dataesr/scientific-paragraphs-categorization huggingface dataset:
The landscape of high-and ultra-high-energy astrophysics has changed in the last decade, largely due to the inflow of data collected by large-scale cosmic-ray, gamma-ray, and neutrino observatories. At the dawn of the multimessenger era, the interpretation of these observations within a consistent framework is important to elucidate the open questions in this field. CRPropa 3. 2 is a Monte Carlo code for simulating the propagation of high-energy particles in the Universe. This version represents a major leap forward, significantly expanding the simulation framework and opening up the possibility for many more astrophysical applications. This includes, among others: efficient simulation of high-energy particles in diffusion-dominated domains, self-consistent and fast modelling of electromagnetic cascades with an extended set of channels for photon production, and studies of cosmic-ray diffusion tensors based on updated coherent and turbulent magnetic-field models.
Motivated by the increasing interest of the community, the next version, CRPropa 3. 0, was released, featuring a modern, modular structure  [32] , which makes it easier to extend the code to other propagation environments, or to use custom input quantities. With the launch of this new version, new features were introduced, such as an interface for Galactic propagation through the so-called lensing, necessary for the comparison of cosmic-ray propagation simulations with cosmic-ray observables including anisotropy. In addition, time-dependence was introduced in order to be able to account for cosmological evolution effects and variable sources. This way, investigations of the contribution of different extragalactic source classes to the composition and to the all-particle spectrum at ultra-high energies could be made (see e.
g. [33] [34] [35] [36] ), and quantitative investigations of the expected anisotropy could be performed  [37, 38]. CRPropa 3. 0 represented a state-of-the-art code for extragalactic cosmic-ray propagation. The next version, CRPropa 3. 1, then went a step further by adding a diffusion module that enables the propagation of particles not by simulating trajectories directly, but by solving diffusion-convection equations. The original method for simulating deterministic trajectories is limited by the resolution of turbulent grids and by the availability of computational time. The method becomes inefficient and inaccurate for propagation within the Galaxy at energies E 10 17 eV. This new module of diffusive propagation made it possible to perform simulations of Galactic cosmic-ray sources and other diffusive environments like the Galactic termination shock  [39] , external galaxies like M51  [40] , and galaxy clusters  [41, 42].
With this addition, CRPropa is the first public tool that handles both ballistic and diffusive propagation within a single framework. This is only possible because, for the diffusive propagation, the transport equation is transformed into stochastic differential equations (SDEs), which is numerically similar to the equations of motion of real particles. In this version, stochastic terms describing the diffusive process were added, with the possibility to realise diffusion tensor configurations in arbitrary background fields. This method has the advantage that particle densities are represented by pseudo-particles, whose trajectories are JCAP09(2022)035 simulated. This makes the transport equation approach fit into the framework of ballistic particle propagation, meaning that all the already existing modules of CRPropa can be used with the diffusion module.
Here we launch the new version of CRPropa, 3. 2, which significantly extends the physics scenarios that can be modelled with the code to address the theoretical challenges in astroparticle physics  [43]. We make several method improvements (section 2. 1) by introducing the Boris push as an alternative algorithm for the numerical solution of the equation of motion, through the introduction of the targeting method for more efficient simulations of isotropic emission scenarios, and by improving the interpolation methods. We further add new magnetic-field models including turbulent and helical fields, as well as different parametrisations of the global magnetic field of the Milky Way, and a component for the central molecular zone. In section 2. 3, the method for ensemble-averaged propagation is presented, as it was extended significantly since its first introduction in CRPropa 3.
1. Changes and additions in the way radiation and particle interactions are handled are summarised in section 2. 4. Here, the description of photopion production via the event generator SOPHIA  [44]  was significantly rearranged by adding a user interface and by rewriting the code such that propagation in arbitrary background field configurations is now possible in a simple way, thanks to the modular structure of CRPropa. Furthermore, some photon production channels have been updated, including photodisintegration, nuclear decay, and elastic scattering, and a native implementation of electromagnetic cascades was introduced. Finally, a module for cosmic-ray acceleration via second-or first-order Fermi mechanism was added. In section 3 we show a few examples that illustrate possible applications of the new features.
