# cosmICweb: Cosmological Initial Conditions for Zoom-in Simulations in the
Cloud

Michael Buehlmann Computational Science Division, Argonne National Laboratory,
Lemont, IL 60439, USA  Lukas Winkler Department of Astrophysics, University of
Vienna, Türkenschanzstraße 17 (Sternwarte), 1180 Wien, Austria  Oliver Hahn
Department of Astrophysics, University of Vienna, Türkenschanzstraße 17
(Sternwarte), 1180 Wien, Austria  Department of Mathematics, University of
Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria  John C. Helly
Institute for Computational Cosmology, Department of Physics, University of
Durham, South Road, Durham, DH1 3LE, UK  Adrian Jenkins Institute for
Computational Cosmology, Department of Physics, University of Durham, South
Road, Durham, DH1 3LE, UK

###### Abstract

We present the online service cosmICweb (COSMological Initial Conditions on
the WEB)— the first database and web interface to store, analyze, and
disseminate initial conditions (ICs) for zoom simulations of objects forming
in cosmological simulations: from galaxy clusters to galaxies and more.
Specifically, we store compressed information about the Lagrangian proto-halo
patches for all objects in a typical simulation merger tree along with
properties of the halo/galaxy across cosmic time. This enables a convenient
web-based selection of the desired zoom region for an object fitting user-
specified selection criteria. The information about the region can then be
used with the music code to generate the zoom ICs for the simulation. In
addition to some other simulations, we currently support all objects in the
Eagle simulation database, so that for example the Auriga simulations are
easily reproduced, which we demonstrate explicitly. The framework is
extensible to include other simulations through an API that can be added to an
existing database structure and with which cosmICweb can then be interfaced.
We make the web portal and database publicly available to the community.

##  1 Introduction

In cosmology and extragalactic astrophysics, numerical simulations are a
fundamental tool to study the growth and evolution of structures in the
universe across an enormous range of scales (e.g., Vogelsberger et al., 2020;
Angulo & Hahn, 2022, for recent reviews). On the largest scales up to several
gigaparsec, they probe the cosmological large scale structure. On the smallest
scales ranging from several parsec to kiloparsec, they can resolve the
formation and structure of individual galaxies.

The computational cost of computing and storing the full dynamic range in the
entire simulated volume sets a limit on the upper and lower scales that can be
simulated in a single run. A common approach to circumvent this limitation is
the _multi-mass_ or _zoom_ technique, in which the immediate region around the
object of interest is simulated with a highly increased resolution compared to
its surrounding large scale environment (Katz et al., 1994; Navarro & White,
1994). This method allows capturing both the influences of the cosmic
environment from fluctuations on scales equal and larger than the object as
well as small scale fluctuations affecting the structure of the object
directly. Zoom-in initial conditions can be generated by degrading a high-
resolution initial condition outside the zoom region (e.g., Garaldi et al.,
2016) or by refining the zoom region (e.g., Bertschinger, 2001; Jenkins, 2010;
Hahn & Abel, 2011). Since the former “degrading” approach is computationally
limited by memory for deep zoom levels, zoom initial conditions are usually
generated using one of the refinement techniques.

When computing such zoom initial conditions, a fundamental ingredient is that
the noise field (also known as the _random phases_) is spatially stable
without having to generate it at full resolution. This can be achieved in
different ways, with the Panphasia approach (Jenkins, 2013) of an expansion in
terms of hierarchically defined basis functions being particularly powerful.
Panphasia has been adopted for various large recent simulations such as Eagle
(Schaye et al., 2015) and Flamingo (Schaye et al., 2023). Various objects such
as e.g., dwarf and Milk-Way-type galaxies in the Auriga simulations (Grand et
al., 2024) have been re-simulated at higher resolution using zoom-ins on for
example the Eagle volume. However, resimulating objects from these simulations
so far required proprietary software and access to the particle data at
initial and final time. Often, the parent simulations of zoom simulations are
not even published in other studies.

Figure 1: Illustration of the steps involved in the creation of a zoom
simulation. A detailed description of the process is provided in the main
text. The cosmICweb database stores halo catalogs, merger trees and the
associated Lagrangian proto-halo patches from existing simulations, all
accessible over an intuitive web interface.

Once the random field is defined, setting up zoom-in simulations requires
knowledge of the initial region which forms the object of interest as the
universe evolves, the _proto-halo patch_. This Lagrangian volume has to be
determined from a (lower resolution) full-box simulation, the “parent
simulation”, in which one has to find the object of interest and trace its
constituent particles back to the initial conditions. Extra care needs to be
taken to not contaminate the zoom region with low-resolution particles, as
these can have a significant effect on measured gas densities and substructure
formation (see Oñorbe et al., 2013). The typical setup process for zoom-
simulations is illustrated in Figure 1 and typically consists of the following
steps:

  * 1-3

Setting up, running, and analyzing the parent simulation. The parent
simulation is characterized by the white noise field used to generate the
initial conditions (i.e. the random number generator and its seed) and the
cosmological parameters under which the system is evolved. Various $N$-body
codes exist to run the simulation. In order to identify halos or regions of
interest in later steps, a halo or a more general structure finder has to be
run in-situ or on the simulation output. In order to obtain information on the
assembly history of halos, it is desirable to generate frequent outputs and
build merger trees that connect halos across time.

  * 4

Identify target(s): From the merger trees, halo catalogs, or directly from the
particle snapshots, select the halos (or a more general region) that match the
research goal at hand.

  * 5

Determine the Lagrangian volume: Back-trace the particles within the target
region to their Lagrangian position. In the case of halos, one usually
identifies the particles within a “traceback”-radius who form the proto-halo
patch in Lagrangian coordinates.

  * 6

Create high-resolution initial conditions refined on this patch, using a
multi-scale initial condition generator such as music (Hahn & Abel, 2011) or
ic_gen (Jenkins, 2010, 2013).

The steps 1 to 5 are preparatory steps that usually have to be repeated for
new zoom simulation, unless they are based on the same parent volume. Public
halo and galaxy catalogs of large simulations exist (e.g., McAlpine et al.,
2016; Nelson et al., 2019; Heitmann et al., 2019), but they are usually not
associated with proto-halo patches. Furthermore, the absence of public access
to initial conditions in research articles complicates the direct comparison
between different simulation codes, baryonic physics prescriptions, and the
verification of scientific results.

With cosmICweb 111short for “COSMological Initial Conditions on the WEB”, we
address three issues with the current work flow for setting up and running
zoom simulations:

  * •

_Finding interesting objects to simulate._ We provide access to databases of
pre-run simulations in which the user can search for halos (and galaxies) at
various redshifts matching their research project requirements.

  * •

_Generating zoom initial conditions._ For every halo, we provide multiple
proto-halo patches that include different amounts of the halo surrounding. The
application combines these patches with the initial conditions of the
simulation to a music (Hahn & Abel, 2011) configuration file that can be used
to generate the initial conditions for various simulation codes.

  * •

_Referencing initial conditions._ Users can create references to halos and
collections of halos that they used in their works, allowing the community to
easily reproduce results and to compare different simulation codes for
verification. For each such reference, a tag is generated that can be included
in research articles, allowing the community to access the zoom initial
conditions using the cosmICweb application or directly through a plugin
distributed with music.

This paper accompanies the release of cosmICweb 222cosmICweb URL:
https://cosmicweb.eu. and documents the simulations and pre-computed initial
conditions that are available at release and the architecture and
extendability of cosmICweb. The structure is as follows: In Section 2, we
introduce the simulations that are included in the cosmICweb application at
release and give an overview of the available halo, galaxy and Lagrangian
ellipsoid selectors which can be used to search the simulation databases. In
Section 3, we discuss the computation of proto-halo patches from simulation
data, in particular our choice of minimum bounding ellipsoids to describe
Lagrangian proto-halos. In Section 4, we present the individual components of
cosmICweb and how its interface can be used to find, download, and publish
initial conditions for zoom simulations. In Section 5, we analyze the proto-
halo patches provided on cosmICweb by measuring their size and efficiency
distributions depending on halo and environmental properties and the choice of
traceback-radius. We also show two proof-of-concept zoom simulations to
highlight the capabilities of cosmICweb. We conclude in Section 6. More
details on the requirements of connecting additional simulations to cosmICweb
can be found in Appendix A.

##  2 Cosmological simulations and merger trees

The cosmICweb platform has been designed in a modular way to allow for the
integration of data from diverse projects hosted at different locations (also
see Section 4.1). Therefore, more simulations can easily be added in the
future by us and the community. Here, we will briefly summarize the
simulations that are available at the date of release.

