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<li><a href="https://bsky.app/profile/aasnova.bsky.social" class="icon fa fa-bluesky" title="Bluesky"><span class="visuallyhidden">Bluesky</span></a></li> </ul></div> </nav> </div> </div> <div class="main wrap cf"> <div class="row"> <div class="col-8 main-content"> <article id="post-13290" class="post-13290 post type-post status-publish format-standard has-post-thumbnail category-astrobites category-highlights tag-active-galactic-nuclei tag-cosmology tag-galaxies tag-hydrodynamic-simulations title-above" itemscope itemtype="http://schema.org/Article"> <header class="post-header cf"> <div class="heading cf"> <h1 class="post-title item fn" itemprop="name"> Feeding CAMELS: An Exploration of Galactic Feedback Effects in Simulations </h1>  </div> <div class="post-meta cf"> <span class="posted-by">By <span class="reviewer" itemprop="author"><a href="https://aasnova.org/person/astrobites/" title="Astrobites">Astrobites</a></span> </span> <span class="posted-on">on <span class="dtreviewed"> <time class="value-title" datetime="2025-02-18T12:00:38-05:00" title="2025-02-18" itemprop="datePublished">18 February 2025</time> </span> </span> <span class="subcat">Astrobites</span> <span class="cats"> <div class="post-share"> <span class="text">Share:</span> <span class="share-links">  <a href="https://twitter.com/intent/tweet?url=https%3A%2F%2Faasnova.org%2F2025%2F02%2F18%2Ffeeding-camels-an-exploration-of-galactic-feedback-effects-in-simulations%2F" class="fa fa-twitter" title="Tweet It"> <span class="visuallyhidden">Twitter</span></a> <a href="http://www.facebook.com/sharer.php?u=https%3A%2F%2Faasnova.org%2F2025%2F02%2F18%2Ffeeding-camels-an-exploration-of-galactic-feedback-effects-in-simulations%2F" class="fa fa-facebook" title="Share on Facebook"> <span class="visuallyhidden">Facebook</span></a> <a href="http://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Faasnova.org%2F2025%2F02%2F18%2Ffeeding-camels-an-exploration-of-galactic-feedback-effects-in-simulations%2F" 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class="featured"> <a href="https://aasnova.org/wp-content/uploads/2025/01/m82-jpg.jpg" title="Feeding CAMELS: An Exploration of Galactic Feedback Effects in Simulations" itemprop="image"> <img width="702" height="336" src="https://aasnova.org/wp-content/uploads/2025/01/m82-jpg-702x336.jpg" class="attachment-main-slider size-main-slider wp-post-image" alt="Messier 82" title="Feeding CAMELS: An Exploration of Galactic Feedback Effects in Simulations" decoding="async" fetchpriority="high" srcset="https://aasnova.org/wp-content/uploads/2025/01/m82-jpg-702x336.jpg 702w, https://aasnova.org/wp-content/uploads/2025/01/m82-jpg-1078x516.jpg 1078w" sizes="(max-width: 702px) 100vw, 702px" /> </a> <div class="caption">The Cigar Galaxy, Messier 82, as seen by the Hubble Space Telescope. This galaxy is a starburst galaxy with a star-formation rate 10 times higher than that of the Milky Way. [<a href="https://science.nasa.gov/mission/hubble/science/explore-the-night-sky/hubble-messier-catalog/messier-82/">NASA, ESA and the Hubble Heritage Team (STScI/AURA); Acknowledgment: J. Gallagher (University of Wisconsin), M. Mountain (STScI) and P. Puxley (National Science Foundation)</a>]</div> </div> </header> <div class="post-container cf"> <div class="post-content-right"> <div class="post-content description " itemprop="articleBody"> <blockquote><p><em>Editor’s Note:</em> <a href="http://astrobites.org" target="_blank" rel="noopener noreferrer">Astrobites</a> is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the <a href="https://aas.org/posts/news/2016/06/aas-and-astrobites-formalize-partnership" target="_blank" rel="noopener noreferrer">partnership</a> between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at <a href="http://astrobites.org" target="_blank" rel="noopener noreferrer">astrobites.org</a>.</p></blockquote> <p><strong>Title: </strong><a href="https://doi.org/10.3847/1538-4357/ada442">Quantifying Baryonic Feedback on Warm-Hot Circumgalactic Medium in CAMELS Simulations</a><br /> <strong>Authors: </strong>Isabel Medlock et al.