Efficient Long-range Active Galactic Nuclei (AGNs) Feedback Affects the Low-redshift Lyα Forest

Tillman, Megan; Burkhart, Blakesley; Tonnesen, Stephanie; Bird, Simeon; Bryan, Greg L.; Anglés-Alcázar, Daniel; Davé, Romeel; Genel, Shy

doi:10.3847/2041-8213/acb7f1

Cited by 14 publications

(25 citation statements)

References 77 publications

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“…Additionally, absorbers at the highest CDs are rarer, which makes them difficult to study both observationally and in the small-box (25 Mpc h −1 ) 3 CAMELS simulations. Tillman et al (2023) found that, at least for TNG, the CAMELS boxes produce a converged CDD as compared to the original TNG100-1 simulation, within observational error bars.…”

Section: Synthetic Spectramentioning

confidence: 61%

“…We generate 5000 sightlines randomly placed in each simulation box-a number found to be sufficient for avoiding variations due to sampling (Tillman et al 2023). We do not add noise to the spectra generated from the simulation box.…”

Section: Synthetic Spectramentioning

confidence: 99%

“…While the factor of five may be partly attributed to poor resolution, it was later shown that a large increase in the UVB magnitude from Haardt & Madau (2012; the UVB model studied in Kollmeier et al 2014) is possible (Khaire & Srianand 2015;Puchwein et al 2019;Bolton et al 2022). As for additional heating sources, several studies explore the potential impact of AGN feedback in heating the IGM, with some finding that different AGN feedback models can have a dramatic impact on the low-z Lyα forest statistics (Gurvich et al 2017;Nasir et al 2017;Christiansen et al 2020;Burkhart et al 2022;Tillman et al 2023;Khaire et al 2023). Fortunately, modern-day simulations have put a greater emphasis on incorporating feedback mechanisms through sophisticated modeling based on observations (e.g., Vogelsberger et al 2014Vogelsberger et al , 2014Hopkins et al 2014Hopkins et al , 2018McAlpine et al 2016;Weinberger et al 2017;Kaviraj et al 2017;Davé et al 2019;Bird et al 2022, andVogelsberger et al 2020 for a review), making the study of feedback effects on the low-z forest possible through simulated data.…”

Section: Introductionmentioning

confidence: 99%

“…While the effects of some AGN feedback models on the statistics of the forest are degenerate with a rescaling of the assumed UVB (e.g., see Burkhart et al 2022;Khaire et al 2023;Mallik et al 2023), other AGN models had different, idiosyncratic effects, highlighting the importance of including feedback subgrid models within cosmological simulations (Tillman et al 2023). The unique effects seen on the Lyα forest column density distribution function (CDD) due to AGN jet feedback (Tillman et al 2023) are primarily attributed to the far-reaching heating effects that the jet feedback has on the large-scale environment (see Tonnesen et al 2017 for a discussion of the dependence of the CDD on the large-scale environment). These results emphasize the need for an improved knowledge of the physical conditions of the IGM and how they vary on large scales.…”

Section: Introductionmentioning

confidence: 99%

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An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low-redshift Lyα Forest

Tillman,

Burkhart,

Tonnesen

et al. 2023

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We explore the role of galactic feedback on the low-redshift Lyα (Lyα) forest (z ≲ 2) statistics and its potential to alter the thermal state of the intergalactic medium. Using the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) suite, we explore variations of the AGN and stellar feedback models in the IllustrisTNG and Simba subgrid models. We find that both AGN and stellar feedback in Simba play a role in setting the Lyα forest column density distribution function (CDD) and the Doppler width (b-value) distribution. The Simba AGN jet feedback mode is able to efficiently transport energy out to the diffuse IGM, causing changes in the shape and normalization of the CDD and a broadening of the b-value distribution. We find that stellar feedback plays a prominent role in regulating supermassive black hole growth and feedback, highlighting the importance of constraining stellar and AGN feedback simultaneously. In IllustrisTNG, the AGN feedback variations explored in CAMELS do not affect the Lyα forest, but varying the stellar feedback model does produce subtle changes. Our results imply that the low-z Lyα forest can be sensitive to changes in the ultraviolet background, stellar and black hole feedback, and that AGN jet feedback in particular can have a strong effect on the thermal state of the IGM.