Table 1: List of simulations available at the time of release of cosmICweb. The simulation parameters include the box length of the simulation volume, the cosmological parameters that were used, dark matter mass resolution, number of snapshots, the structure finder that was used, and the minimum number of particles within the traceback-radius that we require to compute the minimum bounding ellipsoids, $N^{e}_{\min}$.  |  | box length | cosmology | DM resolution | galaxies | $N^{e}_{\min}$ | # snapshots | halo finder
---|---|---|---|---|---|---|---|---
|  | [$h^{-1}\mathrm{Mpc}$ ] |  | [$h^{-1}\mathrm{M}_{\odot}$ ] |  |  | [redshift range] |
primary | agora | 60 | Planck 2015 | $1.21\times 10^{8}$ |  | 1000 | 101 [12.0 – 0] | rockstar
150MPC | 150 | Planck 2015 | $2.70\times 10^{8}$ |  | 100 | 101 [12.0 – 0] | rockstar
300MPC | 300 | Planck 2015 | $2.14\times 10^{9}$ |  | 100 | 101 [12.0 – 0] | rockstar
Rhapsody | 1000 | WMAP 7/9 | $6.46\times 10^{10}$ |  | 1000 | 101 [12.0 – 0] | rockstar
Rhapsody_PlanckCosmo | 1000 | Planck 2015 | $7.99\times 10^{10}$ |  | 1000 | 101 [12.0 – 0] | rockstar
EAGLE | Ref-L0100N1504 | $100h$ | Planck 2014 | $9.7\times 10^{6}h$ | ✓ | 1000 | 29 [20.3 – 0] |  fof/subfind
Ref-L0025N0376 | $25h$ | Planck 2014 | $9.7\times 10^{6}h$ | ✓ | 1000 | 29 [20.3 – 0] |  fof/subfind
Ref-L0025N0752 | $25h$ | Planck 2014 | $1.21\times 10^{6}h$ | ✓ | 1000 | 29 [20.3 – 0] |  fof/subfind
DMONLY-L0100N1504 | $100h$ | Planck 2014 | $1.15\times 10^{7}h$ |  | 1000 | 29 [20.3 – 0] |  fof/subfind
DMONLY-L0025N0376 | $25h$ | Planck 2014 | $1.15\times 10^{7}h$ |  | 1000 | 29 [20.3 – 0] |  fof/subfind
DMONLY-L0025N0752 | $25h$ | Planck 2014 | $1.44\times 10^{6}h$ |  | 1000 | 29 [20.3 – 0] |  fof/subfind

###  2.1 Gravity-only simulations

Currently, we are locally hosting data from five gravity-only simulations. The
parameters that were used for these simulations are summarized in Table 1. The
simulations span cosmological volumes from $60^{3}$ $h^{-3}$Mpc3 to $1$
$h^{-3}$Gpc3 and use cosmological parameters conforming with the Planck 2015
(Planck Collaboration, 2015) and WMAP 7/9 (Komatsu et al., 2011; Hinshaw et
al., 2013) results. We include two simulations which have been previously used
for zoom simulations: the cosmological volumes of the AGORA comparison project
(Kim et al., 2013) and of the RHAPSODY cluster re-simulation project (Wu et
al., 2013b, a; Hahn et al., 2017) which is originally based on the Carmen
simulation of the LasDamas project (McBride et al., 2009) – we labeled the
simulations “agora ” and “Rhapsody ” for the sake of convenience. Rhapsody
uses older WMAP 7/9 cosmological parameters. However, we have updated the
parameters in the Rhapsody_PlanckCosmo simulation to allow comparisons between
the halo catalogs and zoom simulation results.

Initial conditions for the full simulations were generated using music (Hahn &
Abel, 2011) at redshift $z=49$ (Rhapsody & Rhapsody_PlanckCosmo) and $z=99$
(remaining simulations). Using a modified version of the gadget-3 code
(Springel, 2005), we evolved the simulations to redshift $z=0$ and stored 100
snapshots between $z=12$ and 0, logarithmically distributed in scale factor
units. Each snapshot was processed with the rockstar structure finder
(Behroozi et al., 2013a), which identifies halos and subhalos using a six-
dimensional phase-space friend-of-friend algorithm and measures their
properties, including spherical overdensity masses, radii, and concentrations,
spin parameters, and relaxedness quantities. Using consistent-trees (Behroozi
et al., 2013b), we group halos across snapshots to merger trees containing
information about spatial hierarchy (halo substructure) as well as temporal
hierarchy (descendant and progenitor halos and merger events). In addition, we
compute environmental parameters such as the normalized distance to the
closest more massive neighbor $D_{1,1}$ (cf. Table 2) directly from the halo
catalog.

For every host halo in each snapshot, we compute the Lagrangian minimum
bounding ellipsoid at $1,2,4$, and $10$ $R_{\mathrm{vir}}$, if the number of
particles within that radius exceeds the threshold listed in Table 1. We will
discuss the definition, construction, and statistical distribution of these
proto-halo patches in more detail in Section 3 and Section 5.1.

The halo catalogs including the merger-tree information and the Lagrangian
proto-halos are stored in a relational database. To reduce latency and speed-
up queries, the data has been partitioned and indexed on the most commonly
used columns. More details on the storage of and interface to the data can be
found in Section 4.3.

###  2.2 The Eagle database

Another set of halo catalogs and merger trees originates from simulations of
the _Evolution and Assembly of GaLaxies and their Environments_ simulation
suite (Eagle; Schaye et al., 2015; Crain et al., 2015; McAlpine et al., 2016)
and is hosted by the Virgo consortium333http://www.virgo.dur.ac.uk/. In
particular, we provide access to the Ref-L0025N0752, Ref-L0025N0376, and
Ref-L0100N1504 simulation that were run with full gravity, hydrodynamics and
subgrid modelling, and their L0025N0752, L0025N0376, and L0100N1504 gravity-
only counterparts. With the addition of simulations including full baryonic
physics, cosmICweb users can also use galaxy properties to constrain their
search for zoom simulation targets.

The phases for the initial conditions for the Eagle simulations were extracted
from regions within the large Panphasia multi-scale white noise field
(Jenkins, 2013). The phases for the Eagle volumes are published in Schaye et
al. (2015). The updated version of music 444The updated version 2 of music is
available from
https://github.com/cosmo-sims/MUSIC includes the Panphasia white noise field,
allowing us to create zoom simulations in the Eagle volumes from a unified
interface.

The Eagle simulations were run with a modified version of the gadget-3 code
with a full gravity and hydrodynamics treatment, including a large number of
subgrid modules accounting for physical processes below the resolution scale,
such as radiative cooling and heating, star formation and evolution, metal
enrichment, and feedback from supernovae and supermassive black holes (Crain
et al., 2015). Each simulation contains 29 snapshots distributed between
$z=20$ and $z=0$. In the snapshots, halos are identified using a FoF algorithm
with linking length $b=0.2$ and a spherical over-density algorithm. Baryonic
particles are then assigned to the FoF groups and all particle species within
the group are further processed with the subfind algorithm (Springel et al.,
2001; Dolag et al., 2009) to separate self-bound structures and identify
galaxies. The subhalo catalogs are linked across time by determining the
descendant halos using D-Trees (Jiang et al., 2014; Qu et al., 2017), and the
main progenitors defined by the largest “branch mass” (Qu et al., 2017).

The host halos for the Auriga Project galaxy formation simulations (Grand et
al., 2017, 2024) were selected from the Eagle 100 Mpc side-length box. Table
A1 in Grand et al. (2017) matches the host halos of the Auriga sample with
their corresponding halo IDs in the Eagle database. In Section 5.3, we
recreate the host halo of the Auriga 6 galaxy and compare the redshift zero
dark matter halo properties against an original Auriga project dark matter
simulation of that same halo.

##  3 Proto-halo patches

Figure 2: Slices through six halos at redshift $z=0$ (right panels) and their
proto-halo patches (left panels). The halos are selected from the 300MPC
simulation, centered around three halo mass values $M_{200c}\sim
10^{12},10^{13}$, and $10^{14}$ $h^{-1}\mathrm{M}_{\odot}$ from isolated
environments (top) and more populated regions (bottom). The circles in
Eulerian space correspond to traceback-radii of 1 (magenta), 2 (green), 4
(blue), and 10 $R_{\mathrm{vir}}$ (yellow), respectively. Particles within
$R_{\mathrm{tb}}$ are traced back to Lagrangian space and colored
correspondingly. The ellipses represent slices through the minimum bounding
ellipsoids and the background shading illustrate the density field (darker
colors correspond to higher density).

In this section, we present an overview of the zoom regions available in
cosmICweb. Specifically, we define Lagrangian proto-halos and explain the
rationale behind our choice of minimum bounding ellipsoids to describe these
volumes.

The proto-halo patch is defined by the Lagrangian volume that collapses to a
halo as the universe evolves. In an $N$-body simulation, it is traced by the
particles that are within the traceback-radius $R_{\mathrm{tb}}$ of the halo,
which is often chosen as a multiple of the halo radius defined by a spherical
overdensity criterion. Choosing a larger traceback-radius increases the
computational cost of the simulation (although not proportionally to the
volume, as the density decreases with the distance to the halo), but reduces
the probability of contamination by low-resolution particles during the
evolution of the halo. A larger zoom region also allows to resolve the
immediate environment of the halo in high resolution, which is desirable in
many simulations. However, a too large traceback radius potentially includes
surrounding halos, in particular if the targeted halo is located in a
populated environment, resulting in a computationally more expensive
simulation.

Figure 2 illustrates the relation between proto-halos in Lagrangian space and
the resulting halo at redshift $z=0$. We choose six halos from the 300MPC
simulation, two halos each around $M_{200c}\sim 10^{12},10^{13}$, and
$10^{14}$ $h^{-1}\mathrm{M}_{\odot}$, with one from an isolated and one from a
more populated environment. The traceback radii at 1, 2, 4, and 10
$R_{\mathrm{vir}}$ are shown as circles in Eulerian space. All particles
within the circles are traced back to the initial conditions and colored with
the color corresponding to $R_{\mathrm{tb}}$. We note that while in isolated
environments, the proto-halo patches are compact and do not grow significantly
with increasing traceback radius (with the exception of
$R_{\mathrm{tb}}=10R_{\mathrm{vir}}$), they are less spherical, more scattered
and grow significantly at larger radii in populated regions.

