dataesr/scientific-paragraphs-categorization huggingface dataset:
Precision cosmology with gravitational wave (GW) sources requires a deeper understanding of the interplay between GW source population and cosmological parameters governing the dynamics of the Universe. With the swift increase in GW detections, it is necessary to develop a tool for exploring many aspects of cosmology and fundamental physics; this tools allows to simulate GW mock samples for several populations and cosmological models. We have developed a new code called GWSim, which allows us to make a large sample of GW mock events from a broad range of configurations, while varying the cosmology, the merger rate, and the GW source parameters (i. e. mass and spin distributions in particular) for a given network of GW detectors. A large sample of simulated mock GW events will be useful for improving our understanding of the statistical properties of the distribution of GW sources, as long as it is detectable for a given detector noise and an astrophysical and cosmological model.
It will also be useful to compare simulated samples with the observed distribution of the GW sources from data and infer the underlying population of the GW source parameters and cosmology. We restricted the cosmology to spatially flat universes, including models with varying dark energy equation of state. The GWSim code provides each mock event with a position in the sky and a redshift; these values can be those of random host galaxies coming from an isotropic and homogeneous simulated Universe or a user-supplied galaxy catalog. We used realistic detector configurations of the LIGO and Virgo network of detectors to demonstrate the performance of this code for the latest observation runs and the upcoming observation run.
2019b (Abbott et al. , 2021a (Abbott et al. , 2023a,b),b). To understand these, we need realistic simulations of GW events for different astrophysical populations and cosmological models, including realistic detector sensitivities. ⋆ GWSim is publicly available at: https://git. ligo. org/ benoit. revenu/gwsim We used GWSim to simulate a sample of mock GW sources for different (i) mass, (ii) spin, and (iii) merger rate models, as well as for the flat Λ cold dark matter (ΛCDM) and flat w 0w a CDM cosmological models. The code assigns host galaxies to GW events in two cases: assuming a constant in comoving volume distribution of galaxies or using a simulated galaxy catalog, such as MICECATv2  (Fosalba et al. 2014 (Fosalba et al. , 2015;; Crocce et al. 2015). The code also considers different detector sensitivities and observation periods.
Then, the Bilby  (Ashton et al. 2019)  pipeline is used to generate the actual GW strains corresponding to these GW events and perform parameter estimation to finally obtain mock GW samples. Having access to these sets of samples allows us to estimate the performances of the analysis algorithms. For one given GW event, its associated samples consist of a list of values obtained by a Markov chain Monte Carlo (MCMC) algorithm of all the parameters describing the binary system; in particular, the luminosity distance of the source, the masses of the two black holes, and their spins. We applied this simulation code GWSim to generate mock sources with the detector sensitivity of the fourth observation run O4 of LIGO-Virgo-KAGRA collaboration and we came up with a forecast for the measurability of the astrophysical population parameters and Hubble constant.
We find that GWSim will be useful to generate mock samples for different future detector sensitivities as well as varied astrophysical populations and cosmological parameters. The paper is structured as follows. In Sect. 2, we describe different models included in the current version of GWSim. In Sects. 3 and 4, we describe the structure of the code and validated different setups. Finally, in Sect. 5, we discuss the use and future developments of this code.
Code description GWSim is a code to make simulated GW transient catalogs (GWTCs) by assigning GW events to galaxies. The galaxies are taken from a galaxy catalog (or without the usage of a galaxy catalog, simply creating a redshift distribution of galaxies assuming they are distributed uniformly in comoving volume, namely, without any spatial clustering) for different astrophysical models of the binary compact objects and different cosmological models. The basic structure of the code and a flow chart showing the working principle of the code are presented in Fig. 1. We describe the structure of the code below. The code is composed of five different modules, (i) astrophysical and cosmological module, (ii) galaxy catalog module, (iii) detector noise module, (iv) injection module, and (v) parameter estimation module.
