dataesr/scientific-paragraphs-categorization huggingface dataset:
M + X-class flares X-class flares CMEs Fast CMEs SPEs Free parameters: None. Limitations and caveats: Each magnetogram is also subject to multiple quality control checks put in place to increase the fidelity of the data analyzed. MagPy utilizes a 45-degree cone filter to discard magnetograms on the limbs of the solar disk. These magnetograms, distorted by effects like foreshortening, engender high degrees of error in free-energy proxy calculations and spawn false neutral-lines. As these types of magnetograms can severely skew the correlation between proxies and event-rates, they are left out of the training dataset. MagPy is most accurate within a 45 degree cone from disk center. Access to model output or forecasts: The model will be available at CCMC for Runs on Request, as well as on the SEP Probability Scoreboard.
The dataset driving MEMPSEP is a multivariate remote and in situ dataset. The main event list is based on the GOES flare list from the Heliospheric Event Knowledgebase (HEK) and contains over 17,000 flare events. For each flare event, remote solar images along with spacecraft measured X-ray, radio, proton, electron, upstream Interplanetary (IP) plasma and magnetic field properties are included, in addition to associated IP coronal mass ejections and IP shock parameters, whenever available. The in situ data comes from multiple instruments onboard different operational missions, including GOES, Advanced Composition Explorer (ACE), and Wind spacecraft. The remote sensing data, also from various instruments, comprises full-disk magnetograms, EUV, and coronagraph images from STEREO, SOHO, and SDO.
The magnetogram from GONG and the observed CME speed (from CDAW catalog and/or DONKI database) are used to calculate the flux rope parameters. This approach offers a relatively simple, and inexpensive model for CME initiation based on empirical features of pre-event conditions  (Gombosi et al. , 2021). The EEGGL module is publicly available for download at http://csem. engin. umich. edu or can be used through CCMC (https://ccmc. gsfc. nasa. gov/eeggl/). In the proposed SEP forecast model, protons are accelerated at the shocks driven by the CMEs through first order Fermi acceleration mechanism (e. g. ,  Axford et al. , 1977; Krymsky, 1977). The original FLAMPA scheme was developed by  Sokolov et al. (2004). In this early version the energetic particle distribution function was solved along a single time-evolving magnetic field line.
25 (HSS = 0. 6579) for I(3 + O2). In turn, this results in a POD = 65. 87% and an FAR = 35. 16% as well as a POD = 77. 78% and an FAR = 40. 96%, respectively. Model description: Support Vector Machine (SVM) is a machine learning model that finds a hyperplane that best divides the dataset into different classes. PHSVM is an SVM model that follows the same idea described in  Boubrahimi et al. (2017) , in order to predict >100 MeV SEP events and non-SEP events. The data used to train PHSVM is composed of GOES X-ray flux and proton flux channels as multivariate time series that led to >100 MeV events, considered as the positive class, and multivariate time series proton data that did not lead to >100 MeV events, which are considered as the negative class. Specifically, PHSVM considers X-ray channel (xl) and proton channel P8 (p8_flux) as a multivariate time series to classify specific events into SEP and non-SEP events.
Inputs Access to model output or forecasts: ASPECS is a webbased tool directly accessible via http://phobos-srv. space. noa. gr/. There are several options for the users: real-time predictions under the forecasting and the nowcasting mode, run-on-demand functionalities that the user can utilize and test the performance of the tool and its modules on archived data. The only required input is a date. The interface then searches the backend database, obtains the relevant inputs and triggers the tool, and displays the obtained outputs. JavaScript Object Notation (JSON) files are produced and thus all outputs are directly reproducible. SAWS-ASPECS is also available on the CCMC SEP Scoreboards. Model validation: Each module has been validated against an independent test sample. For PROSPER and the SEP time profiles the NASA/CCMC SEP challenge events 14  were utilized.
Because relating flare observations (to give the source longitude) with the associated CME is not always trivial, and in some cases, the flare associated with a CME may be behind the limb, for operational SEP prediction at Earth, SEPSTER uses the CME speed and direction (as a proxy for the source location) taken from the CCMC DONKI database 15. When available, these CME parameters are inferred from multi-spacecraft coronagraph observations (e. g. , from STEREO and SOHO); see  Mays et al. (2015)  for further details of the derivation of CME parameters by CCMC. The connection angle is then taken to be the longitudinal difference between the direction of the CME and the magnetic field line footpoint. The footpoint location is obtained by using the solar wind speed measured at the observing spacecraft or other nearby spacecraft, at the time of the CME, if available, or otherwise a default speed of 450 km/s, to map a Parker spiral field line back to the Sun.
