relative to Fe 2+ . We are, however, able to assess the magnitude of the effect of the ratio Fe 3+ /ΣFe on equilibrium fractionation factors, notably on the ilmenite-hematite solid solution. Equilibrium Fe isotopic fractionation factors between garnet, ilmenite, biotite, tourmaline and fayalite are determined. We also report Mg and Ti isotopic compositions of selected Moosilauke minerals that allow us to better constrain the equilibrium fractionation factors for garnet-biotite-tourmaline (Mg isotopes) and biotite-ilmenite (Ti isotopes). We show how the newly determined equilibrium fractionation factors can be used to address diverse problems in Earth and planetary sciences, notably (i) Fe and Mg isotopic fractionation during anatexis, (ii) Fe isotopic fractionation in lunar ilmenite, and (iii) Ti isotopic fractionation during fluvial transport of minerals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

ν-point energy correletors with F AST EEC: Small-x physics from LHC jets

In recent years, energy correlators have emerged as a powerful tool for studying jet substructure, with promising applications such as probing the hadronization transition, analyzing the quark-gluon plasma, and improving the precision of top quark mass measurements. The projected N-point correlator measures correlations between N final-state particles by tracking the largest separation between them, showing a scaling behavior related to DGLAP splitting functions. These correlators can be analytically continued in N, commonly referred to as ν-correlators, allowing access to non-integer moments of the splitting functions. Of particular interest is the ν → 0 limit, where the small momentum fraction behavior of the splitting functions requires resummation. Originally, the computational complexity of evaluating ν-correlators for M particles scaled as 2 2M , making it impractical for real-world analyses. However, by using recursion, we reduce this to M 2M , and through the FastEEC method of dynamically resolving subjets, M is replaced by the number of subjets. This breakthrough enables, for the first time, the computation of ν-correlators for LHC data. In practice, limiting the number of subjets to 16 is sufficient to achieve percent-level precision, which we validate using known integer-ν results and convergence tests for non-integer ν. We have implemented this in an update to FastEEC and conducted an initial study of power-law scaling in the perturbative regime as a function of ν, using CMS Open Data on jets. The results agree with DGLAP evolution, except at small ν, where the anomalous dimension saturates to a value that matches the BFKL anomalous dimension.

Energy correlators↗