11institutetext: LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne
Université, Univ. Paris Diderot, Sorbonne Paris Cité, Meudon, France
11email<EMAIL_ADDRESS>22institutetext: DIO, Observatoire de
Paris, Université PSL, CNRS, Paris, France
22email<EMAIL_ADDRESS>33institutetext: Space Research Centre,
Polish Academy of Sciences, Warsaw, Poland
33email<EMAIL_ADDRESS>44institutetext: GEOPS, CNRS, Université Paris-
Sud, Université Paris Saclay, Orsay, France
44email<EMAIL_ADDRESS>55institutetext: LPC2E, Université d’Orléans,
CNRS, Orléans, France
55email<EMAIL_ADDRESS>66institutetext: IMCCE, Observatoire de
Paris, Université PSL, CNRS, France
66email<EMAIL_ADDRESS>77institutetext: IRAP, CNRS, Université
Paul Sabatier, Toulouse, France
77email<EMAIL_ADDRESS>
77email<EMAIL_ADDRESS>
# VOEvent for Solar and Planetary Sciences

B. Cecconi 11 P. Le Sidaner 22 L. Tomasik 33 C. Marmo 44 M. B. Garnung 55 J.
Vaubaillon 66 N. André 77 M. Gangloff 77

###### Abstract

With its Planetary Space Weather Service (PSWS), the Europlanet-2020-RI
Research Infrastructure (EPN2020RI) project is proposing a compelling set of
databases and tools to that provides Space Weather forecasting throughout the
Solar System. We present here the selected event transfer system (VOEvent). We
describe the user requirements, develop the way to implement event alerts, and
chain those to the 1) planetary event and 2) planetary space weather
predictions. The service of alerts is developed with the objective to
facilitate discovery or prediction announcements within the PSWS user
community in order to watch or warn against specific events. The ultimate
objective is to set up dedicated amateur and/or professional observation
campaigns, diffuse contextual information for science data analysis, and
enable safety operations of planet-orbiting spacecraft against the risks of
impacts from meteors or solar wind disturbances.

###### keywords:

Planets – Space Weather – Virtual Observatory – Software

##  1 Introduction

The PSWS (Planetary Space Weather Service) (André et al., 2018) Joint Research
Activities (JRA) will set up the infrastructure necessary to transition to a
full planetary space weather service within the lifetime of the project. A
variety of tools (in the form of web applications, standalone software, or
numerical models in various degrees of implementation) are available for
tracing propagation of planetary or solar events through the Solar System and
modeling the response of the planetary environment (surfaces, atmospheres,
ionospheres, and magnetospheres) to those events. As these tools were usually
not originally designed for planetary event prediction or space weather
applications, additional development is required for these purposes. The
overall objectives of PSWS will be to review, test, improve and adapt methods
and tools available within the partner institutes in order to make prototype
planetary event/diary and space weather services operational through PSWS
Virtual Access (VA) at the end of the program. One of the goals is: _To
identify user requirements, develop a methodology for issuing event alerts,
and link those to the planetary event and space weather predictions._ This is
the scope of this paper. We first present selected science cases that
demonstrate the need for the proposed system. The VOEvent infrastructure is
then described, followed with the way we implement it for solar system wide
space weather.

##  2 Science Cases

The forecasting planetary and space weather events is initiated by
observations. The observational events can be used as such, or used as inputs
for prediction or modeling tools to predict potential subsequent effects.

Planetary meteor impacts have been reported by several teams (including
amateurs) in the last decade: First shooting star seen from Mars (Selsis et
al., 2005); amateur astronomer see Perseid hits on the Moon (Phillips, 2008);
Fiercest meteor shower on record to hit Mars via comet Grossman (2013);
Explosion on Jupiter spotted by amateur astronomers (Malik, 2012). The events
are often reported in the news or on amateur online forums. Those transient
events are useful for studying the properties of the impacted region. Quick
and efficient transmission of them is thus a key step. This methodology is
used in astronomy with the Gamma-Ray Bursts alert system (Barthelmy, 2008).