Once the particles within the traceback radius are identified, different
techniques exist to describe the enclosing Lagrangian volume, such as a
rectangular bounding box, minimum bounding ellipsoids, and convex hulls. A
comparison of some commonly used methods can be found in Oñorbe et al. (2013).
Note that the refinement regions are generally chosen to be convex as concave
patches and holes within the volume could contaminate the zoom region with low
resolution particles and impact the accuracy of the simulation. To measure the
quality and computational cost of a proto-halo patch, we can define the
efficiency as the ratio of the mass of the particles enclosed within
$R_{\mathrm{tb}}$ to the mass enclosed by the Lagrangian volume description,
i.e.

$\mathcal{E}=\frac{M_{\mathrm{particles}}}{M_{\mathrm{ellipsoids}}}.$ (1)

In general, the efficiency increases with the complexity of the boundary
description, e.g the rectangular bounding box requiring 6 coordinates is less
efficient than the minimum bounding ellipsoid (9 parameters), which in turn is
less efficient than the convex hull described by all the particles on the
surface (Oñorbe et al., 2013). As we will show in Section 5.1, the variance of
the efficiency is mass dependent and increases for low-mass halos due to
environmental effects, as most of the low-mass halos evolve in clustered
environments with strong tidal fields. In contrast, more massive halos form at
the nodes of the cosmic web where mass accretion occurs more isotropically,
leading to a more well-behaved, spherical proto-halo.

The efficiency parameter is a measure of how well the abstracted Lagrangian
volume describes the proto-halo patch and determines the fraction of
computational resources that is focused on the volume of interest within the
traceback-radius. It is therefore desirable to create proto-halo patches with
high $\mathcal{E}$ for zoom simulations. The efficiency parameter naturally
depends on the environment and formation history of the halo, which is why one
has to be careful to not create a biased sample when selecting halos by the
efficiency parameters of their Lagrangian minimum bounding ellipsoid. We will
discuss this bias more quantitatively in Section 5.2.

Generally, the ellipsoidal proto-halo patches provided in cosmICweb are
sufficient for many applications. However, for extremely high resolution
simulations, users may want to further refine the boundary of the high
resolution region in order to optimize computational resources. This can be
easily achieved by first running a medium-resolution zoom simulation using the
ellipsoidal patches in cosmICweb and then calculating a more efficient
boundary of the Lagrangian region using for example a convex hull algorithm.

###  3.1 Minimal bounding ellipsoids in cosmICweb

For cosmICweb, we provide proto-halo regions in form of minimal bounding
ellipsoids. This choice is motivated by the impracticality of storing convex
hulls for all halos across all snapshots and multiple traceback-radii. Minimal
bounding ellipsoids offer a robust alternative while still capturing the
overall shape of typical proto-halo patches at manageable data storage
requirements.

An ellipsoid is described by its center $\bm{q}_{c}$ and its $3\times 3$ shape
matrix $\mathrm{A}$. The symmetric, positive definite matrix $\mathrm{A}$ can
be normalized such that coordinates $\bm{q}$ that are within the ellipsoid
satisfy

$(\bm{q}-\bm{q}_{c})^{\top}\mathrm{A}\,(\bm{q}-\bm{q}_{c})<1.$ (2)

We outline the computed minimum bounding ellipsoids for the selected halos in
Figure 2. Note that the illustration is only a 2-dimensional slice through the
center of the ellipsoid, hence the ellipsoids appear to be a bad fit in some
cases since the 3-dimensional structure is not visible. Nevertheless, we
clearly notice that halos in isolated environments tend to have more
“efficient” ellipsoids, with a higher fraction of particles within the proto-
halo collapsing into the traceback-radius. We discuss the ellipsoid efficiency
in a more statistical analysis in Section 5.1.

For the simulations provided by the cosmICweb team (labeled “primary” in Table
1), we provide minimal bounding ellipsoids for free halos (i.e., no subhalos)
at every snapshot and for four different traceback-radii
$R_{\mathrm{tb}}=1,2,4$, and 10 $R_{\mathrm{vir}}$ if the number of particles
within $R_{\mathrm{tb}}$ is larger than the threshold $N^{e}_{\min}$. The
EAGLE simulations provide minimum bounding ellipsoids at
$R_{\mathrm{tb}}=1,2,4$, and 8 $R_{200c}$ for all FoF groups at each snapshot.
The ellipsoids are computed using Khachiyan’s algorithm (Khachiyan, 1996;
Kumar & Yildirim, 2005), which iteratively optimizes the volume of the
ellipsoid under the constraint that all particles are contained. We account
for periodic boundary conditions by moving all particles to the same local
coordinate system centered at the proto-halo center of mass. This requires
that no axis of the proto-halo patch is longer than half the box size, which
generally should not occur and for which a zoom-simulation would provide only
marginal benefits.

##  4 Database architecture and web application

Figure 3: Sketch of the architecture of cosmICweb. The server-side is divided
into a main server, which hosts the web-server, manages user accounts, and
stores user data and information, and several API servers that host the
simulation data (halo catalogs and minimum bounding ellipsoids). Initial
condition configurations for music can be accessed via the website and the
cosmicweb-music command-line tool.

In this section, we give an overview of the main components of the web
application and discuss the implementation of the database that hosts the
merger trees and proto-halo patches provided by the cosmICweb team. As the
implementation details may change in the future, this section reflects the
state of the project at the date of release.

###  4.1 Architecture

Due to the diverse and potentially very large datasets, the cosmICweb project
is designed to be decentralized and modular. This allows us to host the merger
trees and proto-halo patches on different servers than the application server,
the user, and the publication data. We can therefore integrate existing
databases to cosmICweb without the need of duplicating data.

The cosmICweb platform can be split into four main parts, visualized in Figure
3:

  * •

The data servers, which we will refer to as _API servers_ , providing access
to the halo/galaxy catalogs and minimum bounding ellipsoids,

  * •

The application server hosting the web application and storing user data,
simulation meta-data, and access information to the data servers,

  * •

The web application running in the user’s browser, and

  * •

A command-line interface for the music initial condition generator.

The main application server, hosted at https://cosmicweb.eu, manages user
accounts, stores collections and publications with references to the included
halos, and provides connection information to the API servers for each
supported simulation. The server hosts the cosmICweb website which features a
JavaScript web application. This web-app loads additional data from the
application and API servers, updating the webpage accordingly. Detailed
information about the web application will be presented below.

Simulation metadata, including URLs, supported feature lists, and
authentication tokens for connecting to the data servers, is dynamically
loaded. This allows the web application to directly submit queries to the API
servers and to download halo details and Lagrangian ellipsoids.

The API servers provide an interface between the actual datasets and the
cosmICweb application. This abstraction is essential to support heterogeneous
databases through a common protocol.

Finally, the cosmicweb-music command-line interface (CLI) is a convenient tool
that allows users to download initial condition configuration files directly
via the command line. By using the CLI, users can avoid the cumbersome process
of downloading files locally and then re-uploading them to a headless compute
cluster can be avoided.

For the interaction between these four components to function seamlessly,
requests to and responses from the API servers must adhere to a specified
protocol, which we further discuss in Appendix A. More detailed and up-to-date
documentation is also available directly on the cosmICweb
website555https://cosmicweb.eu/documentation/api. How these requirements are
best implemented on the API server side needs to be evaluated on a case-by-
case basis, considering factors such as the available data, its storage, and
the server infrastructure. We discuss the implementation of the API server
used for the “primary” simulations in more detail at the end of this section.

###  4.2 Web Interface

The cosmICweb interface includes tools for searching halo and galaxy catalogs,
analyzing and visualizing individual halos, configuring and downloading music
initial condition files, and creating, managing, and publishing collections of
halos. Here, we will briefly present each component and explain how they
integrate into the zoom-simulation workflow. Illustrations of the tools are
shown in Figure 4 and Figure 5, and we provide some screenshots of the
visualization modules in Figure 12.

Figure 4: Illustration of the _Halo Finder_ and the _Halo Explorer_ window.
Arrows indicate interactions with the API Servers (blue) and the main
application server (green). Some screenshots of the _Halo Explorer_
visualizations are shown in Figure 12. Figure 5: Illustration of the _Download
Interface_ and the _Collection Editor_ window. Arrows indicate interactions
with the API Servers (blue), the main application server (green), and with the
client application itself (black).

The Halo Finder is the main interface for searching halo and galaxy catalogs
to identify target objects. Users can select one of the available simulations
and specify a snapshot or a range of redshifts. Limits for halo, galaxy, and
ellipsoid properties can be set using either sliders or text input, with
available filters depending on the selected simulation. Table 2 provides an
overview of the currently configured features.

