Limits on non-canonical heating and turbulence in the intergalactic medium from the low redshift Lyman α forest

Bolton, James S.; Gaikwad, Prakash; Haehnelt, Martin G.; Kim, Tae-Sun; Nasir, Fahad; Puchwein, Ewald; Viel, Matteo; Wakker, Bart P.

doi:10.1093/mnras/stac862

Cited by 15 publications

(26 citation statements)

References 141 publications

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“…At the same time, the simulations over predict absorbers below 20 km s −1 . Using a similar analysis, Bolton et al (2021) found that the simulated b distribution in the IllustrisTNG simulations are also systematically lower than what is observed.…”

Section: Introductionmentioning

confidence: 64%

“…Changes to the UVB model and their effects on the IGM at z< 0.5 have been explored in other works (e.g. Shull et al 2015;Gaikwad et al 2017;Khaire et al 2019a;Bolton et al 2021) therefore we focus on studying the differences between the AGN feedback sub-grid models. This direct comparison is made possible by the fact that TNG keeps the simulation properties known to affect the Lyman-α forest the same as they were in Illustris (e.g.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…a stronger UVB results in a lower density of neutral hydrogen along a LOS at all column densities). Typical spectral indices in UVB models at z ≈ 0.1 are between −1.0 and −1.5 (Bolton et al 2021). Harder spectral indices correspond to more ionizing photons, reducing the amplitude of the CDD.…”

Section: The Column Density Distributionmentioning

confidence: 99%

“…Like the CDD, the distribution of absorption line Doppler widths (i.e. the b-parameter distribution) is consistently found to be mismatched between hydrodynamic simulations and observations at low redshift (Viel et al 2017;Bolton et al 2021). Viel et al (2017) first explored the b distribution for the Illustris, Sherwood, and GADGET-3 simulations and found that all three simulations underpredict the number of absorbers at b = 25 − 45km s −1 .…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the integrated strength, the nuances of the AGN sub-grid model matter substantially for the simulated Lyman-α forest properties. For example, the Sherwood cosmological simulations have generally found a limited impact of AGN feedback on the Lyman-α forest line widths, with most hot gas from AGN too hot and highly ionized for Lyman-α absorption (Viel et al 2017;Nasir et al 2017;Bolton et al 2021). Their proposed solution is additional sources of turbulence in the IGM and heating from IGM dust.…”

Section: Introductionmentioning

confidence: 99%

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The low redshift Lyman-$α$ Forest as a constraint for models of AGN feedback

Burkhart,

Tillman,

Gurvich

et al. 2022

Preprint

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We study the low redshift Lyman-α Forest in the Illustris and IllustrisTNG (TNG) cosmological simulations to demonstrate their utility in constraining aspects of sub-grid models of feedback from active galactic nuclei (AGN). The two simulations share an identical Ultraviolet Background prescription and similar cosmological parameters, but TNG features an entirely reworked AGN feedback model. Therefore a comparison of these simulations is useful to assess the effects of an altered AGN sub-grid model on the low redshift Lyman-α Forest. We find significant differences in the IGM temperaturedensity relation between the two simulations due to changes in the gas heating rate due to AGN. We investigate Lyman-α Forest observables such as the column density distribution function, flux PDF, and Doppler width (b-parameter) distribution. Due to the AGN radio mode model, the original Illustris simulations have a factor of 2-3 fewer absorbers than TNG at column densities N HI < 10 15.5 cm −2 . We show that TNG is in much better agreement with the observed z = 0.1 flux power spectrum than Illustris. The differences in the amplitude and shape of the flux PDF and power spectrum between Illustris and TNG cannot be attributed to simple changes in the photoheating rate. We also compare the simulated Forest statistics to UV data from the Cosmic Origins Spectrograph (COS) and find that neither simulation can reproduce the slope of the absorber distribution. Both Illustris and TNG also produce significantly smaller b-parameter distributions than observed in the COS data, possibly due to unresolved or missing sources of turbulence.

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Section: Introductionmentioning

confidence: 64%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: The Column Density Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The low redshift Lyman-$α$ Forest as a constraint for models of AGN feedback

Burkhart,

Tillman,

Gurvich

et al. 2022

Preprint

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FLAME: Fitting Lyα absorption 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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Measuring the thermal and ionization state of the low-z IGM using likelihood free inference

Teng

Khaire

Walther

et al. 2022

Monthly Notices of the Royal Astronomical Society

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We present a new approach to measure the power-law temperature density relationship $T=T_0 (\rho \ / \bar{\rho })^{\gamma -1}$ and the UV background photoionization rate $\Gamma _{{{{\rm H\, {\small i}}}}{}}$ of the intergalactic medium(IGM) based on the Voigt profile decomposition of the Lyα forest into a set of discrete absorption lines with Doppler parameter b and the neutral hydrogen column density $N_{\rm H\, {\small i}}$. Previous work demonstrated that the shape of the $b-N_{{{{\rm H\, {\small i}}}}{}}$ distribution is sensitive to the IGM thermal parameters T0 and γ, whereas our new inference algorithm also takes into account the normalization of the distribution, i.e. the line-density dN/dz, and we demonstrate that precise constraints can also be obtained on $\Gamma _{{{{\rm H\, {\small i}}}}{}}$. We use density-estimation likelihood-free inference (DELFI) to emulate the dependence of the $b-N_{{{{\rm H\, {\small i}}}}{}}$ distribution on IGM parameters trained on an ensemble of 624 Nyx hydrodynamical simulations at z = 0.1, which we combine with a Gaussian process emulator of the normalization. To demonstrate the efficacy of this approach, we generate hundreds of realizations of realistic mock HST/COS datasets, each comprising 34 quasar sightlines, and forward model the noise and resolution to match the real data. We use this large ensemble of mocks to extensively test our inference and empirically demonstrate that our posterior distributions are robust. Our analysis shows that by applying our new approach to existing Lyα forest spectra at z ≃ 0.1, one can measure the thermal and ionization state of the intergalactic medium(IGM) with very high precision ($\sigma _{\log T_0} \sim 0.08$ dex, σγ ∼ 0.06, and $\sigma _{\log \Gamma _{{{{\rm H\, {\small i}}}}{}}} \sim 0.07$ dex).

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Limits on non-canonical heating and turbulence in the intergalactic medium from the low redshift Lyman α forest

Cited by 15 publications

References 141 publications

The low redshift Lyman-$α$ Forest as a constraint for models of AGN feedback

The low redshift Lyman-$α$ Forest as a constraint for models of AGN feedback

FLAME: Fitting Lyα absorption lines using machine learning

Measuring the thermal and ionization state of the low-z IGM using likelihood free inference

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