<br /> <strong>First Author’s Institution: </strong>Yale University<br /> <strong>Status: </strong>Published in <em>ApJ</em></p> <h4 class="wp-block-heading">Simulating the Universe</h4> <p>Galaxies are complex ecosystems with a wide variety of physical processes impacting how they form and evolve. Studying these processes is often quite difficult, in part because we can’t actually travel to galaxies and poke and prod at them to see what’s going on. So we have to turn to another handy tool in the astrophysicist’s toolkit: simulations. Cosmological <a href="https://ned.ipac.caltech.edu/level5/Sept13/Silk/Silk5.html">hydrodynamic simulations</a> can model how giant swaths of the universe have evolved since the universe’s early years, and take into account a wide range of processes ranging from <a href="https://astrobites.org/2017/11/06/looking-for-structure-in-dark-matter-with-gravitational-lensing/">dark matter structure formation</a> to <a href="https://en.wikipedia.org/wiki/Star_formation" data-type="link" data-id="https://en.wikipedia.org/wiki/Star_formation">star formation</a> to <a href="https://www.cfa.harvard.edu/research/topic/galaxies-merging-and-interacting">galaxy mergers</a>. But because these simulations are so large and complex, there is a limit to how small of a scale can be resolved, and a lot of the activity falls into what we call “<a href="https://astrobites.org/2020/05/06/shaken-not-stirred-subgrid-metal-diffusion-in-galaxy-simulations/">subgrid physics</a>”: phenomena that occur on scales below a simulation’s resolution. Instead of modeling activity from foundational behavior, we have to approximate what impact the subgrid physics would have on the scales we can resolve. For example, instead of directly modeling how a <a href="https://astrobites.org/2023/09/16/today-in-astro-shop-class-how-to-build-a-molecular-cloud/">molecular cloud</a> collapses, we would say if a volume of gas reaches some critical density it automatically forms some amount of stars with some <a href="https://astrobites.org/2011/08/14/a-simple-explanation-for-the-slope-of-the-initial-mass-function/">initial mass function</a>. (If that sounds a bit hand-wavy, that’s because it is, but we gotta work with what we’ve got, and right now we don’t have the computing power to model all the scales of astrophysics at once.)</p> <p>Some of the most important subgrid processes are stellar and black hole feedback. Stellar feedback refers to how star-related events like star formation and supernovae deposit energy into the <a href="https://en.wikipedia.org/wiki/Interstellar_medium#:~:text=The%20interstellar%20medium%20(ISM)%20is,into%20the%20surrounding%20intergalactic%20space.">interstellar medium</a>, which can impact the galactic environment significantly, slowing down star formation and even ejecting material from the galaxy entirely. Black hole feedback (sometimes called active galactic nucleus or <a href="https://astrobites.org/2023/11/25/its-nap-time-does-agn-feedback-cause-our-universe-to-take-a-cosmic-afternoon-nap/">AGN feedback</a>) comes from activity around the supermassive black holes found in the centers of practically every large galaxy. As material accretes around the black hole, the material can heat up or even be ejected at high velocities, causing large amounts of energy to be expelled into and around the host galaxy. Between stars and black holes, these feedback processes have a large impact, and without their inclusion in simulations the results look nothing like the universe as we observe it.</p> <h4 class="wp-block-heading">A Flock of Camels</h4> <p>There are many cosmological simulations widely used today, but comparing their differing feedback implementations can be difficult as other parameters (i.e., initial conditions, cosmological parameters, resolution and simulation volume) are varied. The simulations used in this work come from the <span class="wixui-rich-text__text"><span class="color_18 wixui-rich-text__text">C</span>osmology and Astrophysics with <span class="color_33 wixui-rich-text__text">M</span>achinE </span><span class="wixui-rich-text__text">Learning Simulations (</span><a href="https://www.camel-simulations.org/">CAMELS</a>) project, which provides a suite of model runs that change feedback parameters but keep everything else constant. CAMELS pulls feedback implementations from several simulations, but this work looks specifically at comparing the simulations <a href="http://simba.roe.ac.uk/">SIMBA</a> and <a href="https://www.tng-project.org/">IllustrisTNG</a>. For each of these simulations, CAMELS identifies four parameters that drive feedback (two each for stellar and black hole processes) and provides a range of runs that span this parameter space. These parameters describe things like the mass loading and speed of winds, accretion rates, and energy and momentum flux.