The galaxy host is randomly chosen in agreement with the merger rate and using a weight that can be uniform or proportional to the galaxy luminosity; the redshift of the event and its sky position are taken to be those of the host galaxy. In this case, the galaxies are assumed to follow a uniform comoving volume distribution and they are distributed uniformly over the sky. From the chosen redshifts, the luminosity distances to the GW sources are calculated using the selected cosmological model. The final step of this process is to estimate the matched filtering (S/N) for a chosen network of detectors accordingly to the duty factors and observation time, using the corresponding noise PSDs (which are available in the detector module). The GW events having a (S/N) greater than the detection threshold (S/N) thr chosen by a user make up the detected events and then undergo parameter estimation using the publicly available code Bilby.
The distribution of galaxies in the sky depends on the galaxy catalog. Most of the galaxy catalogs are only covering a small fraction of the sky. In this case, GW events are going to originate from that part only. Full sky galaxy catalogs, like GLADE+  (Dálya et al. 2022) , can be used to avoid this limitation. In the future, this module will be developed further to include different formation channels of GW sources and host galaxy properties. At present, the code cannot take into account the galaxy catalog's incompleteness. This will be added in the future where GWSim will be able to account for galaxies that host GW events but are not present in the galaxy catalog. Below, we describe a mock galaxy catalog that is incorporated in the GWSim code. We have used the mock galaxy catalog MICE-Grand Challenge Galaxy and Halo Light-cone Catalog (MICECATv2;  Fosalba et al.
4 M i , (13) M i = m evol,i -5 log 10 d L (z, H 0 ) Mpc -25, (14) where d L (z, H 0 ) is the luminosity distance of the galaxy, as a function of the measured redshift and the Hubble constant, in units of Mpc. In the following discussion, we explain how MICECATv2 can be used in GWSim to assign galaxies from the catalog to GWTC events. We start by reading the catalog file and by selecting the data columns that we are interested in. We select the sky location in equatorial coordinates (RA and Dec), the true redshift of the galaxies, and the observed magnitude, m evol , for a specific band. Since MICECATv2 is a simulated galaxy catalog, no redshift uncertainty is present. However, GWSim can add redshift uncertainties by taking a certain percentage (user given) of the true redshift and using this as the 1σ standard deviation.
The detected events that pass the (S/N) thr undergo parameter estimation using Bilby, to obtain the posteriors of their parameters, namely individual masses (m 1 and m 2 ) in detector frame, luminosity distance (D L ), source position (RA and Dec), and the inclination angle (θ JN ) for a single event. Having a set of posterior samples at our disposal allows us to use a population and/or cosmology estimation code and retrieve the injected values. We used the python package IcaroGW which can estimate jointly the population parameters of BBHs and the cosmological parameters  (Mastrogiovanni et al. 2021 ). We used a power-law+Gaussian peak distribution for the primary mass model and the flat-ΛCDM for cosmology. In IcaroGW, we fix Ω m = 0. 3 and allow only H 0 to vary. The posteriors obtained on the black hole population parameters and the Hubble constant for O1+O2+O3+O4 configuration are shown in Fig.
Conclusions We present a new GWSim package to simulate GW sources for different cosmological and GW population models. This code can incorporate five scenarios of GW mass models, four scenarios of GW spin distributions, two scenarios of the merger rate, and three models of the cosmological parameters, as described in Fig. 1. The code will be useful to simulate realistic mock samples of the GW sources for different networks of GW detectors. Currently, only second-generation detectors are being used for simulations but in the future, next-generation detectors such as the Cosmic Explorer  (Reitze et al. 2019; Hall et al. 2020 ) and Einstein Telescope  (Punturo et al. 2010)  will also be included. For a chosen model of the GW source population parameters and cosmological parameters, the code generates samples of binary compact objects for a fixed amount of observation time, duty cycle, and configurations of the detector network.
The code can also make use of a simulated galaxy catalog MICECATv2 for assigning the mock GW samples to the redshift and sky position of the host galaxies, possibly using a luminosity weighting. In the future, a revised version will be made to include a real galaxy catalog such as GLADE+  (Dálya et al. 2022)  and  Duncan (2022). The GWSim code is also wrapped up with the code Bilby for parameter estimation of the GW source parameters. This code will be useful for carrying out simulations of GW sources and understand the interplay between GW source population and cosmology. This code can be used for the currently ongoing network of detectors and also for the next-generation GW detectors.