Field line mapping in the corona is not considered in this case because SEP acceleration is believed to occur predominantly at heights above the source surface, often assumed to be at 2. 5 R s. In addition, the field line connection longitude derived from WSA-ENLIL, which does include coronal field line mapping, is used to give an estimate of the influence of uncertainty in the connection point on the predicted SEP intensity. SEPSTER enters these variables into Eq. 12, then predicts the peak intensity of 14-24 MeV protons at the observing location. For events before April 2010, or other events for which DONKI CME parameters are not available, flare and CME observations may be used to give the source location and CME speed. The SEPSTER workflow follows the following steps: 1. SEPSTER queries the CCMC DONKI database then copies a list of recent CME parameters to a local file.
(2014) : T peak ðhÞ % 7:32 exp / CA 114 : ð14Þ 15 https://kauai. ccmc. gsfc. nasa. gov/DONKI/. 8. The CME parameters, predicted SEP intensity and peak time, and whether or not the predicted intensity meets the criterion for a GOES event ''all clear," are written to a JSON file. This file is overwritten/updated if the file already exists for a CME with the same time and speed. 9. These steps are repeated for the next CME in the list. 10. The DONKI database is re-checked every minute to see if a new CME has been added, and the above steps are repeated for this CME. 11. For ''historical" events, SEPSTER options include using the CDAW list of CMEs and flares associated with type II radio emission 16 or a user-generated CME-source location list in place of the DONKI CME list in step 1.
SMARP Model Model developers: The model developers are Spiridon Kasapis, Lulu Zhao, Xiantong Wang, Yang Chen and Tamas Gombosi who are members of the Solar Storms and Terrestrial Impacts Center (SOLSTICE) team at the University of Michigan Climate and Space Science Department 18. Monica Bobra from the Hansen Experimental Physics Laboratory in Stanford University provided the Space-Weather MDI Active Region Patches (SMARP) dataset whose predictive capabilities are being explored in this work. Model description: The SMARP Model from Interpretable Machine Learning to Forecast SEP Events for SC23 is a machine learning-based SEP model developed at the University of Michigan. The developers explore how capable two popular groups of ML algorithms (SVM  (Cortes and Vapnik, 1995)  and Regression Models) are in predicting whether an AR which produces flares will lead to a SEP event.
The SVM models use four different kernels: a linear, two polynomial (second and third degree) and the Gaussian Radial Basis Function (RBF) kernel which has been used in similar studies  (Inceoglu et al. , 2018). In addition to the above, two Regression models are trained: the Ridge and the Logistic Regression models. Based on the magnetograms measured by the MDI  (Scherrer et al. , 1995)  onboard SOHO,  Bobra et al. (2021)  derived a new database called SMARPs, which contains characteristics of the active regions on the solar surface. The developers surveyed the SMARP active regions associated with flares that appear on the solar disk between June 5, 1996 and August 14, 2010, label those that produced SEPs as positive and the rest as negative. Three physical keywords, total unsigned flux, mean gradient of the vertical field, and the logarithm of the total unsigned flux near the polarity inversion line are calculated using the pixels in the active region's magnetogram and are used as inputs to the models.
In addition, the SMARP dataset also contains four spacial features specifying the location of the corresponding AR on the solar surface: the minimum and maximum longitude and latitude. Using these spacial features, two more inputs were calculatedthe angular distance between the AR and the magnetic foot-point of the earth and the size of the active region. In this 14-year time span, 65 Solar Energetic Particle events have information adequate to label the 6,510 flare-AR couples, a label is therefore assigned to each couple: positive if it led to an SEP and negative if it did not. Consequently, the output of the binary prediction models is a 0 (negative) or 1 (positive) label. Inputs: SMARP files and derived values. Outputs: Binary yes/no forecast of SEP occurrence (>10 MeV), probability of SEP occurrence and quantified uncertainty of the prediction model.