</p> <p>SIMBA and IllustrisTNG are broadly similar in their feedback treatments, but they have a few key differences. To model stellar feedback, both codes heat and move around gas particles, but the means in which they calculate temperature gains and velocities are different. Both simulations have two modes of AGN feedback: one associated with low <a href="https://science.nasa.gov/universe/black-holes/anatomy/">accretion rates</a> around the black hole, and the other associated with high accretion rates. For IllustrisTNG, when accretion rates are high the feedback is all thermal, heating nearby gas particles. At lower accretion rates, the feedback is more kinetic, where particles are ejected with energies set by the CAMELS black hole feedback parameters. In SIMBA, both AGN modes have some form of kinetic feedback, with high accretion rates corresponding to lower velocities.</p> <p>This work compares how the different feedback treatments in CAMELS-SIMBA and CAMELS-IllustrisTNG impact black hole growth, feedback energies, and gas distribution in and around galaxies. The authors do this by comparing two different values: f<sub>CGM</sub> and the closure radius. The quantity f<sub>CGM</sub> provides a ratio of the mass found in the circumgalactic medium (the region surrounding a galaxy) relative to the total mass found in the halo. The closure radius takes a different approach, by looking at the physical scale at which the ratio between the mass of baryons (gas, stars, and black holes) and all the mass (including dark matter) is the same as the ratio for the universe as a whole. The measurement provides a way of determining the scale at which matter is associated with a given galaxy.</p> <h4 class="wp-block-heading">Some Feedback on the Feedback</h4> <div id="attachment_13293" style="width: 270px" class="wp-caption alignright"><a href="https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-scaled.jpg" data-rel="lightbox-image-0" data-rl_title="Figure 1: Relationship between the halo mass and closure radius in different simulations run with varying stellar feedback strengths. Here, we can see the ability of SIMBA (purple) to disperse gas to a larger radius than IllustrisTNG (green). [Adapted from Medlock et al. 2025]" data-rl_caption="Figure 1: Relationship between the halo mass and closure radius in different simulations run with varying stellar feedback strengths. Here, we can see the ability of SIMBA (purple) to disperse gas to a larger radius than IllustrisTNG (green). [Adapted from Medlock et al. 2025]" title="Figure 1: Relationship between the halo mass and closure radius in different simulations run with varying stellar feedback strengths. Here, we can see the ability of SIMBA (purple) to disperse gas to a larger radius than IllustrisTNG (green). [Adapted from Medlock et al. 2025]"><img decoding="async" aria-describedby="caption-attachment-13293" class="size-thumbnail wp-image-13293" src="https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-260x264.jpg" alt="Relationship between the halo mass and closure radius" width="260" height="264" srcset="https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-260x264.jpg 260w, https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-702x714.jpg 702w, https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-768x781.jpg 768w, https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-600x610.jpg 600w, https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-1510x1536.jpg 1510w, https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-2013x2048.jpg 2013w, https://aasnova.org/wp-content/uploads/2025/01/runningmed_closrad_4params-1320x1343.jpg 1320w" sizes="(max-width: 260px) 100vw, 260px" /></a><p id="caption-attachment-13293" class="wp-caption-text">Figure 1: Relationship between the halo mass and closure radius in different simulations run with varying stellar feedback strengths. Here, we can see the ability of SIMBA (purple) to disperse gas to a larger radius than IllustrisTNG (green). [Adapted from Medlock et al. 2025]</p></div>Overall, this work found that the variations in feedback processes not only impacted the gas distribution and galaxy properties, but also that the links between stellar and AGN feedback are important and vary between different simulations. In general, the feedback implemented by the IllustrisTNG code had a higher energy than that from SIMBA. However, SIMBA had a greater impact on the baryon distribution, with larger closure radii than the IllustrisTNG runs, as shown in Figure 1. When examining links between stellar and black hole feedback, the authors found that improving the efficiencies of stellar feedback weakened AGN feedback in IllustrisTNG but slightly strengthened it in SIMBA. Figure 2 shows how varying one of the stellar feedback parameters changed the energy of an AGN feedback mode. Finally, the authors looked at changes over time; earlier in the universe, AGN feedback was rarer than stellar activity, and the complexities of their interplay did not become important until redshifts of <em>z</em> < 2.