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master-thesis-presentation/refinements.typ
2025-09-18 00:02:13 +02:00

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Typst

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