77 lines
2.0 KiB
Typst
77 lines
2.0 KiB
Typst
#import "globals.typ": *
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= Halo growth
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== Motivation
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#layouts.contained(
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[
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Crucial dependence on the *star formation rate*
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- assumed to be directly linked to halo growth rate $dot(M)$:
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$
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dot(M)_star = f_star (M_"h") dot dot(M_"h")
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$
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- growth according to the exponential model:
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$
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M_"h" (z) = M_"h" (z_0) dot exp[-alpha (z - z_0)]
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$
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with $alpha = dot(M_"h")/M_"h"$ the _specific growth rate_
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#pause
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],
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[
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$->$ inaccurate when applied to all halos
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$->$ #text(weight: "bold")[inconsistent] with the N-body output
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#pause
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$->$ how to implement #text(weight: "bold")[consistent] growth?
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]
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)
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== Effect on the flux profiles
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#let notebook = json("../workdir/11_visualization/alpha_dependence_of_profiles.ipynb")
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#image_cell(notebook, cell_id: "profile_plot_alpha_dependence")
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$M_"h" = 6.08 dot 10^11 M_dot.circle$ (fixed)
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$==>$ correction up to $times 5$
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// COMMENTS
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// That will be directly affect the global signal as well
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// shifting
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//
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// Yu-Siu already investigated the more nuanced effect of stochasticity but the approach we propose should supersede that
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#pagebreak()
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== Inferring growth from #smallcaps[Thesan] data
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- already includes precomputed merger trees @Springel2005
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// ideal for rapid iterations
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- follow main progenitor branch back in time
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- fit the exponential model to main progrenitor branch
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// in a parallelized fashion => want to stay fast
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// fix the original mass for max. consistency
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// fix the allowed dynamic range
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- use *individual growth* to select profile
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// this sort of "breaks the degeneracy" between halos of the same mass but different growth histories
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- *self-consistent*#pause$*$#meanwhile treatment of halo growth leveraging the snapshots
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#pagebreak()
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#let notebook = json("../workdir/11_visualization/show_trees.ipynb")
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#[
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#set image(width: 100%, fit: "contain")
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#image_cell(notebook, cell_id: "merger_tree_and_fitting")
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]
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// COMMENTS:
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// no clear trend between mass and growth rate
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