Difference between revisions of "Peak Patch and WebSky"

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(→‎The Peak Patch simulations: filling in details on how the sims work)
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=The <i>Peak Patch</i> simulations=
 
=The <i>Peak Patch</i> simulations=
The <i>Peak Patch</i> simulations model the distribution of dark matter (DM) in the universe by mapping out catalogues of DM halos.  
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The <i>Peak Patch</i> simulations model the distribution of dark matter (DM) in the universe by mapping out catalogues of [https://en.wikipedia.org/wiki/Dark_matter_halo DM halos].
  
[ add basic steps that pkp takes ]
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1. <b>Generate <math>\delta_L(\mathbf{x})</math> field:</b> as initial conditions, <i>Peak Patch</i> uses a linear-theory matter overdensity field <math>\delta_L(\mathbf{x})</math>, which is the matter overdensity <math>\delta(\mathbf{x}) = \rho_m(\mathbf{x},t) / \bar{\rho}_m(t) - 1</math> at a time <math>t</math> sufficiently early in the history of the universe that linear theory is a suitable approximation to the dynamics that formed it. <math>\delta_L(\mathbf{x})</math> can either by read from a file or generated from a [https://en.wikipedia.org/wiki/Matter_power_spectrum power spectrum] (which describe the number of structures of different sizes that we observe in the universe).
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2. <b>Smooth and find peaks:</b> the <math>\delta_L(\mathbf{x})</math> field is then smoothed at a series of scales by convolving with top hat functions (<i>e.g.</i> see section 3.2 of [https://www.cita.utoronto.ca/~njcarlson/public/papers/van_de_Weygaert_and_Bond_i.pdf this review article]). This removes any fluctuations smaller than about the top-hat function's cutoff radius <math>R_\mathrm{th}</math> allowing us to isolate just structures in <math>\delta_L(\mathbf{x})</math> larger than <math>R_\mathrm{th}</math>. Peaks in each filtered density field are found, these are candidate sites where dark matter halos may form.
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3. <b>Ellipsoidal collapse:</b> dark matter halos are gravitationally bound collections of dark matter, and therefore [https://en.wikipedia.org/wiki/Virial_theorem#Dark_matter virialised]. To determine if a given peak will collapse to form a virialised halo, we model it as a uniform-density sphere of radius <math>R_\mathrm{th}</math> (the top-hat filter radius at which the peak was identified), and allow it to collapse ellipsoidally (<i>e.g.</i> see section 3.3 of [https://www.cita.utoronto.ca/~njcarlson/public/papers/van_de_Weygaert_and_Bond_i.pdf this review article]) subject to the local strain of the <math>\delta_L(\mathbf{x})</math> field. If the collapse has enough time to reach a density contrast characteristic of virialised structure by time <math>t</math>, it is saved as a candidate halo of radius <math>R_\mathrm{th}</math>, or mass <math>M = \bar{\rho}_{m}(t_0) \frac{4}{3} \pi R_\mathrm{th}^3</math>.
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<i>Peak Patch</i> can either produce <b>single-redshift</b> simulations, where all halos collapse until the same time <math>t</math>, or <b>light cone</b> simulations where each halo is evolved only until the age it appears to be at relative to a specified observer (so <math>t</math> is the light travel time to the halo). The latter is ideal for making sky maps.
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4. <b>Merging and exclusion:</b> because halo candidates are found at multiple filter scales, overlaps are inevitable. So a merging and exclusion algorithm is run with any halo entirely inside another being tossed out and halos overlapping regions partially allotted to each halo to avoid double counting mass.
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5. <b>Displacement to final state halos:</b> up to this point, we have been working in Lagrangian space, essentially determining what mass in the initial conditions fields will form halos. But as matter collapses into halos, these halos will also move relative to one another. Most of the highly nonlinear dynamics occurs within DM halos, so the displacement of the halos themselves can be accomplished with 2nd order Lagrangian perturbation theory.
  
 
<i>Peak Patch</i> has been validated by <math>N</math>-body simulations and has performed favourably compared to other halo-finders.
 
<i>Peak Patch</i> has been validated by <math>N</math>-body simulations and has performed favourably compared to other halo-finders.
  
<i>Peak Patch</i> is additionally a particularly powerful tool for studying beyond-standard-model (BSM) cosmology because the simulation takes as initial conditions either a [https://en.wikipedia.org/wiki/Matter_power_spectrum power spectrum] (describing the amount of structures of different sizes that we observe in the universe) or a linear matter density field <math>\delta_L(\mathbf{x})</math> (the density of matter in the early universe, before nonlinear effects like gravity have had much chance to operate, leaving a field that is propagated from quantum fluctuations by linear theory only), and because it has built-in support for varying <math>\Lambda</math>CDM model parameters and primordial non-Gaussianities of a number of forms informed by a range of early-universe physics phenomena. Because it employs only approximate dynamics, complete knowledge of the BSM equations of motion is not required, allowing us to probe wider parameter space.
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<i>Peak Patch</i> is additionally a particularly powerful tool for studying beyond-standard-model (BSM) cosmology because the simulation has built-in support for varying <math>\Lambda</math>CDM model parameters and [https://en.wikipedia.org/wiki/Non-Gaussianity primordial non-Gaussianities] of a number of forms informed by a range of early-universe physics phenomena. Because it employs only approximate dynamics, complete knowledge of the BSM equations of motion is not required, allowing us to probe wider parameter space.
  