Acknowledgements. The authors thank  Simone Mastrogiovanni  for his helpful review of the manuscript as a part of LVK P&P. C. Karathanasis is partially supported by the  Spanish MINECO  under the grants  SEV-2016-0588  and  PGC2018-101858-B-I00 , some of which include  ERDF  funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. The work of  S. Mukherjee  is a part of the ⟨data|theory⟩ Universe-Lab which is supported by the  TIFR  and the  Department of Atomic Energy, Government  of A124, page  11  of 18 Karathanasis, C. , et al. : A&A,  677 , A124 (2023) India. This analysis is carried out at the computing facility of the LSC cluster. We acknowledge the use of data from the MICE simulations, publicly available at https://cosmohub.
2001). The authors are grateful for computational resources provided by the  LIGO Laboratory  and supported by  National Science Foundation  Grants  PHY-0757058  and  PHY-0823459. This research has made use of data or software obtained from the  Gravitational Wave Open Science Center  (gw-openscience. org), a service of  LIGO Laboratory , the  LIGO Scientific Collaboration , the  Virgo Collaboration , and  KAGRA. LIGO Laboratory and Advanced LIGO  are funded by the  United States National Science Foundation (NSF)  as well as the  Science and Technology Facilities Council (STFC)  of the  United Kingdom , the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of  Advanced LIGO  and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council.
Virgo is funded, through the  European Gravitational Observatory (EGO) , the  French Centre National de Recherche Scientifique (CNRS) , the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , and the  Dutch Nikhef , with contributions by institutions from  Belgium ,  Germany ,  Greece ,  Hungary ,  Ireland ,  Japan ,  Monaco ,  Poland ,  Portugal ,  Spain. The construction and operation of KAGRA are funded by the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) ,  Japan Society for the Promotion of Science (JSPS) ,  National Research Foundation (NRF) , and  Ministry of Science and ICT (MSIT) in Korea ,  Academia Sinica (AS)  and the  Ministry of Science and Technology (MoST) in Taiwan. This material is based upon work supported by  NSF 's  LIGO Laboratory  which is a major facility fully funded by the  National Science Foundation.
Origin of the bark beetle module 155 Although mortality from windthrow  (Chen et al. , 2018)  and forest management  (Naudts et al. , 2015; Luyssaert et al. , 2018)  were already accounted for in ORCHIDEE prior to r7791, insect outbreaks and their interaction with other disturbances were not. The LandClim model  (Schumacher et al. , 2004)  approach and more specifically the bark beetle module developed by  Temperli et al. (2013)  were adjusted to develop a bark beetle module in ORCHIDEE r7791. LandClim is a spatially explicit stochastic landscape model in which forest dynamics are simulated at a yearly time 160 step for 10-100 km2 landscapes consisting of 25 × 25 m patches. Within a patch recruitment, growth, mortality and competition among age cohorts of different tree species are simulated with a gap model  (Bugmann, 2001)  in response to monthly mean temperature, climatic drought, and light availability.
In ORCHIDEE, however, the simulation unit is about six orders of magnitude larger, i. e. , 25 km x 25 km. Hence, a single pixel in ORCHIDEE exceeds the size of an entire landscape in LandClim. Where landscape characteristics in LandClim can be represented by a statistical distribution, the same characteristics in ORCHIDEE are summarized in a single value. These differences between LandClim and ORCHIDEE imply that the original bark beetle module 190 cannot be implemented in ORCHIDEE without adjustments. Hence, the original bark beetle module was modified to obtain a pixel-level model that does not account for the spatial information and statistical distribution of landscape characteristics. In the following we will detail the development of the bark beetle outbreak module into ORCHIDEE by following the forest stand stages and bark beetle outbreak stages introduced in Fig.
Simulation experiments 435 Eight locations were selected which represent the range of climatic conditions within the distribution area of spruce in Europe (Picea Abies Karst L. ) as shown in Table  4. Half-hourly weather data from the FLUXNET database  (Pastorello et al. , 2020)  for these locations were used to drive ORCHIDEE. Some of these locations (FON, SOR, HES, COL, WET) are not populated with spruce but all are located within the species distribution. For each location, a pure spruce stand was simulated and the available FLUXNET data was looped to simulate a 100-year period. The  The amount of fresh breeding woody substrate inputs used by the bark beetles to breed was controlled by modifying the maximum wind speed of a windthrow event in ORCHIDEE. Seven wind speeds ranging between 19 m/s and 40 m/s were selected (Table  3 ).