Table 2: List of currently available selectors, grouped by halo properties,
galaxy properties, and further parameters including properties of available
proto-halo patches. [L] and [E] mark the features that are currently supported
in the locally hosted “primary” simulations and the Eagle simulations.

| Parameter | Units |  Description | L | E
---|---|---|---|---|---
halo |  $M_{200c}$ / $M_{500c}$ / $M_{2500c}$ | $h^{-1}\mathrm{M}_{\odot}$ |  Mass within $\rho>=\Delta\rho_{c}$ contrast | ✓ | ✓
$M_{200b}$ / $M_{500b}$ / $M_{2500b}$ | $h^{-1}\mathrm{M}_{\odot}$ |  Mass within $\rho>=\Delta\bar{\rho}$ contrast | ✓${}^{*}$${}^{*}$footnotemark:  | ✓${}^{\dagger}$${}^{\dagger}$footnotemark:
$M_{\mathrm{vir}}$ | $h^{-1}\mathrm{M}_{\odot}$ |  Mass within the virial boundary using the definition of $\rho_{\mathrm{vir}}$ from Bryan & Norman (1998) | ✓ |
$R_{\Delta c}$ / $R_{\Delta b}$ / $R_{\mathrm{vir}}$ | $h^{-1}\mathrm{kpc}$ |  Radius of the halo according to the mass definition | ✓${}^{*}$${}^{*}$footnotemark:  | ✓${}^{\dagger}$${}^{\dagger}$footnotemark:
$c_{\Delta c}$ / $c_{\Delta b}$ / $c_{\mathrm{vir}}$ | — |  concentration parameter $c_{x}=R_{x}/R_{s}$, where $R_{s}$ is the scale radius of the NFW profile | ✓${}^{*}$${}^{*}$footnotemark:  |
$\lambda_{\mathrm{Peebles}}$ | — |  Spin parameter (Peebles definition, Peebles (1971)) | ✓ |
$\lambda_{\mathrm{Bullock}}$ | — |  Spin parameter (Bullock definition, Bullock et al. (2001)) | ✓ |
$f_{\mathrm{sub}}$ | — |  Substructure parameter $\sum_{i}M_{\mathrm{sub},i}/M_{\mathrm{host}}$ | ✓ |
virial ratio | — |  Ratio between kinetic and potential energy $2T/U$ | ✓ |
$x_{\mathrm{offset}}$ | — |  Normalized offset between center of mass and the most bound particle
$x_{\mathrm{offset}}=||\bm{x}_{\mathrm{com}}-\bm{x}_{\mathrm{core}}||/R_{h}$ | ✓ |
$D_{1,1}$ | — |  Normalized distance to the closest more massive neighbor
$D_{1,1}=||\bm{x}_{h}-\bm{x}_{n}||/R_{h}$ ($n$: neighbor) | ✓ | ✓
last major merger | — |  redshift of last major merger | ✓ |
galaxy | $M_{\mathrm{dm}}$ | $h^{-1}\mathrm{M}_{\odot}$ |  dark matter mass in the central galaxy / subhalo |  | ✓
$M_{\mathrm{star}}$ | $h^{-1}\mathrm{M}_{\odot}$ |  stellar mass in the central galaxy / subhalo |  | ✓
$M_{\mathrm{gas}}$ | $h^{-1}\mathrm{M}_{\odot}$ |  gas mass in the central galaxy / subhalo |  | ✓
$M_{\mathrm{BH}}$ | $h^{-1}\mathrm{M}_{\odot}$ |  black hole mass in the central galaxy / subhalo |  | ✓
others | $z$ | — |  redshift (range) | ✓ |
$n_{\mathrm{part}}$ | — |  Number of particles used to construct the minimum bounding ellipsoid | ✓ |
$\epsilon$ | — |  Efficiency of the Lagrangian minimum bounding ellipsoid $\epsilon=n_{\mathrm{part}}/V_{\mathrm{ellipsoid}}$ | ✓ |
include substructure | — |  boolean option to include substructure in the search | ✓ |

**footnotetext: the primary simulations currently do not support $M_{500b}$,
$M_{2500b}$ and the corresponding radii and concentration parameter.

††footnotetext: the Eagle simulations currently support $M_{\mathrm{200b}}$,
$M_{\mathrm{200c}}$, $M_{\mathrm{500c}}$, $M_{\mathrm{2500c}}$, and
$R_{\mathrm{200c}}$.

Once a query is submitted, the API server returns a list of matching halos.
The properties of these halos can be displayed in a table and visualized on
two-dimensional scatter plot. From the results panel, users can open halos in
the Halo Explorer for a more detailed overview, group and store them as
collections, and directly download as initial condition files for
resimulation.

The Halo Explorer lists and visualizes the properties of a single halo and
allows users to navigate the substructure and merger tree. The available
information varies depends on the simulation. We have implemented several
visualizations, including a graphical schematic of the substructure, a merger
tree with a customizable merger-ratio threshold, a graph showing the evolution
of any halo property along the main-progenitor branch, a three-dimensional
visualization of the halo surrounding using WebGL, a tracking tool for the
halo position which can be used to configure movie tools in various simulation
codes, and a graphical visualization of the Lagrangian minimum bounding
ellipsoids at the available traceback-radii. Some screenshots are provided in
Figure 12. From the _Halo Explorer_ , users can directly download the music
configuration file for the selected halo and add the halo to new or existing
collections for a later analysis or publication.

When querying halos in the _Halo Finder_ , numerous halos may meet the search
criteria and be suitable for re-simulation. Narrowing down the selection
requires a more thorough investigation of the candidates using the _Halo
Explorer_ , or possibly running medium-resolution zoom simulations on a few
targets. It is convenient to temporary store the candidates and share them
with colleagues. Collections are designed to fulfill this need by providing an
intuitive interface for grouping halos, with the capability to name and add a
description to both the group and individual halos.

Collections are viewed and managed in the Collection Editor. This tool allows
users to name individual halos and add short descriptions to highlight why the
halos were chosen and what their characteristics are. The collection interface
includes visualizations to compare the properties of the halo sample and their
evolution history along the main progenitor branches.

If the collection is being used in a published article, a permanent public URL
to the collection can be created and included in the article666If the article
references a single halo only, a publication link to this object can also be
created directly from the _Halo Explorer_ without having to create a
collection.. The collection will then appear under the list of publications on
cosmICweb, along with the description and a link to the research article.

Finally, the Download interface allows users to obtain the music configuration
files for one or multiple halos of the same simulation. The overlay includes
options for the traceback radius of the minimum bounding ellipsoid and various
music parameters such as the starting redshift, the coarsest and finest
particle resolution of the simulation, and the type of initial conditions file
that will be generated. These options are added to the configuration file,
facilitating the batch processing of multiple zoom simulations. Depending on
the selected output format, additional options specific to that code are shown
in the interface.

After specifying the options, the interface offers three different methods for
retrieving the initial conditions configuration files: clicking on an
individual entry to open a single configuration directly in the browser,
downloading an archive containing music files for all halos, or storing the
configuration online to later download the configurations using the cosmicweb-
music tool. This allows users to download the initial conditions directly on
the server where the full initial conditions will be generated.

###  4.3 API implementation for the cosmICweb datasets

The “primary” simulations provided by the cosmICweb team support all optional
features and visualizations, except for images (see Table 5). The server thus
needs to be able to efficiently access halo catalogs with merger-tree
information and associated Lagrangian ellipsoids. To achieve this, we store
the data in a SQL database, with halo-relationships, such as descendant halos,
main progenitor halos, host halos, and main subhalos, expressed as foreign
keys. Merger- and substructure trees can then be efficiently retrieved using
recursive queries. To minimize storage requirements, properties such as the
fraction of mass in substructure, half mass time, and major mergers with
custom mass ratios are computed on the fly.

Lagrangian ellipsoids for the cosmICweb simulations have been obtained from
particles within 1, 2, 4, and 10 $R_{\mathrm{vir}}$ of the halos. We store the
centers and shapes of these minimal bounding ellipsoids, along with the number
of particles used to compute the ellipsoid and the resulting efficiency, in a
separate table. This additional information helps identify and filter
unconverged and inefficient ellipsoids.

To parse incoming API requests to various endpoints, dispatch queries to the
database, and format the response according to the API specifications, we use
the Flask framework in combination with the SQLAlchemy library. Some complex
and time-intensive queries, such as building merger trees or hierarchical
trees, have been manually profiled and optimized to ensure reasonable
execution times. To identify future bottlenecks in the API server, queries
that take a disproportionate amount of time to process are logged on the
server, allowing us to optimize the application as needed.

##  5 Discussion and Validation

In this section, we provide a quantitative discussion of the minimum bounding
ellipsoids available on cosmICweb and demonstrate how re-simulation targets
based on “well-behaved” ellipsoid potentially leads to a biased selection of
halos. Additionally, we present two zoom simulations of halos in our catalogs
as a proof-of-concept to demonstrate the capability of cosmICweb.

###  5.1 Efficiency distribution of minimum bounding ellipsoids

Figure 6: Top: Efficiency of the Lagrangian minimum bounding ellipsoids as a
function of the halo mass ($M_{\mathrm{vir}}$). Shown is the median efficiency
and the 66% and 90% contours. The minimal bounding ellipsoids are computed
from particles within $R_{\mathrm{tb}}=1,2,4$, and $10R_{\mathrm{vir}}$ of
free halos in the 150MPC, 300MPC, and the Rhapsody simulation at redshift
$z=0$. The dashed vertical lines show the mass of 1000 particles for
estimating the convergence threshold. Bottom: Efficiency of the Lagrangian
minimum bounding ellipsoids from halos in the 300MPC simulation as a function
of the halo mass ($M_{\mathrm{vir}}$) normalized by the nonlinear mass
$M_{\star}(z)$ at redshifts $z=0,1$, and 2. Figure 7: Relative change of mass
of Lagrangian proto-halo patches (top) and the efficiency parameter (bottom)
with increasing $R_{\mathrm{tb}}$ for halos in the mass bins detailed in Table
3. For the mass increase, we separately plot the change in
$M_{\mathrm{ellipsoid}}$ (dashed) and $M_{\mathrm{particles}}$ (solid)
separately. In addition, we subdivide two mass bins according to the
environmental parameter $D_{1,1}$, shown on the right.

While high mass halos generally originate from more spherical patches (Bardeen
et al., 1986; Rossi et al., 2011), lower mass halos in regions with strong
tidal fields can have more elongated and irregular proto-halos, leading to a
less efficient fit of the minimal bounding ellipsoid. This mass-efficiency
relation has been analyzed in Oñorbe et al. (2013), which found an increased
mean inefficiency and a larger standard deviation for lower mass halos.