Free parameters: Both SVM and Linear models are affected by user-defined constants such as the a (complexity parameter) and C (regularization parameter). An embedded grid search using the testing dataset to avoid overfitting is employed varying each free parameter on a range between 0. 05 to 20. All the results reported in this work use a probability decision threshold pt = 0. 5. Limitations and caveats: The task of SEP prediction using SMARPs is subject to a number of limitations such as data uncertainties, a vastly unbalanced dataset which only includes a limited amount of positive events and the inherent limitation of the SMARP dataset which is that compared to other data products it provides lower resolution time series (96-min cadence). It is important to acknowledge that the SMARP dataset could not be used alone to reliably forecast SEP events, therefore the aim of this study is to explore the prediction capabilities of the SMARP dataset and the quality of its different physical parameters rather than propose an operational forecasting apparatus.
Access to model output or forecasts: Forecasts from the model are not available, however the source code may be accessed at https://github. com/skasapis/SEP_Prediction_ Using_SMARP. Model validation: The mean accuracy we obtain when validating a Third Degree Polynomial SVM trained on the best two SMARP predictors (unsigned flux and active region distance from the magnetic foot-point of the earth) suggests that 72% of the times (AE12% for a single run) algorithm can correctly predict whether a flare will lead or not to an SEP. These are the results when training on a balanced data set, however there is a significant reduction in accuracy when training with a more realistic imbalanced data set. These outcomes show that although the SMARP dataset has some ability to make distinctions between flares that produce SEPs and flares that do not, when put in an operational setting where the testing is performed in a vastly imbalanced set of samples, the forecasting model fails to produce meaningful results.
SOLPENCO and SOLPENCO2 -SOlar Particle ENgineering COde Model developers: SOLPENCO: Angels Aran (main developer, University of Barcelona (UB)), Blai Sanahuja (UB), David Lario (NASA Goddard Space Flight Center). SOLPENCO2: Angels Aran (main developer, UB), Blai Sanahuja (UB), Carla Jacobs (Space Applications Services, Belgium), Stefaan Poedts (KU Leuven). Model description: The original SOLar Particle ENgineering Code (SOLPENCO) was an initial step towards developing an operational tool to rapidly predict proton intensity-time profiles and fluences for gradual SEP events  (Aran et al. , 2006; Aran et al. , 2008). The SOLPENCO predictions are based on database interpolations from synthetic proton intensity-time profiles pre-computed for a range of CME-driven shock scenarios and associated solar-interplanetary conditions.
The SOLPENCO synthetic proton flux database is computed from a physics-based SAP model  (Heras et al. , 1992; Lario et al. , 1998). The conceptual framework of SOL-PENCO is based on the assumption that the proton intensity-time profile of a gradual SEP event is mainly the result of particle acceleration by an IP shock, and the subsequent injection of shock-accelerated particles that takes place at the point on the traveling IP shock front that magnetically connects to the observation location  (Lario, 2005). This point on the shock front has been given the name Connecting-with-OBserver-POINT (cobpoint)  (Heras et al. , 1995). The shock model determines the propagation of an IP shock from the 18 R inner boundary of a 2. 5D (2dimensions, 3 vector components for the flow velocity and magnetic induction) MHD model to 1.
, 2006). The SOLPENCO database of synthetic proton intensity-time profiles contains 448 combined shock-andparticle scenarios  (Aran et al. , 2005b; Aran et al. , 2006). There are eight IP shocks with initial velocities ranging from 750 km s À1 to 1800 km s À1 , which translate to shock transit times at 1. 0 AU that range from roughly 25 h to 100 h  (Aran et al. , 2008). The model calculations were also run for 14 heliolongitudes of the associated parent solar event with respect to the observer, from E75 to W90 for observers at 1. 0 AU. In addition, for each database shock/heliolongitude combination there are four IP particle transport conditions: parallel scattering lengths of k k0 = 0. 2 AU and 0. 8 AU, and simulations with and without a turbulent foreshock region. Furthermore, the output results for the shock-and-particle model scenarios are provided at heliocentric radial distances of 0.
