Painting baryons onto N-body simulations of galaxy clusters with image-to-image deep learning

Galaxy cluster mass functions are a function of cosmology, but mass is not a direct observable, and systematic errors abound in all its observable proxies. Mass-free inference can bypass this challenge, but it requires large suites of simulations spanning a range of cosmologies and models for directly observable quantities. In this work, we devise a U-net - an image-to-image machine learning algorithm - to ``paint'' the IllustrisTNG model of baryons onto dark-matter-only simulations of galaxy clusters. Using 761 galaxy clusters with $M_{200c} \gtrsim 10^{14}M_\odot$ from the TNG-300 simulation at $z<1$, we train the algorithm to read in maps of projected dark matter mass and output maps of projected gas density, temperature, and X-ray flux. The models train in under an hour on two GPUs, and then predict baryonic images for $\sim2700$ dark matter maps drawn from the TNG-300 dark-matter-only (DMO) simulation in under two minutes. Despite being trained on individual images, the model reproduces the true scaling relation and scatter for the $M_{DM}-L_X$, as well as the distribution functions of the cluster X-ray luminosity and gas mass. For just one decade in cluster mass, the model reproduces three orders of magnitude in $L_X$. The model is biased slightly high when using dark matter maps from the DMO simulation. The model performs well on inputs from TNG-300-2, whose mass resolution is 8 times coarser; further degrading the resolution biases the predicted luminosity function high. We conclude that U-net-based baryon painting is a promising technique to build large simulated cluster catalogs which can be used to improve cluster cosmology by combining existing full-physics and large $N$-body simulations.Comment: Accepted to MNRA

Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses

We evaluate the effectiveness of deep learning (DL) models for reconstructing the masses of galaxy clusters using X-ray photometry data from next-generation surveys. We establish these constraints using a catalog of realistic mock eROSITA X-ray observations which use hydrodynamical simulations to model realistic cluster morphology, background emission, telescope response, and AGN sources. Using bolometric X-ray photon maps as input, DL models achieve a predictive mass scatter of $\sigma_{\ln M_\mathrm{500c}} = 18.3\%$, a factor of two improvements on scalar observables such as richness $N_\mathrm{gal}$, 1D velocity dispersion $\sigma_\mathrm{v,1D}$, and photon count $N_\mathrm{phot}$ as well as a $31\%$ improvement upon idealized, volume-integrated measurements of the bolometric X-ray luminosity $L_X$. We then show that extending this model to handle multichannel X-ray photon maps, separated in low, medium, and high energy bands, further reduces the mass scatter to $16.4\%$. We also tested a multimodal DL model incorporating both dynamical and X-ray cluster probes and achieved marginal gains at a mass scatter of $16.2\%$. Finally, we conduct a quantitative interpretability study of our DL models and find that they greatly down-weight the importance of pixels in the centers of clusters and at the location of AGN sources, validating previous claims of DL modeling improvements and suggesting practical and theoretical benefits for using DL in X-ray mass inference.Comment: 13 pages, 9 figures, 3 tables, submitted to MNRA

Predicting the impact of feedback on matter clustering with machine learning in CAMELS

Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation processes on the distribution of matter. We investigate the impact of baryonic physics on matter clustering at $z=0$ using a library of power spectra from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, containing thousands of $(25\,h^{-1}{\rm Mpc})^3$ volume realizations with varying cosmology, initial random field, stellar and AGN feedback strength and sub-grid model implementation methods. We show that baryonic physics affects matter clustering on scales $k \gtrsim 0.4\,h\,\mathrm{Mpc}^{-1}$ and the magnitude of this effect is dependent on the details of the galaxy formation implementation and variations of cosmological and astrophysical parameters. Increasing AGN feedback strength decreases halo baryon fractions and yields stronger suppression of power relative to N-body simulations, while stronger stellar feedback often results in weaker effects by suppressing black hole growth and therefore the impact of AGN feedback. We find a broad correlation between mean baryon fraction of massive halos ($M_{\rm 200c} > 10^{13.5}$\,\Msun) and suppression of matter clustering but with significant scatter compared to previous work owing to wider exploration of feedback parameters and cosmic variance effects. We show that a random forest regressor trained on the baryon content and abundance of halos across the full mass range $10^{10} \leq M_\mathrm{halo}/$\Msun$< 10^{15}$ can predict the effect of galaxy formation on the matter power spectrum on scales $k = 1.0$--20.0\,$h\,\mathrm{Mpc}^{-1}$

Astro2020 APC White Paper: The Early Career Perspective on the Coming Decade, Astrophysics Career Paths, and the Decadal Survey Process

In response to the need for the Astro2020 Decadal Survey to explicitly engage early career astronomers, the National Academies of Sciences, Engineering, and Medicine hosted the Early Career Astronomer and Astrophysicist Focus Session (ECFS) on October 8-9, 2018 under the auspices of Committee of Astronomy and Astrophysics. The meeting was attended by fifty six pre-tenure faculty, research scientists, postdoctoral scholars, and senior graduate students, as well as eight former decadal survey committee members, who acted as facilitators. The event was designed to educate early career astronomers about the decadal survey process, to solicit their feedback on the role that early career astronomers should play in Astro2020, and to provide a forum for the discussion of a wide range of topics regarding the astrophysics career path. This white paper presents highlights and themes that emerged during two days of discussion. In Section 1, we discuss concerns that emerged regarding the coming decade and the astrophysics career path, as well as specific recommendations from participants regarding how to address them. We have organized these concerns and suggestions into five broad themes. These include (sequentially): (1) adequately training astronomers in the statistical and computational techniques necessary in an era of "big data", (2) responses to the growth of collaborations and telescopes, (3) concerns about the adequacy of graduate and postdoctoral training, (4) the need for improvements in equity and inclusion in astronomy, and (5) smoothing and facilitating transitions between early career stages. Section 2 is focused on ideas regarding the decadal survey itself, including: incorporating early career voices, ensuring diverse input from a variety of stakeholders, and successfully and broadly disseminating the results of the survey