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@@ -68,7 +68,7 @@ Authors: E. M. Vavagiakis, S. McDermott, H. Awan, E. Ran, K. Banker, S. Usman, C
 
 # Summary
 
-Current and upcoming measurements of the cosmic microwave background (CMB), the oldest observable light in the universe, elucidate the fundamental physics of our universe, including the development of cosmic large-scale structure. Galaxy clusters are the largest gravitationally bound structures in our universe and make up a significant portion of this large-scale structure. Through measurements of galaxy clusters, we can derive insights into the growth of structure and place powerful constraints on cosmology. Simulations of galaxy clusters that are well-matched to upcoming data sets are a key tool for addressing systematics (e.g., cluster mass inference) that limit these current and future cluster-based cosmology constraints. However, most state-of-the-art simulations are too computationally intensive to produce multiple versions of significant  systematic effects: from underlying gas physics to observational modeling uncertainties. 
+Current and upcoming measurements of the cosmic microwave background (CMB), the oldest observable light in the universe, elucidate the fundamental physics of our universe, including the development of large-scale structure. Galaxy clusters are the largest gravitationally bound structures in our universe and serve as powerful probes of this large-scale structure [@BardeenBond1986, @1996BondMyers, @Bond_1996]. Through measurements of galaxy clusters, we can derive insights into the growth of structure and place significant constraints on cosmology. Simulations of galaxy clusters that are well-matched to upcoming data sets are a key tool for addressing systematics (e.g., cluster mass inference) that limit current and future cluster-based cosmology constraints. However, most state-of-the-art simulations are too computationally intensive to produce multiple versions of significant systematic effects: from underlying gas physics to observational modeling uncertainties. 
 
 We present DeepSZSim, a novel user-friendly Python framework for generating simulations of the CMB and the thermal Sunyaev–Zel’dovich (tSZ) effect in galaxy clusters, which is  based on average galaxy cluster thermal pressure profile models. DeepSZSim includes CMB power spectra generation using CAMB and simulated CMB temperature maps using `namaster` [@alonsoUnifiedPseudoC_2019], as well as tSZ signal modeling, instrument beam convolution, and noise. By tuning the input parameters based on a cosmology, distributions of halo mass and redshift, and experiment properties (e.g., map depth and observation frequency), users are able to generate a variety of simulated primary and secondary CMB anisotropy images. These simulations offer a fast and flexible method for generating large datasets to test mass inference methods like machine learning and simulation-based inference. 
 
@@ -90,7 +90,7 @@ The full package workflow is demonstrated in Figure \autoref{fig:workflow}.
 
 
 
-## DeepCMBSim Modul
+## DeepCMBSim Module
 
 The `DeepCMBsim` package combines physical processes and sources of noise in a software framework that enables fast and realistic simulation of the CMB in which key cosmological parameters can be varied. DeepCMBSim simulates correlations of temperatures and polarization signals from the CMB, including large-scale gravitational lensing and BB polarization caused by non-zero tensor-to-scalar ratios.
 
@@ -109,7 +109,7 @@ We present examples of the primary outputs from `DeepCMBSim` in  Figure \autoref
 
 The user provides inputs to generate an array of redshift and mass ($M_200$) for dark matter halos, the desired pixel and submap size for the output submaps, and inputs such as experiment properties (observation frequency, noise level, beam size) and a cosmological model. These inputs are easily customizable, or the user can run defaults based on the Atacama Cosmology Telescope `[@ACT:2021]` and Planck cosmology `[@Planck:2019]`. Cosmology computations depend on `colossus` `[@Colossus:2018]` and `astropy` `[@Astropy:2013]`.
 
-From these inputs, pressure profiles [cite], Compton-y profiles [cite], and tSZ signal maps are generated for the dark matter halo array. Simulated CMB primary anisotropy maps can be generated through a dependency on `DeepCMBSim`. Final simulated submaps can include instrument beam convolution and white noise `[@actnotebooks:2015]`. Plotting functions for the simulations and an aperture photometry filter are included as tools. The submap handling functions rely on `pixell` `[@pixell:2024]`.
+From these inputs, pressure profiles, Compton-y profiles, and tSZ signal maps are generated for the dark matter halo array [@Kaiser1986, @Arnaud_2010, @Battaglia:2012]. Simulated CMB primary anisotropy maps can be generated through a dependency on `DeepCMBSim`. Final simulated submaps can include instrument beam convolution and white noise `[@actnotebooks:2015]`. Plotting functions for the simulations and an aperture photometry filter are included as tools. The submap handling functions rely on `pixell` `[@pixell:2024]`.
 
 ![Example outputs for the `DeepSZsim` package for a set of masses, redshifts, and noise configurations.\label{fig:sz}](figures/SZCluster_Examples.png)