New features in CRPropa 3. 2 In this section the new features of CRPropa 3. 2 are described. Methodological improvements are shown in section 2. 1. New magnetic-field models, including turbulent and a regular Galactic field, are listed and explained in section 2. 2. Section 2. 3 summarises the ensembleaveraged transport model and section 2. 4 explains the improved interactions. The last part of this section, 2. 5, deals with the newly-implemented acceleration modules.
However, when simulating charged particles with PropagationCK without considering interactions or other energy losses, CRPropa enforces conservation of energy. Enforcing a constant particle energy leads to an adjustment of the momentum components when using the intrinsically non-energy-conserving Cash-Karp algorithm. The Boris push, on the other hand, keeps the momentum components unchanged due to its intrinsic ability to conserve energy. Figure  1  shows the time evolution of the momentum components of the particles and demonstrates the conservation of these components for the Boris push, for a magnetic field oriented along the z-direction. Interested readers can compare both algorithms in a dedicated example script. 1  In the PropagationCK module, the decrease in the parallel component of the momentum is accompanied by an increase in the perpendicular component to accommodate the error in the energy of the Cash-Karp algorithm and keep the energy constant.
Galactic gas densities CRPropa now includes an interface for Galactic gas distributions. They can be used as hadronic targets for upcoming interaction modules or to model the Galactic source distribution based on the distribution of gas in the Milky Way. The implementation is similar to that of magnetic fields, allowing the combination of two or more models into one, making the addition of user-defined models easier. CRPropa 3. 2 provides several models of the distributions of different states of interstellar hydrogen: atomic (H-I), ionised (H-II), and molecular (H 2 ). All models contain three boolean flags which turn each component on and off. All modules of the Density class give the number density n = n H-I +n H-II +n H 2 (getDensity) and the nucleon number density n nucl = n H-I +n H-II +2n H 2 (getNucleonDensity).
Turbulent magnetic fields Since its inception, CRPropa was mainly used to study high-energy cosmic rays in the intergalactic context. As of version 3. 0, it also provides the technique of cosmic-ray lensing inherited from the PARSEC software  [58]  to take into account deflections in the arrival directions caused by Galactic magnetic fields. However, with the emergence of version 3. 1  [59] , the code has gained traction in the research of Galactic cosmic-ray diffusion  [60] [61] [62]. These studies focus on cosmic rays whose gyroradii, r L , tend to be comparable or smaller than the correlation length, L B , of the turbulent magnetic field, in contrast to the regime of highenergy cosmic rays which propagate through intergalactic turbulent magnetic fields where r L L B. In the latter case, the diffusion coefficients are easier to model (e.
Ensemble-averaged propagation In some situations the single-particle approach -solving the equation of motion for each cosmic-ray individually -is neither useful nor computationally feasible. This is usually the case when the particle transport becomes diffusive, e. g. in the case of Galactic cosmic rays with energies 3 PeV in magnetic fields with an average strength of ∼µG. Here, an averaged description of the complete particle ensemble (or phase-space distribution) is the method of choice. In CRPropa 3. 1 this ensemble-averaged approach was introduced with the module DiffusionSDE, which has now been extended to include advective transport and corresponding adiabatic effects.
Equations 2. 12 and 2. 13 can be seen as the equations of motion of phase-space elements (or pseudo-particles) and they are technically not very different from their real-particle counterparts. Therefore, the SDE ansatz fits perfectly into the CRPropa structure and can be used together with many of the available modules. Example applications of this technique are presented in section 3. 1 and can be found in  [39, 59]. Interpreting the outcome of a simulation based on the module DiffusionSDE needs to be done with care as trajectories of individual pseudo-particles are almost meaningless. The user has to keep in mind that only by averaging over a sufficiently large number of pseudo-particles a reasonable approximation of the transport equation will be obtained. In summary, the ensemble-averaged description -based on the modules DiffusionSDE, AdiabaticCooling, and the AdvectionField class -allows for cosmic-ray simulations that are dominated by diffusive transport within the CRPropa framework.