In Figure 6 (top), we show the median efficiency (see the definition in
Equation 1) of the 150MPC, 300MPC, and the Rhapsody_PlanckCosmo simulation,
and the variance depending on the halo mass and the traceback-radius. As halo
mass increases, the median efficiency increases at all traceback-radii and its
distribution becomes more coherent. At the low-mass end, not all ellipsoids
have converged due to the low number of particles used to compute the
ellipsoid. This can be seen by the upturn in the median efficiency for
$R_{\mathrm{tb}}=1$ and 2 and the drop for the lowest masses. We highlight the
simulation dependent 1000-particle mass threshold, above which the ellipsoids
with $R_{\mathrm{tb}}=1R_{\mathrm{vir}}$ seem to converge.

As halos grow, more particles from the outskirts fall within the traceback-
radius. In the lower panel of Figure 6, we show the evolution of the ellipsoid
efficiency in the 300MPC simulation at $z=0,1$, and $2$. To account for the
mass evolution, we normalize the halo masses by the nonlinear mass
$M_{\mathrm{NL}}(z)$ defined by

$\sigma(M_{\mathrm{NL}},z)=D_{+}(z)\sigma(M_{\mathrm{NL}},z=0)=\delta_{c},$
(3)

where $\sigma(M,z)$ is the mass variance within a tophat kernel with radius
$R=(3M(4\pi\bar{\rho})^{-1})^{1/3}$, $D_{+}(z)$ is the linear growth function,
and $\delta_{c}=1.686$ is the critical overdensity for spherical collapse.
After correcting for the linear growth, efficiency distributions overlap among
the analyzed snapshots, except for the upturn and downturn for the non-
converged low-mass halos.

Table 3: Details of the mass bins used for Figure 7, including which simulation the halos are obtained from, number of host halos in that mass range, and corresponding number of particles. Note that the number of particles within the ellipsoid is larger than the number of bound particles within the virial radius, since not all particles are gravitationally bound and proto-halos are not perfectly ellipsoidal. mass range | # halos | simulation | # particles
---|---|---|---
$M_{\mathrm{vir}}$ [$h^{-1}\mathrm{M}_{\odot}$ ] |  |  | per halo
$(3,6)\times 10^{10}$ | 140736 | 150MPC | 110 – 220
$(1,2)\times 10^{11}$ | 51407 | 150MPC | 370 – 740
$(1,2)\times 10^{12}$ | 52030 | 300MPC | 470 – 930
$(1,2)\times 10^{13}$ | 6449 | 300MPC | 4670 – 9340
$(1,2)\times 10^{14}$ | 17803 |  Rhapsody_
PlanckCosmo | 1250 – 2500
Figure 8: Distribution of some intrinsic and environmental halo properties of
halos of the 300MPC simulation in the mass bin $M_{\mathrm{vir}}\in[2,4]\times
10^{12}$$h^{-1}\mathrm{M}_{\odot}$. We separate the halos with proto-halo
efficiency below the 10th ($\varepsilon\simeq 0.013$) and above the 90th
($\varepsilon\simeq 0.60$) percentile separately. Shown are the normalized
distance to the closest more massive neighbor $D_{1,1}$, the relative mass
within $10R_{\mathrm{vir}}$ $M_{10,1}$, the ellipticity of the minimum
bounding ellipsoid at $R_{\mathrm{tb}}=2R_{\mathrm{vir}}$, the concentration
parameter $c_{\mathrm{vir}}$, the spin parameter $\lambda_{\mathrm{Peebles}}$,
and the averaged mass history.

Choosing a larger traceback-radius increases the volume of the minimal
bounding ellipsoid. The exact growth of the Lagrangian ellipsoid depends on
the mass and the environment of the halo. To analyze the mass growth of the
proto-halo patches at different traceback radii, we compare halos from five
mass bins, detailed in Table 3. Additionally, we split the $10^{11}$
$h^{-1}\mathrm{M}_{\odot}$ and $10^{13}$ $h^{-1}\mathrm{M}_{\odot}$ bins into
three different environmental regimes, determined by the distance to the
closest more massive halo $D_{1,1}$ (see Table 2). The results are shown in
Figure 7.

The mass of the particles within $R_{\mathrm{tb}}$ as well as the minimal
bounding ellipsoid, increases faster with increasing $R_{\mathrm{vir}}$ for
lower-mass halos and halos in more clustered environments. For the lowest two
mass bins, the relative mass growth of the Lagrangian ellipsoids is largest
from 1 to 2 $R_{\mathrm{vir}}$ traceback-radii, whereas the proto-halos of the
more massive halos grow faster at larger $R_{\mathrm{tb}}$. In the case of
low-mass halos, the minimum bounding ellipsoid grows faster than the mass
within the traceback-radius, resulting in an efficiency drop at
$R_{\mathrm{tb}}=2R_{\mathrm{vir}}$, as shown in the lower panel. The trend
reverses for high-mass halos, where the number of particles within
$R_{\mathrm{tb}}$ increases faster than the volume of the Lagrangian
ellipsoid. For high-mass halos and for all halos at large $R_{\mathrm{tb}}$,
the average efficiency therefore increases.

When splitting the mass bins according to the environmental parameter
$D_{1,1}$, we find that the mass within $R_{\mathrm{tb}}$ rises more slowly
for isolated halos than for halos in close proximity to a more massive
neighbor. The presence of such a neighbor also leads to a faster growth of the
minimum bounding ellipsoid mass, lowering the mean efficiency parameter for
the low-mass bin.

In summary, the efficiency of the minimum bounding ellipsoid, i.e., the ratio
of the mass in the final target volume to the mass in the proto-halo patch, is
markedly influenced by the halo mass and its environment. Higher mass halos
and halos in isolated environments typically have a more spherical mass
distribution that is better described by an ellipsoid compared to lower mass
halos in dense, tidal regions. With the exception of the lowest mass bin, the
numerical overhead, measured as the fraction of additional particles not part
of the final target volume, decreases when the traceback-radius is increased.

###  5.2 Biased target selection by efficiency constraints

As discussed above, the environment clearly has a strong influence on the
efficiency parameter. When choosing targets for zoom simulations, one might be
inclined to select well-behaved proto-halos to minimize computational
requirements. However, this approach potentially selects isolated objects,
resulting in a biased sample within the chosen mass range. The environment is
connected to other halo properties via a process commonly referred to as
assembly bias (Gao et al., 2005; Wechsler et al., 2006; Gao & White, 2007;
Borzyszkowski et al., 2016), which means these parameters will also be
affected by the efficiency constraints.

In order to measure these effects, we select halos with Lagrangian
efficiencies below the 10th and above the 90th percentile from the
$M_{\mathrm{vir}}\in[2,4]\times 10^{12}$ $h^{-1}\mathrm{M}_{\odot}$ mass bin
of the 300MPC simulation at $z=0$. We then compute the distributions of a
selection of external and intrinsic parameters and compare it to the
distribution of all halos in that mass bin. The chosen parameters include the
normalized distance to the closest more massive halo $D_{1,1}$, the relative
mass increase between $1$ and $10R_{\mathrm{vir}}$ ($M_{10,1}$), the
concentration parameter $c_{\mathrm{vir}}$, the spin parameter
$\lambda_{\mathrm{Peebles}}$ (cf. Table 2), and the ellipticity of the minimum
bounding ellipsoid $e=(a_{3}-a_{1})(2\sum a_{i})$, where
$a_{i}=\lambda_{i}^{-1/2}$ are the semi-axes of the ellipsoid related to the
eigenvalues $\lambda_{1}\geq\lambda_{2}\geq\lambda_{3}$ of the shape matrix
(cf. Equation 2). The first two parameters measure the local clustering, while
the following two are intrinsic properties of the halo. The ellipticity
measures how spherical (and therefore well-behaved) the proto-halo patch is.
Merger events and strong tidal fields are expected to distort the proto-halo
patch, leading to a more elliptical and less efficient bounding ellipsoid.

In addition, we also measure the average mass growth history of the
subsamples. The results are shown in Figure 8. As shown above, the proto-halo
efficiency and the local clustering are strongly correlated. Selecting only
high-efficiency proto-halos results in a biased sample of preferentially
isolated halos, with large $D_{1,1}$ and low mass increase $M_{10,1}$.
Additionally, these proto-halo shapes are more spherical, indicating that the
halos were not exposed to strong tidal fields during their evolution.

The effect on the halo concentration and the halo spin are weaker. Halos with
high efficiency proto-halos tend to have lower spins, in agreement with
predictions from e.g., Tidal-Torque Theory (Doroshkevich, 1970; White, 1984),
in which the initial spin depends on the strength of the tidal tensor and its
misalignment with respect to the proto-halo shape. The mass evolution of high
efficiency proto-halos follows the average of the entire mass-bin. However,
low efficiency proto-halos accrete their mass early, with growth slowing down
at earlier times compared to the the mass-bin average.

###  5.3 Example Simulations

As a proof-of-concept, we present two zoom simulations originating from the
Eagle RefL0100N1504 volume. In addition to demonstrating the capability of
cosmICweb, these examples also serve as a check of the Panphasia white-noise
field integration into music. One of the challenges to incorporate Panphasia
is that (currently) music requires power-of-two box resolutions due to the
implementation of a multigrid Poisson solver to compute LPT terms (see Hahn &
Abel, 2011). This constraint requires a Fourier-space remapping of Panphasia
fields. Specifically, we generate the Panphasia fields at the native
resolution, e.g., $3\times 2^{\ell}$ where $\ell\in\mathbb{N}$ is some
subdivision level, and then cut out the modes representable at the next
smaller power of two (in the example $2^{\ell+1}$). This naturally introduces
some small discrepancies and warrants an investigation how well we can
reproduce existing zoom simulations with music and its Panphasia integration.