2 AU and 1. 6 AU. For any modeled event, the tool provides the 5 -300 MeV proton differential intensity-time profiles for the eleven energy channels of RDS v2. 0. SOLPENCO2 consists of two main parts: (i) A database of simulated interplanetary shocks and (ii) a database of ten modeled SEP events. These data bases are generated from the physics-based SAP model  (Aran et al. , 2011; Pomoell et al. , 2015). The underlying 2D MHD component of the SAP model used in SOLPENCO2 was significantly updated with respect to the original SOLPENCO version  (Aran et al. , 2006; Aran et al. , 2011; Jacobs and Poedts, 2011; Pomoell et al. , 2015). Its inner boundary is now low in the solar corona at 1. 03 R  (Aran et al. , 2011) , enabling the modeling of shock-accelerated proton spectra to be extended from 64 MeV to 300 MeV.
, 2011; Pomoell et al. , 2015). The functional form and shape parameters of the IP shock were selected to reproduce IP shock transit times and solar wind plasma measurements for various gradual SEP events observed at 1. 0 AU. The 2D MHD equations are solved using the Versatile Advection Code (VAC)  (Toth, 1996). The more reduced grid spacing of the MHD model has led to noticeable improvements in the numerical representation of IP shock propagation with respect to the MHD model used in SOLPENCO. The database of 2D MHD shocks includes the the evolution of the COBPOINT for virtual observers located at different heliolongitudes with respect to the incoming shocks  (Aran et al. , 2011). Specifically, the simulated shocks and cobpoints are computed for: Solar winds extending from 1. 03 R to 1. 6 AU: a slow wind of 365 km s À1 , and a fast wind of 595 km s À1.
The database of intensity-time profiles are generated with the SAP's transport model  (Lario et al. , 1998). For the 10 reference events SOLPENCO2 provides: The particle source at the COBPOINT, Q, which is obtained by means of a Q(VR) relation based on the parameters derived from the modeling of the April 4, 2000 and December 13, 2006 SEP events  (Aran et al. , 2011; Pomoell et al. , 2015). The proton intensity-time profiles for the 11 SEPEM reference energies, for the virtual observers located betwen 0. 2 AU and 1. 6 AU. The profiles are obtained by setting constant proton mean free path values (0. 35 AU or 1. 17 AU for 8. 7 MeV protons) and the presence of a turbulent foreshock as in SOLPENCO  (Aran et al. , 2011). The choice of these transport parameters that leads a better comparison with data is selected.
Therefore, in contrast to SOLPENCO, SOLPENCO2 can include a prediction for the downstream fluence of the synthetic events. The main difference between SOLPENCO and SOL-PENCO2 lies in the purpose of the two tools. SOLPENCO is a stand-alone tool ready to be operated by any user with some knowledge of SEP events; and SOLPENCO2 is designed for providing inputs to the SEPEM interplanetary statistical SEP model. SOLPENCO2 uses scaling factors that are specific for the reference events, in order to match 1. 0 AU data, whereas SOLPENCO used a universal scaling factor for its SEP events database. Both, however, share the same modular approach, which would enable the possibility of developing another tool more similar to the first SOLPENCO, using the simulations available in SOL-PENCO2. The first attempt for this is the inclusion of SOLPENCO2 outputs in SAWS-ASPECS.
The free parameters of the shock propagation models used to describe the initial perturbation: initial speed, width and duration for  (Wu et al. , 1983)  and density  (Aran et al. , 2011) , which are fixed in the databases of the SOLPENCO tools to obtain different shock transit times to the virtual observers. The solar wind model of SOLPENCO2 has also the polytropic index as a free parameter to match observations  (Jacobs and Poedts, 2011). Limitations and caveats: SOLPENCO and SOL-PENCO2 generated databases of SEP time profiles for use in forecasting. The models themselves are not run in real time. The main difficulty for that is to find an appropriate proxy for the initial shock speed. The transport model used assumes an Archimedian IMF, perpendicular diffusion is not included, and transport is simulated only up to the shock arrival to the observer.
However, a comparison between 2D and 3D models show very good agreement  (Steyn et al. , 2020). Access to model output or forecasts: A 1D (i. e. neglecting the cross-field coordinate) version of the model is described in detail by van den Berg et al. (  2020 ) and available at https://github. com/RDStrauss/SEP_propagator. Once the real-time implementation of 2D model is complete, it will be included in the same repository. Interested users can contact the model developers for access to a preliminary version of the model.
