A Comparison and Joint Analysis of Sunyaev-Zel'dovich Effect Measurements from Planck and Bolocam for a set of 47 Massive Galaxy Clusters

We measure the SZ signal toward a set of 47 clusters with a median mass of $9.5 \times 10^{14}$ M$_{\odot}$ and a median redshift of 0.40 using data from Planck and the ground-based Bolocam receiver. When Planck XMM-like masses are used to set the scale radius $\theta_{\textrm{s}}$, we find consistency between the integrated SZ signal, $Y_{\textrm{5R500}}$, derived from Bolocam and Planck based on gNFW model fits using A10 shape parameters, with an average ratio of $1.069 \pm 0.030$ (allowing for the $\simeq 5$% Bolocam flux calibration uncertainty). We also perform a joint fit to the Bolocam and Planck data using a modified A10 model with the outer logarithmic slope $\beta$ allowed to vary, finding $\beta = 6.13 \pm 0.16 \pm 0.76$ (measurement error followed by intrinsic scatter). In addition, we find that the value of $\beta$ scales with mass and redshift according to $\beta \propto M^{0.077 \pm 0.026} \times (1+z)^{-0.06 \pm 0.09}$. This mass scaling is in good agreement with recent simulations. We do not observe the strong trend of $\beta$ with redshift seen in simulations, though we conclude that this is most likely due to our sample selection. Finally, we use Bolocam measurements of $Y_{500}$ to test the accuracy of the Planck completeness estimate. We find consistency, with the actual number of Planck detections falling approximately $1 \sigma$ below the expectation from Bolocam. We translate this small difference into a constraint on the the effective mass bias for the Planck cluster cosmology results, with $(1-b) = 0.93 \pm 0.06$.Comment: Updated to include one additional co-author. Also some minor changes to the text based on initial feedbac

A Comparison and Joint Analysis of Sunyaev-Zel'dovich Effect Measurements from Planck and Bolocam for a set of 47 Massive Galaxy Clusters

We measure the Sunyaev–Zel'dovich (SZ) signal toward a set of 47 clusters with a median mass of 9.5 × 10^(14) M_☉ and a median redshift of 0.40 using data from Planck and the ground-based Bolocam receiver. When Planck XMM-like masses are used to set the scale radius θ_s, we find consistency between the integrated SZ signal, Y_(5R500), derived from Bolocam and Planck based on generalized Navarro, Frenk, and White model fits using A10 shape parameters, with an average ratio of 1.069 ± 0.030 (allowing for the ≃ 5% Bolocam flux calibration uncertainty). We also perform a joint fit to the Bolocam and Planck data using a modified A10 model with the outer logarithmic slope β allowed to vary, finding β = 6.13 ± 0.16 ± 0.76 (measurement error followed by intrinsic scatter). In addition, we find that the value of β scales with mass and redshift according to β ∝ M^(0.077 ± 0.026) x (1 + z))^(-0.06 ± 0.09). This mass scaling is in good agreement with recent simulations. We do not observe the strong trend of β with redshift seen in simulations, though we conclude that this is most likely due to our sample selection. Finally, we use Bolocam measurements of Y 500 to test the accuracy of the Planck completeness estimate. We find consistency, with the actual number of Planck detections falling approximately 1σ below the expectation from Bolocam. We translate this small difference into a constraint on the effective mass bias for the Planck cluster cosmology results, with (1 - b)=0.93 ± 0.06

Using Sports Videos to Showcase Exciting Content to Viewers

In this thesis, we explore the task of generating highlight videos from sports games through the means of assessing the level of excitement of such videos to extract interesting moments from a game as well as utilize NLP techniques to generate captions for such videos. We create pipelines for the extraction of highlight clips using an audio heuristic for which we obtain transcriptions and, using a defined schema for exciting captions, fine-tune pre-trained transformer models to extract the best sentence from the video clip to use as a caption. Our results show improvements over baselines that solely use emotion-prediction categories of input sentences, suggesting our models are able to learn additional features to determine the excitement of captions.M.Eng