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Section: Synthetic Spectramentioning

confidence: 61%

Section: Synthetic Spectramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low-redshift Lyα Forest

Tillman,

Burkhart,

Tonnesen

et al. 2023

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“…While all three of these simulations show a decline in the incidence rate of O VI absorption as a result of integrated black hole feedback, they implement the feedback in different ways (Schaye et al 2015;Weinberger et al 2017;Davé et al 2019). The feedback schemes lead to different values for bulk CGM properties such as CGM mass as a fraction of halo mass (e.g., Davies et al 2021;Tillman et al 2023). It is therefore plausible that while black hole feedback is involved in all three cases, the actual mechanisms by which O VI incidence is suppressed are not the same.…”

Section: Galaxy Quenching and Cgm Transformationmentioning

confidence: 99%

The CGM² Survey: Quenching and the Transformation of the Circumgalactic Medium

Tchernyshyov

Werk

Wilde

et al. 2023

ApJ

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This study addresses how the incidence rate of strong O vi absorbers in a galaxy’s circumgalactic medium (CGM) depends on galaxy mass and, independently, on the amount of star formation in the galaxy. We use Hubble Space Telescope/Cosmic Origins Spectrograph absorption spectroscopy of quasars to measure O vi absorption within 400 projected kpc and 300 km s−1 of 52 galaxies with M * ∼ 3 × 1010 M ⊙. The galaxies have redshifts 0.12 < z < 0.6, stellar masses 1010.1 M ⊙ < M * < 1010.9 M ⊙, and spectroscopic classifications as star-forming or passive. We compare the incidence rates of high column density O vi absorption (N O VI ≥ 1014.3 cm−2) near star-forming and passive galaxies in two narrow ranges of stellar mass and, separately, in a matched range of halo mass. In all three mass ranges, the O vi covering fraction within 150 kpc is higher around star-forming galaxies than around passive galaxies with greater than 3σ-equivalent statistical significance. On average, the CGM of star-forming galaxies with M * ∼ 3 × 1010 M ⊙ contains more O vi than the CGM of passive galaxies with the same mass. This difference is evidence for a CGM transformation that happens together with galaxy quenching and is not driven primarily by halo mass.

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FLAME: Fitting Lyαabsorption lines using machine learning

Jalan,

Khaire,

Vivek

et al. 2024

A&A

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We introduce FLAME, a machine-learning algorithm designed to fit Voigt profiles to H i Lyman-alpha ( absorption lines using deep convolutional neural networks. FLAME integrates two algorithms: the first determines the number of components required to fit absorption lines, and the second calculates the Doppler parameter $b$, the H i column density N$_ HI $, and the velocity separation of individual components. For the current version of FLAME, we trained it on low-redshift forests observed with the far-ultraviolet gratings of the Cosmic Origin Spectrograph (COS) on board the Hubble Space Telescope (HST). Using these data, we trained FLAME on sim $10^6$ simulated Voigt profiles ---which we forward-modeled to mimic absorption lines observed with HST-COS--- in order to classify lines as either single or double components and then determine Voigt profile-fitting parameters. FLAME shows impressive accuracy on the simulated data, identifying more than 98<!PCT!> (90<!PCT!>) of single (double) component lines. It determines $b$ values within $ (15)$ km s$^ $ and log $N_ HI cm ^2$ values within $ 0.3 (0.8)$ for 90<!PCT!> of the single (double) component lines. However, when applied to real data, FLAME's component classification accuracy drops by sim 10<!PCT!>. Nevertheless, there is reasonable agreement between the $b$ and HI $ distributions obtained from traditional Voigt profile-fitting methods and FLAME's predictions. Our mock HST-COS data analysis, designed to emulate real data parameters, demonstrates that FLAME is able to achieve consistent accuracy comparable to its performance with simulated data. This finding suggests that the drop in FLAME's accuracy when used on real data primarily arises from the difficulty in replicating the full complexity of real data in the training sample. In any case, FLAME's performance validates the use of machine learning for Voigt profile fitting, underscoring the significant potential of machine learning for detailed analysis of absorption lines.

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Efficient Long-range Active Galactic Nuclei (AGNs) Feedback Affects the Low-redshift Lyα Forest

Cited by 14 publications

References 77 publications

An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low-redshift Lyα Forest

An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low-redshift Lyα Forest

The CGM² Survey: Quenching and the Transformation of the Circumgalactic Medium

FLAME: Fitting Lyαabsorption lines using machine learning

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Efficient Long-range Active Galactic Nuclei (AGNs) Feedback Affects the Low-redshift Lyα Forest

Cited by 14 publications

References 77 publications

An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low-redshift Lyα Forest

An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low-redshift Lyα Forest

The CGM2 Survey: Quenching and the Transformation of the Circumgalactic Medium

FLAME: Fitting Lyαabsorption lines using machine learning

Contact Info

Product

Resources

About

The CGM² Survey: Quenching and the Transformation of the Circumgalactic Medium