Code availability • R script and data are available at: https://doi. org/10. 5281/zenodo. 8004954 ORCHIDEE rev 7791 code is also available from: https://forge. ipsl. jussieu. fr/orchidee/wiki/GroupActivities/CodeAvalaibilityPublication/ORCHIDEE_gmd-665 2023-05 8. Data availability • The FLUXNET climate forcing data are available at: https://fluxnet. org/ 670 • The simulation results use in this study are available at: https://doi. org/10. 5281/zenodo. 8107315 9.
Scaling relations can also be predicted using numerical simulations (e. g. Kravtsov & Borgani 2012). While predicting the baryonic properties of groups and clusters is hindered by our poor understanding of feedback (for recent progress, see e. g. Eckert et al. 2021; Pop et al. 2022) , the link between the total cluster mass and velocity dispersion of galaxies has been well established  (Carlberg et al. 1997; Mamon et al. 2013). Kirkpatrick et al. (2021)  used the large SPectroscopic IDentification of ERosita Sources (SPIDERS) database  (Clerc et al. 2016; Blanton et al. 2017)  to place cosmological constraints on the modelling of velocity dispersion and its statistical uncertainty. Scaling relations are conventionally characterized by the logarithmic slope, normalization, and intrinsic scatter, for which a log-normal distribution is assumed.
In this paper we extend the spectroscopic database of CODEX and apply established methods for substructure detection to investigate the influence of substructure on scaling relations. Our work extends past efforts to understand cluster richness as a mass proxy, pioneered by  Rozo et al. (2011 Rozo et al. ( , 2014 Rozo et al. ( , 2015)) , to its current widely used definition. An important aspect of our study is the large sample size, which includes nearly a thousand clusters, and access to extensive spectroscopy for the member galaxies. By design, the bright (>0. 4 L * ) red-sequence galaxies of each cluster are sampled at a sufficiently high rate, through multiple pointings using SDSS fibre spectroscopy or multi-object slit spectroscopy from the Nordic Optical Telescope (NOT), extending the completeness of SDSS data from r AB = 17.
2021) , or the galaxy luminosity functions and velocity dispersion functions  (Sohn et al. 2017). These observational strategies are, however, not incompatible, as illustrated by stacking analyses of poorly sampled systems which provide access to averaged, finely detailed kinematics measurements (e. g. Capasso et al. 2019; Mamon et al. 2019). Future large-aperture instrumentation, such as DESI  (Levi et al. 2019 ) and  4MOST (de Jong et al. 2019) , is likely to deliver large numbers of scarcely sampled systems, and analyses of the kind presented in this paper are crucial to preparing for the flow of spectroscopic data analysis from upcoming surveys. Deeper spectroscopic data can be used to spatially identify substructure  (Burgett et al. 2004) , which is beyond the reach of our dataset. This paper is structured as follows: In Sect.
Samples of galaxy clusters and red sequences In this work we use the data collected by the CODEX survey  (Finoguenov et al. 2020 ). The CODEX cluster catalogue is constructed based on the ROSAT All-Sky Survey (RASS) source detection and redMaPPer identification  (Rykoff et al. 2014). redMaPPer provides a catalogue of red-sequence cluster member galaxies, which is publicly released as a targeting catalogue of SDSS 1. redMaPPer searches for red sequences in photometric and sky positional data using X-ray positions as initial putative galaxy cluster centres. For each red sequence, the algorithm determines each galaxy's probability of being an actual member, p mem , based on its colours, position, and magnitude. The sum of these probabilities across a given red sequence measures the cluster's optical richness, λ.