Note that music allows to specify a minimum refinement level for the entire
simulation, levelmin, which sets the number and mass of the coarsest particles
in the zoom simulation, and a minimum refinement level when generating the
initial conditions levelmin_TF. The particle displacements and velocities are
later averaged down to levelmin. We present simulations varying both. This
tests the dependence on the coupling of small-scale modes into the high-res
regions, i.e. the accuracy of the zoom approach itself as well as its
implementation in music, and the impact of a coarsely represented tidal field
on the non-linear evolution during the simulation.

#### Milky-Way sized halo

Figure 9: Visual comparison of the Au6 halo (Grand et al., 2017) between the reference simulation and three zoom simulations (cf. Figure 10). We calculate the surface density using a Delaunay tessellation field estimator (Rangel et al., 2016). The white circle indicates $R_{\mathrm{200c}}$ determined in the reference simulation. Figure 10: Density profile comparison for the Au6 halo (Grand et al., 2017). We compare three zoom simulations initialized with music using proto-halo patches from cosmICweb against the reference simulation by Grand et al. (2017) that has been initialized with Panphasia. The proto-halo patches have been calculated at a traceback radius of $4R_{\mathrm{200c}}$. In addition to the total matter density profiles (solid) we highlight the contribution of low resolution particles outside the refinement region separately (dashed). The lower panel shows the relative difference of the total matter density profile with respect to the reference solution. Table 4:  Global halo properties of the Milk-Way-sized Auriga 6 halo in the re-simulations at different resolution – see also Figure 9 for images. Reference values have been re-measured from the original simulation (Grand et al., 2024) with consistent rockstar parameters and slightly differ from the values reported in Grand et al. (2024). simulation | (7,8,10) | (7,10,10) | (7,10,12) | Ref.
---|---|---|---|---
$m_{p}$ [$10^{5}\mathrm{M}_{\odot}$] | $364$ | $364$ | $5.69$ | $2.34$
$\epsilon_{\rm comoving}$ [$\mathrm{kpc}$] | 3.26 | 3.26 | 0.813 | 0.739
$M_{200,c}$ [$10^{10}\mathrm{M}_{\odot}$] | 105.3 | 108.1 | 105.2 | 105.4
$M_{500,c}$ [$10^{10}\mathrm{M}_{\odot}$] | 83.2 | 83.3 | 83.0 | 82.0
$M_{2500,c}$ [$10^{10}\mathrm{M}_{\odot}$] | 48.0 | 48.0 | 49.8 | 48.5
$R_{200,c}$ [kpc] | 214.5 | 216.3 | 214.4 | 214.5
$R_{s}$ [kpc] | 22.7 | 24.2 | 21.1 | 22.5
$c_{200,c}$ | 9.45 | 8.95 | 10.18 | 9.53

The first example is the Au6 halo from the Auriga Project (Grand et al., 2017,
2024). This Milky Way mass halo has previously been studied to analyze
formation mechanisms of disc galaxies. We perform gravity only zoom
simulations at different levels of refinement of the Au6 Lagrangian patch,
calculated at a traceback radius $R_{\mathrm{tb}}=4R_{\mathrm{200c}}$ at
redshift 0. The halo can be found in the publication section on cosmICweb
777Auriga 6: https://cosmicweb.eu/publication/auriga-halos.

We re-simulated this halo at two different mass resolutions (levelmax=10 and
12). In addition, we once generated the initial noise field at the full
resolution followed by averaging in the coarse regions
(levelmin_TF=levelmax=10 : (7,10,10)), and using the zoom technique
(levelmin_TF $<$ levelmax: (7,8,10) and (7,10,12)). The re-simulation was
carried out using the swift $N$-body code (Schaller et al., 2024). The
respective mass resolution of all runs along with the force softening are
indicated in Table 4. We compare these halos with the redshift zero output of
a reference calculation, which is an original Auriga level 4 gravity only
simulation run with gadget. The initial conditions for the reference were
created by the ic_gen code (Jenkins, 2010, 2013) using a $1536^{3}$ cell patch
from the Panphasia field. The nearest smaller representable resolution in
music is levelmin_TF=10 with $2^{10}=1024$. We determined halo properties
using the rockstar halo finder (Behroozi et al., 2013a) and list the recovered
main properties of the halos in Table 4, as well as show the projected mass
distribution in Figure 9 and spherically averaged differential mass density
profiles in Figure 10.

Regarding global properties, we find that the halo mass $M_{\mathrm{200,c}}$
is recovered at the level of better than 1 per cent for two re-simulations,
while the third, $(7,10,10)$, is somewhat more discrepant at $\sim 3$ per
cent. The virial radius always agrees to better than one per cent. Properties
of the inner mass distribution (concentration $c_{200c}$ and mass $M_{2500c}$)
of the halos are slightly more discrepant but still consistent at the few per
cent level. Note that the rockstar measurement of the mass and other
properties of the reference run is slightly discrepant from the values
reported in Grand et al. (2024) using subfind.

The visual impression from Figure 9 is similar in the overall shape of the
halo, also positions of some halos outside the virial radius are in excellent
agreement. Interestingly, the most massive substructure is however at
different locations in all of the runs — likely a consequence of it being very
sensitive to any perturbations of the initial conditions.

Finally, the density profiles shown in Figure 10 show agreement at the few per
cent level at all radii up to the radius affected by finite force and mass
resolution and out to the maximum radius of the zoom region of $4R_{200c}$.

#### Group-sized halo

Figure 11: Comparison of the dark matter density and gas properties between
three re-simulations of the halo described in Richings et al. (2021) using
swift (Schaller et al., 2024) (top), gizmo (Hopkins, 2015) (middle), and
ramses (Teyssier, 2002) (bottom). The four columns show the dark matter
density, gas density, gas temperature, and gas entropy in a slice
perpendicular to the $z$-axis centered on the halo. The white circle indicates
the $R_{200c}$ spherical overdensity radius measured in the gizmo run. See
main text for simulation details.

The second halo we resimulate is a group-mass halo that has been featured in a
study quantifying the impact of baryonic physics on halo and substructure
properties (Richings et al., 2021). This halo is also from the Eagle 100 Mpc
volume and its GroupID from the Eagle database in the reference model is 129
in snapshot 26 ($z=0.18$)888Initial conditions on cosmICweb can be accessed
under https://cosmicweb.eu/publication/group-sized-halo-richings-2021.

The initial conditions for the resimulation have been generated with music
using levelmin=7, levelmin_TF=10, and levelmax=11, with the zoom region
obtained from the $R_{tb}=8R{200c}$ Lagrangian ellipsoid. For these
resimulations, we follow a different strategy in that we demonstrate how
cosmICweb facilitates the reproducibility of cosmological simulation results
across different simulation codes by leveraging the plethora of output plugins
available for the music code. Specifically, we show resimulations, at fixed
resolution of the initial conditions, using the ramses (Teyssier, 2002) code
(Eulerian adaptive mesh finite volume hydrodynamics and adaptive mesh particle
mesh gravity solver), the gizmo (Hopkins, 2015) code (Mesh-free Lagrangian
finite volume hydrodynamics with a tree-particle mesh gravity solver), and
swift (Schaller et al., 2024) (smooth particle hydrodynamics (SPH) Lagrangian
hydrodynamics with fast multipole-particle mesh gravity solver) code. For
these simulations, initial conditions were generated for both dark matter and
baryons (neglecting any baryon isocurvature perturbations so that the two
initially perfectly trace each other). We assumed non-radiative evolution for
the gas, disabling any extra astrophysics such as a UV background and feedback
processes. For gizmo and swift, we set a comoving gravitational force
softening of $2\,\text{kpc}$. For ramses, we enabled Lagrangian refinement
with a maximum refinement level of 18, so that the smallest grid cells at
$z=0$ have a linear size of $\sim 0.4$ kpc.

In Figure 11, we show the resulting dark matter density, gas density, gas
temperature, and gas entropy maps at the end of the simulations for each of
the three codes. We find a generally good agreement of the large-scale
structure visible in the three simulations, in both dark matter and gas
density, as well as a good agreement in the thermodynamic properties of the
gas inside the virial shock of the halo. While some differences exist in the
thermodynamic properties of the gas, we leave a detailed discussion of them to
code comparison studies, such as e.g. Roca-Fàbrega et al. (2021). Instead, we
note that with cosmICweb, it is easily possible to resimulate any object
listed in the database with all of the state-of-the-art cosmological
hydrodynamics, which we expect to greatly facilitate the reproducibility of
research in this area.

##  6 Conclusion

We have presented the cosmICweb project, an online platform that aims to
simplify the creation and sharing of zoom simulations. We have laid out the
current work-flow for setting up and running zoom simulations and showed how
cosmICweb integrates with this process. In particular, we address three key
steps: i) finding and selecting targets for zoom simulations, ii) retrieving
the zoom initial conditions with a high resolution region placed on the
Lagrangian volume of the target, and iii), sharing the initial condition
configuration in research publication, allowing the community to compare and
verify scientific results. We envision the following workflow with cosmICweb:

  1. 1.

select a suitable simulation for simulation target,

  2. 2.

choose appropriate filters in the _Halo Finder_ and query results,

  3. 3.

preselect some halos using the property list and scatter plot and store them
in a new collection,

  4. 4.

take a closer look at individual halos in the _Halo Explorer_ ,

  5. 5.

select the target halo(s) by either removing halos from the collection or by
creating a new collection,

  6. 6.

download the initial conditions configuration files for the halo(s) using the
_Downloader_ overlay or the music plugin,

  7. 7.

create the initial conditions with music and run the zoom simulations,

  8. 8.

when publishing the results, promote the collection to a publication, include
the generated cosmICweb link in the article, and add a link to the article in
cosmICweb.

