Estimating major merger rates and spin parameters ab initio via the clustering of critical events
(2024) In Monthly Notices of the Royal Astronomical Society 531(1). p.1385-1397- Abstract
We build a model to predict from first principles the properties of major mergers. We predict these from the coalescence of peaks and saddle points in the vicinity of a given larger peak, as one increases the smoothing scale in the initial linear density field as a proxy for cosmic time. To refine our results, we also ensure, using a suite of ∼400 power-law Gaussian random fields smoothed at ∼30 different scales, that the relevant peaks and saddles are topologically connected: They should belong to a persistent pair before coalescence. Our model allows us to (a) compute the probability distribution function of the satellite-merger separation in Lagrangian space: They peak at three times the smoothing scale; (b) predict the distribution... (More)
We build a model to predict from first principles the properties of major mergers. We predict these from the coalescence of peaks and saddle points in the vicinity of a given larger peak, as one increases the smoothing scale in the initial linear density field as a proxy for cosmic time. To refine our results, we also ensure, using a suite of ∼400 power-law Gaussian random fields smoothed at ∼30 different scales, that the relevant peaks and saddles are topologically connected: They should belong to a persistent pair before coalescence. Our model allows us to (a) compute the probability distribution function of the satellite-merger separation in Lagrangian space: They peak at three times the smoothing scale; (b) predict the distribution of the number of mergers as a function of peak rarity: haloes typically undergo two major mergers (>1:10) per decade of mass growth; (c) recover that the typical spin brought by mergers: it is of the order of a few tens of per cent.
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- author
- Cadiou, Corentin LU ; Pichon-Pharabod, Eric ; Pichon, Christophe and Pogosyan, Dmitri
- organization
- publishing date
- 2024-06-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- cosmology: Theory, large-scale structure of Universe
- in
- Monthly Notices of the Royal Astronomical Society
- volume
- 531
- issue
- 1
- pages
- 13 pages
- publisher
- Oxford University Press
- external identifiers
-
- scopus:85193909579
- ISSN
- 0035-8711
- DOI
- 10.1093/mnras/stae1220
- language
- English
- LU publication?
- yes
- id
- 0d498c8c-f884-4365-bd1b-f609362e6deb
- date added to LUP
- 2024-05-31 13:58:01
- date last changed
- 2024-05-31 13:59:01
@article{0d498c8c-f884-4365-bd1b-f609362e6deb, abstract = {{<p>We build a model to predict from first principles the properties of major mergers. We predict these from the coalescence of peaks and saddle points in the vicinity of a given larger peak, as one increases the smoothing scale in the initial linear density field as a proxy for cosmic time. To refine our results, we also ensure, using a suite of ∼400 power-law Gaussian random fields smoothed at ∼30 different scales, that the relevant peaks and saddles are topologically connected: They should belong to a persistent pair before coalescence. Our model allows us to (a) compute the probability distribution function of the satellite-merger separation in Lagrangian space: They peak at three times the smoothing scale; (b) predict the distribution of the number of mergers as a function of peak rarity: haloes typically undergo two major mergers (>1:10) per decade of mass growth; (c) recover that the typical spin brought by mergers: it is of the order of a few tens of per cent.</p>}}, author = {{Cadiou, Corentin and Pichon-Pharabod, Eric and Pichon, Christophe and Pogosyan, Dmitri}}, issn = {{0035-8711}}, keywords = {{cosmology: Theory; large-scale structure of Universe}}, language = {{eng}}, month = {{06}}, number = {{1}}, pages = {{1385--1397}}, publisher = {{Oxford University Press}}, series = {{Monthly Notices of the Royal Astronomical Society}}, title = {{Estimating major merger rates and spin parameters ab initio via the clustering of critical events}}, url = {{http://dx.doi.org/10.1093/mnras/stae1220}}, doi = {{10.1093/mnras/stae1220}}, volume = {{531}}, year = {{2024}}, }