Radiation and particle interactions In this new version of CRPropa we introduced several features, adding new capabilities to the code and improving the way in which particle interactions are handled. In section 2. 4. 2 we describe the newly-implemented channels for photon production. In section 2. 4. 3 we present the new treatment of electromagnetic processes. Therefore, we can now model the photonuclear and electromagnetic interactions relevant for high-energy particle propagation, as shown in figure  4. Among the technical developments, we describe the implementation of a thinning algorithm for efficient simulations of electromagnetic cascades in section 2. 4. 4, as well as the improvements in the treatment of photopion production, in section 2. 4. 1. Finally, section 2. 4. 5 contains a description of user-defined photon fields.
Improvements in the treatment of photopion production The treatment of photopion production in CRPropa is done with SOPHIA  [44]. A direct CRPropa-SOPHIA interface has been implemented as a feature of the PhotoPionProduction module, for the user to access. In this way, the SOPHIA particle interaction algorithm  [44]  can be directly invoked to simulate the interaction of an arbitrary primary high-energy nucleon with a background photon. Here the primary particle must either be a proton or a neutron, since these are the only particles which SOPHIA accepts. In the case of nuclei, a re-scaling is performed based on the number of nucleons of each species (protons and neutrons), as done in previous versions of CRPropa  [27, 32]. Note that while this approximation is sufficient for the purposes of intergalactic cosmic-ray propagation, for other applications such as interactions in sources it may affect the flux of secondary particles  [90].
In the following, the implementation of the new photon channels from ref. [91]  is summarised. Photodisintegration. Photodisintegration is a nuclear process in which a high-energy cosmic-ray nucleus absorbs a photon from the cosmic background field, enters an excited state, and immediately decays by emitting one or more subatomic particles. CRPropa uses the TALYS 1. 8  [92, 93]  nuclear code to simulate the interaction rates and branching ratios for all isotopes with atomic numbers Z ≤ 26, mass numbers 12 ≤ A ≤ 56 and lifetimes τ > 2 s, as described in  [27, 32]. After the interaction, the remaining nucleus can be in an excited state resulting in the emission of multiple high-energy photons depending on the number of nuclearlevel transitions within the nucleus. To compute the photon emission by photodisintegration, TALYS 1.
Photodisintegration may produce radioactive nuclei that subsequently suffer α-or β ± -decays. The daughter nuclei are then generally left behind in an excited state from which they will decay by emitting one or several gamma rays, depending on their respective nuclear-level scheme. The α-and β ± -decays are implemented as described in  [27] , JCAP09(2022)035 using the nuclear data of the NuDat 2. 6  [94]  database for all isotopes with Z ≤ 26 and A ≤ 56. For the implementation of the γ-decay, the NuDat data of the recorded photon energies and corresponding intensities are used to calculate photon emission probabilities for each discrete photon energy within 124 γ-decay channels. The number of photons emitted by each nucleus can be larger than one, depending on the excitation energy at which the produced nucleus is formed and the specific properties of the nuclear level scheme.
But it can also be smaller than one because not all preceding decays will leave the nucleus in an excited state. Photon emissions caused by other processes listed in the NuDat database, like positron annihilation or X-ray emission in electron shell processes are not considered because they do not apply to the fully ionised cosmic rays considered here. As before, the created are boosted to the observer frame and are considered, for energy conservation. Elastic scattering. Photons from the cosmic background radiation can scatter elastically off cosmic-ray nuclei. In this process, the nuclei transfer part of their energy to the photon, similarly to inverse Compton scattering. The interaction length for this process is calculated for all isotopes with Z ≤ 26, 12 ≤ A ≤ 56 and lifetimes τ > 2 s using the cross-section data simulated with TALYS 1.