The ability to access initial conditions from published research will allow
the scientific community to easily verify results and compare and improve
different models of baryonic physics and code implementations, which will
ultimately help us to improve our overall understanding of the evolution of
galaxies and clusters in our Universe.

The proto-halo patches offered by cosmICweb are in the form of minimal
bounding ellipsoid, which capture the shape of a typical proto-halo patch
without requiring a large amount of storage. For each halo, we provide
ellipsoids at varying traceback-radii. To evaluate the goodness-of-fit of
these ellipsoids, thereby determining the computational overhead by high-
resolution particles not part of the final target volume, we have provided an
analysis of the ellipsoid efficiency parameters. We find that the efficiency
is significantly shaped by the halo’s mass and environmental context. High-
mass halos and those in less dense environments tend to have more spherical
mass distributions, which are more accurately encapsulated by ellipsoids than
those of lower mass halos in regions with strong tidal forces.

In the context of selecting targets for zoom simulations, choosing proto-halos
that are well-described by their ellipsoids can reduce computational demands.
Our Halo-Finder tool leverages ellipsoid efficiency as a criterion for
selection. However, this may lead to a preference for isolated halos,
potentially introducing a bias in the formation histories of the selected
objects. This bias warrants careful consideration to ensure an unbiased
representation in zoom-simulation studies.

We provide an updated version 2 of the music 999The updated version 2 of music
is available from https://github.com/cosmo-sims/MUSIC initial condition
generator software that supports Panphasia noise fields along with other
improvements such as e.g., a reduced memory usage and direct interfacing with
the Class linear Einstein-Boltzmann code.

Development of cosmICweb is a continuing effort, since the data as well as the
requirements change over time, requiring adaptations and extensions to the
project. We categorize future developments into two classes: the addition of
new data, in particular the integration of new simulations, and the addition
of new features. The kind of improvements that we will consider highly depends
on the feedback and requirements from the community.

###  6.1 New simulations and data

New halo catalogs and proto-halos from additional simulations can either be
directly hosted on cosmICweb servers, or from existing or new databases
located outside. To facilitate the setting up of the API interface, we may
publish our own implementation described in Section 4.3 as a template in the
future.

Running zoom simulations on targets in the cosmICweb database will naturally
improve the knowledge on these objects, for example improving estimates of
properties of the halo and the embedded galaxy and better constraining
Lagrangian volumes of the proto-halo. In the future, we hope to provide a
mechanism to feed this information back into the database and therefore
provide more fine-grained information for later runs on these targets.

###  6.2 Additional functionality

As a result of the modular design, new exploration features and filters can be
easily added in the future if required. Such features may include
visualizations of the full halo merger tree with the ability to navigate
different progenitor branches, animations of the halo growth, motion, and
merger events using the already existing 3D framework for the visualization of
the halo surrounding, or a tool to find similar halos in the database for
statistical studies (e.g., by suggesting halos with similar properties).

For initial conditions generation and proto-halos, we plan to add support for
alternative generators, such as the native implementation of ic_gen (Jenkins,
2010, 2013) in addition to the music plugin. We may also consider supporting
additional descriptors of proto-halo shapes, such as convex hulls, for more
optimized zoom simulations.

## Data availability

The data underlying this article will be shared on reasonable request to the
corresponding author.

## Acknowledgements

MB and OH acknowledge funding from the European Research Council (ERC) under
the European Union’s Horizon 2020 research and innovation programme (grant
agreement No. 679145, project ’COSMO-SIMS’). JCH and ARJ acknowledge support
from UKRI grants ST/T000244/1 and ST/X001075/1.

This work used the DiRAC@Durham facility managed by the Institute for
Computational Cosmology on behalf of the STFC DiRAC HPC Facility
(www.dirac.ac.uk). The equipment was funded by BEIS capital funding via STFC
capital grants ST/K00042X/1, ST/P002293/1 and ST/R002371/1, Durham University
and STFC operations grant ST/R000832/1. DiRAC is part of the UK National
e-Infrastructure. This work was granted access to the HPC resources of
TGCC/CINES under the allocation A0060410847 attributed by GENCI (Grand
Equipement National de Calcul Intensif). Some of the computational results
presented have been achieved using the Vienna Scientific Cluster (VSC).

## References

  * Angulo & Hahn (2022) Angulo, R. E., & Hahn, O. 2022, Living Reviews in Computational Astrophysics, 8, 1, doi: 10.1007/s41115-021-00013-z
  * Bardeen et al. (1986) Bardeen, J. M., Bond, J. R., Kaiser, N., & Szalay, A. S. 1986, ApJ, 304, 15, doi: 10.1086/164143
  * Behroozi et al. (2013a) Behroozi, P. S., Wechsler, R. H., & Wu, H.-Y. 2013a, ApJ, 762, 109, doi: 10.1088/0004-637X/762/2/109
  * Behroozi et al. (2013b) Behroozi, P. S., Wechsler, R. H., Wu, H.-Y., et al. 2013b, ApJ, 763, 18, doi: 10.1088/0004-637X/763/1/18
  * Bertschinger (2001) Bertschinger, E. 2001, ApJS, 137, 1, doi: 10.1086/322526
  * Borzyszkowski et al. (2016) Borzyszkowski, M., Porciani, C., Romano-Diaz, E., & Garaldi, E. 2016, MNRAS, 000, 1.  https://arxiv.org/abs/1610.04231
  * Bryan & Norman (1998) Bryan, G. L., & Norman, M. L. 1998, ApJ, 495, 80, doi: 10.1086/305262
  * Bullock et al. (2001) Bullock, J. S., Dekel, A., Kolatt, T. S., et al. 2001, ApJ, 555, 240, doi: 10.1086/321477
  * Crain et al. (2015) Crain, R. A., Schaye, J., Bower, R. G., et al. 2015, MNRAS, 450, 1937, doi: 10.1093/mnras/stv725
  * Dolag et al. (2009) Dolag, K., Borgani, S., Murante, G., & Springel, V. 2009, MNRAS, 399, 497, doi: 10.1111/j.1365-2966.2009.15034.x
  * Doroshkevich (1970) Doroshkevich, A. G. 1970, Astrophysics, 6, 320, doi: 10.1007/BF01001625
  * Gao et al. (2005) Gao, L., Springel, V., & White, S. D. M. 2005, MNRAS: Letters, 363, L66, doi: 10.1111/j.1745-3933.2005.00084.x
  * Gao & White (2007) Gao, L., & White, S. D. M. 2007, MNRAS: Letters, 377, L5, doi: 10.1111/j.1745-3933.2007.00292.x
  * Garaldi et al. (2016) Garaldi, E., Baldi, M., & Moscardini, L. 2016, J. Cosmology Astropart. Phys, 2016, 050, doi: 10.1088/1475-7516/2016/01/050
  * Grand et al. (2024) Grand, R. J. J., Fragkoudi, F., Gómez, F. A., et al. 2024, arXiv e-prints, arXiv:2401.08750, doi: 10.48550/arXiv.2401.08750
  * Grand et al. (2017) Grand, R. J. J., Gómez, F. A., Marinacci, F., et al. 2017, MNRAS, 467, 179, doi: 10.1093/mnras/stx071
  * Hahn & Abel (2011) Hahn, O., & Abel, T. 2011, MNRAS, 415, 2101, doi: 10.1111/j.1365-2966.2011.18820.x
  * Hahn et al. (2017) Hahn, O., Martizzi, D., Wu, H.-Y., et al. 2017, MNRAS, stx001, doi: 10.1093/mnras/stx001
  * Heitmann et al. (2019) Heitmann, K., Uram, T. D., Finkel, H., et al. 2019, ApJS.  https://arxiv.org/abs/1904.11966
  * Hinshaw et al. (2013) Hinshaw, G., Larson, D., Komatsu, E., et al. 2013, ApJS, 208, 19, doi: 10.1088/0067-0049/208/2/19
  * Hopkins (2015) Hopkins, P. F. 2015, MNRAS, 450, 53, doi: 10.1093/mnras/stv195
  * Jenkins (2010) Jenkins, A. 2010, MNRAS, 403, 1859, doi: 10.1111/j.1365-2966.2010.16259.x
  * Jenkins (2013) Jenkins, A. 2013, MNRAS, 434, 2094, doi: 10.1093/mnras/stt1154
  * Jiang et al. (2014) Jiang, L., Helly, J. C., Cole, S., & Frenk, C. S. 2014, MNRAS, 440, 2115, doi: 10.1093/mnras/stu390
  * Katz et al. (1994) Katz, N., Quinn, T., Bertschinger, E., & Gelb, J. M. 1994, MNRAS, 270, L71, doi: 10.1093/mnras/270.1.L71
  * Khachiyan (1996) Khachiyan, L. G. 1996, Mathematics of Operations Research, 21, 307, doi: 10.1287/moor.21.2.307
  * Kim et al. (2013) Kim, J.-h., Abel, T., Agertz, O., et al. 2013, ApJS, 210, 14, doi: 10.1088/0067-0049/210/1/14
  * Komatsu et al. (2011) Komatsu, E., Smith, K. M., Dunkley, J., et al. 2011, ApJSupplement Series, 192, 18, doi: 10.1088/0067-0049/192/2/18
  * Kumar & Yildirim (2005) Kumar, P., & Yildirim, E. A. 2005, Journal of Optimization Theory and Applications, 126, 1, doi: 10.1007/s10957-005-2653-6
  * McAlpine et al. (2016) McAlpine, S., Helly, J., Schaller, M., et al. 2016, Astronomy and Computing, 15, 72, doi: 10.1016/j.ascom.2016.02.004
  * McBride et al. (2009) McBride, C., Berlind, A., Scoccimarro, R., et al. 2009, in Bulletin of the American Astronomical Society, Vol. 41, American Astronomical Society Meeting Abstracts #213, 253
  * Navarro & White (1994) Navarro, J. F., & White, S. D. M. 1994, MNRAS, 267, 401, doi: 10.1093/mnras/267.2.401
  * Nelson et al. (2019) Nelson, D., Springel, V., Pillepich, A., et al. 2019, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x
  * Oñorbe et al. (2013) Oñorbe, J., Garrison-Kimmel, S., Maller, A. H., et al. 2013, MNRAS, doi: 10.1093/mnras/stt2020
  * Peebles (1971) Peebles, P. J. E. 1971, A&A, 11, 377
  * Planck Collaboration (2015) Planck Collaboration. 2015, arXiv.  https://arxiv.org/abs/1502.01589
  * Qu et al. (2017) Qu, Y., Helly, J. C., Bower, R. G., et al. 2017, MNRAS, 464, 1659, doi: 10.1093/mnras/stw2437
  * Rangel et al. (2016) Rangel, E., Li, N., Habib, S., et al. 2016, in 2016 IEEE International Conference on Cluster Computing (CLUSTER), 30–39, doi: 10.1109/CLUSTER.2016.40
  * Richings et al. (2021) Richings, J., Frenk, C., Jenkins, A., Robertson, A., & Schaller, M. 2021, MNRAS, 501, 4657, doi: 10.1093/mnras/staa4013
  * Roca-Fàbrega et al. (2021) Roca-Fàbrega, S., Kim, J.-H., Hausammann, L., et al. 2021, ApJ, 917, 64, doi: 10.3847/1538-4357/ac088a
  * Rossi et al. (2011) Rossi, G., Sheth, R. K., & Tormen, G. 2011, MNRAS, no, doi: 10.1111/j.1365-2966.2011.19028.x
  * Schaller et al. (2024) Schaller, M., Borrow, J., Draper, P. W., et al. 2024, MNRAS, 530, 2378, doi: 10.1093/mnras/stae922
  * Schaye et al. (2015) Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521, doi: 10.1093/mnras/stu2058
  * Schaye et al. (2023) Schaye, J., Kugel, R., Schaller, M., et al. 2023, MNRAS, 526, 4978, doi: 10.1093/mnras/stad2419
  * Springel (2005) Springel, V. 2005, MNRAS, 364, 1105, doi: 10.1111/j.1365-2966.2005.09655.x
  * Springel et al. (2001) Springel, V., White, S. D. M., Tormen, G., & Kauffmann, G. 2001, MNRAS, 328, 726, doi: 10.1046/j.1365-8711.2001.04912.x
  * Teyssier (2002) Teyssier, R. 2002, A&A, 385, 337, doi: 10.1051/0004-6361:20011817
  * Vogelsberger et al. (2020) Vogelsberger, M., Marinacci, F., Torrey, P., & Puchwein, E. 2020, Nature Reviews Physics, 2, 42, doi: 10.1038/s42254-019-0127-2
  * Wechsler et al. (2006) Wechsler, R. H., Zentner, A. R., Bullock, J. S., Kravtsov, A. V., & Allgood, B. 2006, ApJ, 652, 71, doi: 10.1086/507120
  * White (1984) White, S. D. M. 1984, ApJ, 286, 38, doi: 10.1086/162573
  * Wu et al. (2013a) Wu, H.-Y., Hahn, O., Wechsler, R. H., Behroozi, P. S., & Mao, Y.-Y. 2013a, ApJ, 767, 23, doi: 10.1088/0004-637X/767/1/23
  * Wu et al. (2013b) Wu, H.-Y., Hahn, O., Wechsler, R. H., Mao, Y.-Y., & Behroozi, P. S. 2013b, ApJ, 763, 70, doi: 10.1088/0004-637X/763/2/70

