Impact of Weak Lensing Mass Calibration on eROSITA Galaxy Cluster Cosmological Studies -- a Forecast

We forecast the impact of weak lensing (WL) cluster mass calibration on the cosmological constraints from the X-ray selected galaxy cluster counts in the upcoming eROSITA survey. We employ a prototype cosmology pipeline to analyze mock cluster catalogs. Each cluster is sampled from the mass function in a fiducial cosmology and given an eROSITA count rate and redshift, where count rates are modeled using the eROSITA effective area, a typical exposure time, Poisson noise and the scatter and form of the observed X-ray luminosity-- and temperature--mass--redshift relations. A subset of clusters have mock shear profiles to mimic either those from DES and HSC or from the future Euclid and LSST surveys. Using a count rate selection, we generate a baseline cluster cosmology catalog that contains 13k clusters over 14,892~deg$^2$ of extragalactic sky. Low mass groups are excluded using raised count rate thresholds at low redshift. Forecast parameter uncertainties for $\Omega_\mathrm{M}$, $\sigma_8$ and $w$ are 0.023 (0.016; 0.014), 0.017 (0.012; 0.010), and 0.085 (0.074; 0.071), respectively, when adopting DES+HSC WL (Euclid; LSST), while marginalizing over the sum of the neutrino masses. A degeneracy between the distance--redshift relation and the parameters of the observable--mass scaling relation limits the impact of the WL calibration on the $w$ constraints, but with BAO measurements from DESI an improved determination of $w$ to 0.043 becomes possible. With Planck CMB priors, $\Omega_\text{M}$ ($\sigma_8$) can be determined to $0.005$ ($0.007$), and the summed neutrino mass limited to $\sum m_\nu < 0.241$ eV (at 95\%). If systematics on the group mass scale can be controlled, the eROSITA group and cluster sample with 43k objects and LSST WL could constrain $\Omega_\mathrm{M}$ and $\sigma_8$ to 0.007 and $w$ to 0.050.Comment: 28 pages, 13 figur

A Regression Model for Plasma Reaction Kinetics

Machine learning (ML) is used to provide reactions rates appropriate for models of low temperature plasmas with a focus on A + B $\rightarrow$ C + D binary chemical reactions. The regression model is trained on data extracted from the QBD, KIDA, NFRI and UfDA databases. The regression model used a variety of data on the reactant and product species, some of which also had to be estimated using ML. The final model is a voting regressor comprising three distinct optimized regression models: a support vector regressor, random forest regressor and a gradient-boosted trees regressor model; this model is made freely available via a GitHub repository. As a sample use case, the ML results are used to augment the chemistry of a BCl3/H2 gas mixture