Implementation of electromagnetic cascades In the previous version of CRPropa, propagation of electromagnetic particles (γ, e ± ) was performed separately using the stand-alone cascading codes EleCa  [95]  and DINT  [96]. To enable simultaneous electromagnetic and hadronic propagation, interaction modules for the dominant electron and photon processes are implemented and can be added directly to the CRPropa simulation chain. These processes are: pair production (Breit-Wheeler), double pair production, triplet pair production, and inverse Compton scattering. In addition, an interaction module for synchrotron radiation was added to the code. The implementation of the electromagnetic interaction modules is based on the EleCa code as described in  [91] , while several improvements were made, which have an impact on the generated secondary particle spectra.
Introduction of custom photon fields Prior to this work, CRPropa only provided a pre-defined set of photon fields relevant for particle propagation at (inter-)Galactic distances. We now introduce a new handling of photon fields which, next to several internal optimisations, enables the user to add their own custom photon fields to CRPropa. On-the-fly calculations of, e. g. , mean free paths of a particle within a photon field, would be too costly from a computational point of view, so that these quantities are provided as precomputed tabular data files. All these files are automatically downloaded upon installation and are stored in a designated location from where it is accessed by the respective CRPropa module. For reference, all relevant scripts to generate these files are accessible through a separate repository.
In CRPropa 3. 2, all previous implementations of photon fields were replaced by their corresponding class representations. This approach not only allows us to streamline the software architecture of target fields with the magnetic field and matter density classes but also centralises data related to photon fields for much smarter data handling. Furthermore, the introduced changes provide a general platform for the user to customise photon fields. This particularly includes the ex-situ construction of photon field objects in a brick-like way as it is also realised by, e. g. , the magnetic field classes. One newly-implemented class of photon field is the BlackbodyPhotonField. The blackbody photon field is further specialised by CMB, which is now represented by an object (as opposed to the previous CMB enumerator object), inheriting all properties of the parent BlackbodyPhotonField.
Acceleration The new version of CRPropa adds an important ingredient to build complete models of high-energy emission: particle acceleration. It is now possible to simulate the acceleration of particles through their scattering off scattering centres representing, e. g. , irregularities in the magnetic field. This is a generalisation of the implementation used in  [101]. The modules scatter the particles into a random direction in the rest-frame of a scattering centre moving with normalised velocity β = v/c in the laboratory frame after propagating a distance d randomly chosen according to an exponential distribution with mean free path λ. Acceleration is achieved depending on the properties of the movement of the scattering centres via the second-or first-order Fermi mechanism. Acceleration via second-order Fermi mechanism is modelled by assuming isotropic movements of the cosmic rays and scattering centres in the rest frame of the accelerating region.
Galactic propagation In galactic environments the motion of cosmic rays often can be described by diffusion. As shown in section 2. 3, CRPropa solves the transport equation within the DiffusionSDE module. The impact of the magnetic field on the Galactic centre is shown in a sample simulation  4  for Galactic cosmic rays. We take the DiffusionSDE module with the default values for the step size and the ratio = κ ⊥ /κ of perpendicular and parallel diffusion. The energy dependence of the diffusion coefficient is: κ = κ 0 E 4 GeV 1 3. (3. 1) For the magnetic field we only use the JF12Solenoidal  [76]  in the first run without the outer transition and without the turbulent component. In the second run we use the superposition of the JF12Solenoidal field with the inter-cloud component of  [74]  in the CMZField module.