Observations Targeted spectroscopic follow-up of CODEX clusters was performed as a part of SDSS-III  (Eisenstein et al. 2011 ) and continued as a part of the SPIDERS programme of the SDSS-IV survey  (Blanton et al. 2017). To date, spectroscopic redshifts of 66 274 red-sequence galaxies have been measured. The cluster characterization based on these data has been made public through several SDSS releases. In assembling the data for this work, we used SDSS DR14 and DR16 SDSS  (Abolfathi et al. 2018; Ahumada et al. 2020). The redshift measurement uncertainty for typical cluster galaxies targeted in SDSS is a fraction of 10 -4 , corresponding to a velocity uncertainty of 20 km s -1 at z = 0. 2, much below the expected velocity dispersion within a galaxy cluster. We, therefore, neglect this source of uncertainty in what follows.
We supplied r-band magnitudes for the selection of the brightest galaxy. x-axis is their projected distance to the X-ray centre, normalized by R λ. Spectroscopically identified members are displayed at y = 1 and coloured in black (p i = 1), and interlopers are displayed at y = 0 and coloured in white (p i = 0). Galaxies not assigned a spectroscopic redshift are coloured according to their p mem value (p i = p mem ). The dashed red line shows the completeness rate, S (<x) (Eq. (  1 )) within radius x. The dotted line indicates the virial radius estimated from an X-ray luminosity-mass relation. The SCGF code performs a reproducible separation of the projected components, which was manually done for the SPIDERS. It also performs cleaning of spatially isolated galaxies, which are potentially more strongly contaminated by outliers.
4 ; the trend behaves similarly as the entire SPIDERS visually inspected sample (Fig. 1 ). The number of clusters remaining in the SPIDERS and SCGF samples are then 611 and 640, respectively, and 402 clusters are present in both samples. All these clusters have only a single SCGF group component per CODEX cluster remaining after applying the cuts on the number of members. The median number of clean members in the sample is 20 and 95% of the clusters have fewer than 40 members, without any strong richness trend. This makes this sample fairly uniform in terms of the quality of cluster dynamics data. The spectroscopic sampling improves only for rich clusters at z < 0. 1. The two catalogues reveal differences in the membership as well as the values of σ v. Given that SCGF is a fully reproducible methods and is easily extendable to other datasets, we selected it as a primary, using the SPIDERS catalogue to verify the robustness of our conclusions.
2D contours show the 68% and 95% significance levels. Gaussian clusters are marked with solid purple lines and non-Gaussian clusters with dashed black lines. Solid orange lines show the cluster population with a marginal probability of having a substructure (between 85% and 95% confidence level). would expect a higher L X for contaminated systems, contrary to what is observed. In Figs. 19 and 20 we study the redshift evolution of the X-ray luminosity-richness scaling relation. We see a substantial (a factor of 2) reduction of scatter in the scaling relation with increasing redshift. A 2D KS test between the z < 0. 15 and 0. 15 < z ≤ 0. 3 samples yields a p-value of 10 -30. A sharp transition in the behaviour of scatter with redshift, using a consistent and complete dataset, is presented for the first time.
Discussion and conclusions Using the large spectroscopic dataset of the CODEX clusters, we show that the presence of velocity substructure leads to a noticeable increase in scatter in σ v (Fig. 12 ), which, as we show in Fig. 15 , is not associated with richness-dependent deviations from a single power law. The use of different velocity dispersion estimators does not lead to a reduction in scatter. Diversity in the intrinsic scatter in the scaling relations is important for using velocity functions in cosmology (dN/dσ v dV), which currently sets the goal for understanding cluster systematics to a per cent level. On the positive side, the scatter in the velocity dispersionrichness scaling relation is very small (8 ± 2% for rich Gaussian clusters and 12 ± 2% for poor Gaussian ones; Table  2 ).
(2018). An important aspect of our work is that we cover the full range of cluster richness, sampling a great deal of clusters of 20 galaxies, adopted as the low-richness threshold for DES cluster cosmology papers  (Costanzi et al. 2021 ). We provide one of the first consistency checks for scaling relations between low-and high-richness clusters, concluding that, within a single power law description of the dataset, the normalization, slope, and scatter parameters of the model are the same among rich and poor X-ray clusters. If we substitute σ v with total mass, we obtain an M-λ slope of 0. 93 ± 0. 05, which is consistent with the results of our dynamical mass calibration  (Capasso et al. 2019 ). As we are entering a new era of large sample sizes of clusters, the errors on the derived parameters of the scaling relations reveal the intrinsic inhomogeneities of a sample.