##  Appendix A API Server Requirements

The communication between the cosmICweb application and external databases is
handled by API Servers following a predefined set of rules. Since these
specifications are subject to change over time, the outline given here
reflects the state at the date of release. Up-to-date and more detailed
specifications can be found directly on the
webpage101010https://cosmicweb.eu/documentation/api.

The API consists of several endpoints that receive requests from the client
application and send the requested data back in the JSON format. We list and
explain these endpoints in Table 5. Different simulations may not contain all
data and support all features. This is why we differentiate between _required_
and _optional_ endpoints. The information on which features and properties are
available for each simulation are stored on the central server.

Table 5: List of API endpoints, split into required and optional features. The endpoints use the HTTP GET method with the exception of /halo_finder, for which the selectors will be transmitted by a POST request. The endpoint paths are relative to the simulation API URL stored on the cosmICweb application server. The precise format of the request and JSON response is specified on cosmICweb. endpoint | request |
---|---|---
Required Endpoints |  |
/halo_finder | POST |  Interface for halo queries, accepting filters to specify halo parameter ranges, mass definitions, availability of ellipsoids, etc.
/snapshots | GET |  List of available snapshots for the simulation, including redshifts and number of halos
/snapshots/<id> | GET |  Details of a specific snapshot such as redshift and number of halos
/halo/<id> | GET |  Details of a specific halo (and associated galaxy if available). The returned properties depend on the enabled features for the simulation (cf. Table 2).
/halo/<id>/ellipsoids | GET |  All Lagrangian ellipsoids for the halo (measured at different traceback-radii), specified by the center, shape matrix, and traceback-radius (optional: number of particles within $R_{\mathrm{tb}}$ and efficiency parameter).
/halo/<id>/ellipsoids/<id> | GET |  Definition of a specific Lagrangian ellipsoid
Optional Endpoints |  |
/snapshots/<id>/massfunction | GET |  The halo mass function computed from all host halos in the snapshot.
/halo/<id>/substructure | GET |  Substructure tree of the halo
/halo/<id>/main_progenitors | GET |  List of halos corresponding to the main progenitors sequence of the specified halo
/halo/<id>/mergertree | GET |  Merger tree of the halo. The merger threshold can be specified by the request argument ?mm_fraction=<r>, where $r$ is the ratio between merging and resulting halo. A 1:3 merger for example corresponds to $r=0.25$.
/halo/<id>/surrounding | GET |  All halos within a certain distance of the halo. The distance can be specified by the request argument ?box_size=<m>, where $m$ is a multiplier to the halo radius.
/halo/<id>/tracking | GET |  A list of coordinates of the main progenitors, normalized to the unit cube
/halo/<id>/images | GET |  If there are images or graphics associated with the halo, return a list of image URLs and descriptions

The required endpoints are necessary for the basic functionality of cosmICweb.
These include information on the available snapshots, the halo-finding
capability, basic information on individual halos, and their Lagrangian
ellipsoids. The optional endpoints are mainly used for visualizations of the
halo substructure, the formation history, and the local environment of the
halo. For each simulation in cosmICweb, individual features can be enabled,
and the interface automatically adapts to show the available information.

External datasets can be integrated in cosmICweb by setting up a server
listening on the various endpoints, parsing incoming requests, querying the
dataset, and returning the result in the specified format. For the cosmICweb
simulations, we are using the flask framework for that purpose, but in general
any web server that can parse and process the requests is suitable.

##  Appendix B cosmicweb-music Command Line Interface

To facilitate downloading initial conditions from cosmICweb to computing
facilities and local machines, we provide a Python command-line
executable111111https://github.com/cosmo-sims/cosmICweb-music that interfaces
with the cosmICweb API. It allows downloading individual initial condition
configuration files and bulk configurations for collections and publication
lists. The executable has two modes (publication and get) and is used as:

cosmicweb-music

[--output-path OUTPUT_PATH] [--common-directory]
[publication [--traceback-radius RADIUS] PUBLICATION_TAG]
(or)
[get DOWNLOAD_STRING]

\--output-path OUTPUT_PATH:

By default, the music configuration files are stored in the current working
directory in a sub-directory named after the publication or the simulation of
the halos. If \--output-path is specified, the script stores the files in this
directory instead without creating the subdirectory. Folders for each halo
will be created however, unless the \--common-directory flag is set.

\--common-directory:

If this option is set, all music configuration files will be stored in the
same directory instead of individual directories for each halo.

publication [--traceback-radius TRACEBACK_RADIUS] PUBLICATION_TAG:

The tag of the publication can be used to download zooms for all halos within
the publication list. The traceback-radius for the proto-halo patches is set
to 2 as default but can be changed. The executable will skip a halo if no
minimum bounding ellipsoid with the specified traceback radius exists.

get DOWNLOAD_STRING:

A download string can be generated from the download interface on cosmICweb.
This string can then be used to download the music configuration files with
this executable. Configuration settings from the download window will be
applied during the process.

Figure 12: Screenshots of some visualization tools in the _Halo Explorer_ :
overview of the halo parameters and links for main progenitor and descendant
halo (top row), evolution of the halo parameters along the main progenitor
branch (middle left), visualization of the major merger events with variable
threshold (bottom left), list and projections of the Lagrangian minimum
bounding ellipsoids (middle right), and a 3D WebGL rendering of halos in the
surrounding.