Summary and outlook With the recent advances in astroparticle physics and high-energy astrophysics, it is important to understand the production and propagation of cosmic rays, photons, and neutrinos within a single self-consistent framework. To obtain a global picture of these messengers, advanced computational tools are required, enabling the exploration of the multi-dimensional parameter space relevant for interpreting the available observational data. Here we introduced the new version of the publicly available software for high-energy particle propagation -CRPropa 3. 2. It inherits all features from the previous version and extends its capabilities to tackle several other astrophysical problems. New physics features include improved treatment and implementation of new models of magnetic fields, a native treatment of electromagnetic cascades, updated channels for photon production, modules for first-and second-order Fermi acceleration, support for user-defined photon fields, and Galactic gas density models.
JCAP09(2022)035 In the near future, CRPropa will be extended to become an integrated simulation framework for the production and propagation of (ultra-)high-energy particles from inside astrophysical sources, through intergalactic space and the Galaxy, down to the Solar System, spanning cosmic-ray energies from GeV up to ZeV. The code will be able to handle all particles ranging from photons and neutrinos up to the heaviest nuclei in a consistent way. Until then, the modular structure of CRPropa allows the user to tailor it to their needs and efficiently build the extensions necessary for their specific applications. These developments can now also be shared via plugins for the benefit of the community. Information on how to download the code, an online documentation, and examples of applications (including some of those presented in this work) can be found in https://crpropa.
Acknowledgments We thank  Thomas Fitoussi  for implementing the TF17Field magnetic-field model,  Timo Schorlepp  for implementing the JF12FieldSolenoidal model,  Aritra Ghosh  for the first work on first-order Fermi acceleration,  Matthew Weiss  for first work on particle splitting,  Jens Jasche  and  Jörg Rachen  for work on the targeting method,  Alexander Korochnikin  for his contribution to improve the secondary yields of electromagnetic interactions, and  Simone Rossoni  for testing various parts of the software.
Additionally, ~50 TFs assayed in TARGET can now be integrated with a large collection of published TF-target datasets housed in the new ConnecTF software platform & database (https://connectf. org), which enables analyses, refinement, and visualization of extensive gene regulatory networks and their potential physiological relevance  [12]. Aside from its use in fully-sequenced model plants, like Arabidopsis, the TARGET system can potentially also be used in crop species as well as in species where the genome sequence is unavailable or incomplete. Furthermore, the TARGET approach, which uses transient transformation of plant cell protoplasts, may be applicable to species where transgenic approaches to study transcriptional regulation are not feasible. Importantly, the TARGET system can be deployed in a much shorter time-frame, and at higher throughput compared to transgenic plant approaches.
TF). When multiple TFs are examined across multiple days with the same treatments, the inclusion of the control allows the data to be analyzed simultaneously by including a Batch factor (e. g. TF + Batch) in the model, as in Brooks et al. 2019  [10]. 16. We have successfully used 488 nm excitation for RFP detection. The excitation and emission frequencies listed here are not strictly the only ones that can be used. 17. A detailed methodology for setting up a FACS for sorting protoplasts is available  [5]. 18. We generally get about 85 ng of total RNA from 20*10 3 root cells. A Bioanalyzer (Agilent, USA) with a 6000 RNA Pico Kit can be used to accurately assess RNA quantity and quality. 19. Library preparation kits from other manufactures can also be used, but ensure that they are optimized for low RNA input.
Kits designed for 3' RNA sequencing can be used and do not require the purification of mRNA from total RNA (Step 3. 6. 1). 20. If using a kit designed for 3' RNA sequencing, the number of reads required to identify differentially expressed genes will be lower, between 3 and 6 million reads per sample, and allows pooling of more samples per run. 21. Standard quality control should be performed on each step in the bioinformatics analysis, beginning with the raw sequence data using a program such as MultiQC  [22]. It should be noted that sequencing results from TARGET experiments often contain a large percentage of duplicate reads. A. Sterilized seeds are plated on top of 100 µm nylon mesh in a 10 x 10 cm square plate with solidified plant growth medium. The plates are placed vertically in a growth chamber.