</p> <div id="attachment_13292" style="width: 712px" class="wp-caption aligncenter"><a href="https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-scaled.jpg" data-rel="lightbox-image-1" data-rl_title="Figure 2: Relationship between the halo mass and energy coming from the thermal AGN mode in IllustrisTNG (left) and SIMBA (right). The different colors represent different strengths of stellar feedback, with purple being the lowest and orange the highest. Here we see how changing the stellar feedback parameters impacts the black hole feedback, with stronger stellar feedback corresponding to lower AGN energies in IllustrisTNG, but slightly higher energies in SIMBA. [Adapted from Medlock et al. 2025]" data-rl_caption="Figure 2: Relationship between the halo mass and energy coming from the thermal AGN mode in IllustrisTNG (left) and SIMBA (right). The different colors represent different strengths of stellar feedback, with purple being the lowest and orange the highest. Here we see how changing the stellar feedback parameters impacts the black hole feedback, with stronger stellar feedback corresponding to lower AGN energies in IllustrisTNG, but slightly higher energies in SIMBA. [Adapted from Medlock et al. 2025]" title="Figure 2: Relationship between the halo mass and energy coming from the thermal AGN mode in IllustrisTNG (left) and SIMBA (right). The different colors represent different strengths of stellar feedback, with purple being the lowest and orange the highest. Here we see how changing the stellar feedback parameters impacts the black hole feedback, with stronger stellar feedback corresponding to lower AGN energies in IllustrisTNG, but slightly higher energies in SIMBA. [Adapted from Medlock et al. 2025]"><img decoding="async" aria-describedby="caption-attachment-13292" class="size-medium wp-image-13292" src="https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-702x346.jpg" alt="Plot showing the relationship between the halo mass and energy" width="702" height="346" srcset="https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-702x346.jpg 702w, https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-260x128.jpg 260w, https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-768x378.jpg 768w, https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-600x296.jpg 600w, https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-1536x757.jpg 1536w, https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-2048x1009.jpg 2048w, https://aasnova.org/wp-content/uploads/2025/01/asn1_decompose_efb-1320x650.jpg 1320w" sizes="(max-width: 702px) 100vw, 702px" /></a><p id="caption-attachment-13292" class="wp-caption-text">Figure 2: Relationship between the halo mass and energy coming from the thermal AGN mode in IllustrisTNG (left) and SIMBA (right). The different colors represent different strengths of stellar feedback, with purple being the lowest and orange the highest. Here we see how changing the stellar feedback parameters impacts the black hole feedback, with stronger stellar feedback corresponding to lower AGN energies in IllustrisTNG, but slightly higher energies in SIMBA. [Adapted from Medlock et al. 2025]</p></div>Overall, these results indicate that one cannot simply treat stellar and black hole feedback independently (insert Boromir meme here). These processes are related in complex ways that are currently not fully understood. The fact that different simulations have different interplay (sometimes with directly opposite results!) points to a need for further constraining of these subgrid models going forward.</p> <p><em>Original astrobite edited by Lucie Rowland.</em></p> <blockquote> <h4>About the author, <span class="ppma-category-group ppma-category-group-1 category-index-0">Skylar Grayson</span>:</h4> <p><span class="ppma-category-group ppma-category-group-1 category-index-0">Skylar Grayson is an Astrophysics PhD Candidate and NSF Graduate Research Fellow at Arizona State University. Her primary research focuses on active galactic nucleus feedback processes in cosmological simulations. She also works in astronomy education research, studying online learners in both undergraduate and free-choice environments. 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