[ link to George's paper ]
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For the most up-to date description of <i>Peak Patch</i> see [https://arxiv.org/abs/1810.07727 arXiv:1810.07727].
  
 
=The <i>WebSky</i> simulations=
 
=The <i>WebSky</i> simulations=

Revision as of 21:52, 29 June 2024

[ Note this page is under construction ]

The Peak Patch and WebSky simulations are a highly efficient tool for modelling the large-scale structure of the universe.

The Peak Patch simulations

The Peak Patch simulations model the distribution of dark matter (DM) in the universe by mapping out catalogues of DM halos.

1. Generate [math]\displaystyle{ \delta_L(\mathbf{x}) }[/math] field: as initial conditions, Peak Patch uses a linear-theory matter overdensity field [math]\displaystyle{ \delta_L(\mathbf{x}) }[/math], which is the matter overdensity [math]\displaystyle{ \delta(\mathbf{x}) = \rho_m(\mathbf{x},t) / \bar{\rho}_m(t) - 1 }[/math] at a time [math]\displaystyle{ t }[/math] sufficiently early in the history of the universe that linear theory is a suitable approximation to the dynamics that formed it. [math]\displaystyle{ \delta_L(\mathbf{x}) }[/math] can either by read from a file or generated from a power spectrum (which describe the number of structures of different sizes that we observe in the universe).

2. Smooth and find peaks: the [math]\displaystyle{ \delta_L(\mathbf{x}) }[/math] field is then smoothed at a series of scales by convolving with top hat functions (e.g. see section 3.2 of this review article). This removes any fluctuations smaller than about the top-hat function's cutoff radius [math]\displaystyle{ R_\mathrm{th} }[/math] allowing us to isolate just structures in [math]\displaystyle{ \delta_L(\mathbf{x}) }[/math] larger than [math]\displaystyle{ R_\mathrm{th} }[/math]. Peaks in each filtered density field are found, these are candidate sites where dark matter halos may form.

3. Ellipsoidal collapse: dark matter halos are gravitationally bound collections of dark matter, and therefore virialised. To determine if a given peak will collapse to form a virialised halo, we model it as a uniform-density sphere of radius [math]\displaystyle{ R_\mathrm{th} }[/math] (the top-hat filter radius at which the peak was identified), and allow it to collapse ellipsoidally (e.g. see section 3.3 of this review article) subject to the local strain of the [math]\displaystyle{ \delta_L(\mathbf{x}) }[/math] field. If the collapse has enough time to reach a density contrast characteristic of virialised structure by time [math]\displaystyle{ t }[/math], it is saved as a candidate halo of radius [math]\displaystyle{ R_\mathrm{th} }[/math], or mass [math]\displaystyle{ M = \bar{\rho}_{m}(t_0) \frac{4}{3} \pi R_\mathrm{th}^3 }[/math].

Peak Patch can either produce single-redshift simulations, where all halos collapse until the same time [math]\displaystyle{ t }[/math], or light cone simulations where each halo is evolved only until the age it appears to be at relative to a specified observer (so [math]\displaystyle{ t }[/math] is the light travel time to the halo). The latter is ideal for making sky maps.

4. Merging and exclusion: because halo candidates are found at multiple filter scales, overlaps are inevitable. So a merging and exclusion algorithm is run with any halo entirely inside another being tossed out and halos overlapping regions partially allotted to each halo to avoid double counting mass.

5. Displacement to final state halos: up to this point, we have been working in Lagrangian space, essentially determining what mass in the initial conditions fields will form halos. But as matter collapses into halos, these halos will also move relative to one another. Most of the highly nonlinear dynamics occurs within DM halos, so the displacement of the halos themselves can be accomplished with 2nd order Lagrangian perturbation theory.

Peak Patch has been validated by [math]\displaystyle{ N }[/math]-body simulations and has performed favourably compared to other halo-finders.

Peak Patch is additionally a particularly powerful tool for studying beyond-standard-model (BSM) cosmology because the simulation has built-in support for varying [math]\displaystyle{ \Lambda }[/math]CDM model parameters and primordial non-Gaussianities of a number of forms informed by a range of early-universe physics phenomena. Because it employs only approximate dynamics, complete knowledge of the BSM equations of motion is not required, allowing us to probe wider parameter space.

For the most up-to date description of Peak Patch see arXiv:1810.07727.

The WebSky simulations

The WebSky simulations produce mock sky maps in a range of observables. Mock sky maps are simulated maps of the sky that are statistically analogous to maps of the sky made by observatories.

[ discuss model ]

[ discuss different response functions ]

[ link to paper ]

Notes

1. WebSky has two pronunciations wɛbskaɪ and wɛbskiː which are understood to exist in superposition [math]\displaystyle{ | \text{WEB} \rangle \otimes \left( \frac{ | \text{sky} \rangle + | \text{skee} \rangle }{ \sqrt{2} } \right) }[/math].