, 2013; Dalla et al. , 2013 ). In the test particle simulations particles are injected instantaneously at 2R sun across a spatially extended source region that represents a CME-driven shock in the corona. The input particle spectrum is assumed to be a power law E Àc. Particles are propagated and every time they cross a spherical boundary at 1 AU, their parameters are output and used to generate a synthetic particle spectrum. SPARX is part of the COMESEP Alert System and its implementation within that system is described in detail in Section 3. 6. Running of the 3D test particle code is too time consuming for operational usage, therefore the real-time forecasting tool, SPARX, uses a pre-generated database of outputs of runs with varying proton injection locations. The database contains 30 model runs for each IMF polarity, each run describing a specific 6 Â 6 injection tile at a given central latitude, following the particles for 100 h.
SPRINTS combines the pre-eruptive forecast probabilities of MAG4 with SPRINTS post-eruptive forecast probabilities to provide continuously-updated and coupled forecast probabilities of SPEs as new phenomena and events are observed. It is designed as an extensible forecast application, where new datasets and new models can be integrated into the system's database and forecast modeling processes. The model currently runs automatically and in real-time on an Amazon Web Services GovCloud environment. For its SPE primary forecast model, SPRINTS uses MAG4's X-ray flare forecast probabilities and feeds them into a user-defined ML model. The currently running ML model is the Multi-Layer Perceptron (MLP) model but has options to run other models including random forecasts and SVM. The MLP model is trained to predict SPEs at user-defined proton energy channels (e.
Free parameters (FTE): Seed population spectral shape and normalization, parallel mean free path, perpendicular diffusion. Limitations and caveats: In terms of forecasting, STAT is a computationally intensive physics model that at present does not run in real time. To use STAT in an operational environment, it may be feasible to follow the approach of other physics-based models, such as SPARX (Section 3. 31), and create a database of pre-run historical SEP events that can be accessed to generate a forecast. At the time of writing, CME propagation within STAT is limited to the boundary of the coronal domain at 20-30 R. Once the CME has left this domain, particle acceleration from CME propagation in the heliosphere is not modeled, restricting the simulation to only the first few hours of the SEP event  (Young et al.
Critical measurements In Table  10 , the observations required for input into each model are specified. The bottom row shows the total number of models that use each kind of observation. For models that ingest CME parameters produced by forecasters or extracted from a database, we specify the observations required to produce these parameters. CME parameters are derived using as many coronagraph vantage points as possible, as well as EUV imagery from the SDO or STEREO to view the eruption source region  (Millward et al. , 2013). For models that use output from another model to run, we identify the observations required as input into the first model in the chain. Physics-based models often run on top of MHD solar wind models with propagating CMEs (e. g. WSA-ENLIL + Cone and SEPMOD, MAS + CORHEL and EPREM, AWSOM + EEGGL and MFLAMPA) and these require magnetograms and CME parameters as input.
Some models ingest and report NOAA AR information, which is derived from the SOON 20  by the USAF (Robert Steenburg, NOAA/SWPC forecaster, private communication). H-alpha imagery from SOON is also used to identify solar flare locations. Databases such as the Solar DEMON derive solar flare location from real time EUV imagery from SDO. Other flare databases, such as Hinode/EUV Imaging Spectrometer (EIS) 21  provide flare locations derived from X-ray imagery, which are valuable for model training but are not available as real time inputs. The GOES-R XRS product on the NOAA website 22  provides flare location from the XRS quad diodes  (Machol et al. , 2021). Until recently, this location product was not available in real time. For the models described in this paper, flare locations are derived in real time from H-alpha or EUV imagery, however the GOES XRS locations services may provide an alternative now that they are available.
: capable of producing forecasts for multiple locations in the heliosphere; 3D: produces 3D environmental data and particle info, such as pitch angle distributions. flux and time, end time, and fluence as applicable. There are some time profile models that cannot currently simulate the full duration of the event and for these, only the predictions that are possible to derive from their time profiles are indicated. It is important to consider whether the model outputs together can answer all the above questions or if there are clear gaps in our current capabilities. All 35 models generate predictions for protons in the $10 MeV energy range. This is not surprising as the NOAA definition of an SEP event is >10 MeV proton flux exceeds 10 pfu, and modelers have focused on providing predictions that are consistent with this definition.