Several studies have been published (Prangé et al., 2004; Lamy et al., 2012)
presenting the observed effects of interplanetary shocks while the hit various
planets throughout the solar system, from the Sun to Jupiter, Saturn or even
Uranus. Figure 1 shows an interplanetary shock triggered by three coronal mass
ejections (CME) in September 2011. The shock has been observed at Earth a few
days later (with in situ measurement on the WIND spacecraft, as well as in the
auroral power monitored by NOAA). It also triggered intense decametric radio
emissions at Jupiter three weeks later, that were observed by the
STEREO-A/Waves instrument. It finally hit Uranus after a two months journey in
the interplanetary medium, with the activation of Uranus atmospheric aurora.
The planning of Uranus’ aurorae has been prepared using the 1D MHD mSWiM model
(Zieger and Hansen, 2008) developed at University of Michigan. This code also
confirmed the Jovian radio detection link with the studied event. The major
outcome of this study is the first observation of the faint Uranus’ aurorae
from Earth orbit, and this was only possible thanks to the propagation model.

Several heliospheric propagation models (see, e.g., Tao et al., 2005a; Zieger
and Hansen, 2008) have been recently developed and provide the space physics
community with time of arrivals of interplanetary shocks, or high energy
particle beam at planets or spacecraft in the solar system. Online tools and
repositories are providing access to heliospheric simulation runs. The
Coordinated Community Modeling Center (CCMC111CCMC. http://ccmc.gsfc.nasa.gov)
is a run on demand service center with several propagation models available.
Users can also use simulation runs that were previously computed. The French
Plasma Physics Data Centre (CDPP222CDPP. http://www.cdpp.eu) is providing
precomputed simulation runs in his AMDA (Automated Multi Dataset Analysis)
tool333CDPP-AMDA. http://amda.cdpp.eu as time series of predicted Solar Wind
parameters at the place of the various spacecraft and planets. It also
proposes a Propagation Tool444CDPP Propagation Tool.
http://propagationtool.cdpp.eu that uses both simulation and observational
products to derive time of arrivals of Solar Wind events at the place of the
various spacecraft and planets (see Figure 2).

Figure 1: Following an interplanetary shock throughout the solar system, from
the Sun to Uranus. Figure extracted from Lamy et al. (2012) Figure 2: CDPP
Propagation Tool

Many science teams and space missions could have taken advantage of such
predictions in the recent years (Smart 1, Rosetta, MEx, MAVEN, VEx, HST, MSL,
Dawn), and would take advantage of those within the next five years (Exomars,
Juno, HST/JWST, Parker Solar Probe, Solar Orbiter) and on a longer term (Bepi-
Colombo, JUICE). This list of missions will be used to prioritize the event
catalogs, tools or models that will be implemented in the PSWS alert system.

##  3 VOEvent

VOEvent Allan et al. (2016) is a standardized language used to report
observations of astronomical events; it was officially adopted in 2006 by the
International Virtual Observatory Alliance (IVOA). Though most VOEvent
messages currently issued are related to supernovae, gravitational
microlensing, and gamma-ray bursts, they are intended to be general enough to
describe all types of observations of astronomical events, including
gravitational wave events.

The VOEvent system is already used or planned to be used by several large-
scale projects: the Gamma-Ray Coordinate System (GCN) Barthelmy (2008); the
Large Synoptic Survey Telescope (LSST) Kantor (2014); the European Low
Frequency Array (LOFAR) van Haarlem et al. (2013); or the Solar Dynamic
Observatory (SDO) Somani et al. (2010). That last project has scopes included
in PSWS goal. In each of those projects, VOEvent is used for fast transmission
of transient observations. In PSWS, we plan to use VOEvent for both
observations and predictions.

Messages are written in XML, providing a structured metadata description of
both the observations and the inferences derived from those observations.
VOEvent messages are designed to be compact and quickly transmittable over the
internet via the VOEvent Transfert Protocol (VTP). The version of the VOEvent
standard is 2.0, at the time of writing.

As shown on Figure 3, there are three types of nodes: _Author_ , _Broker_ and
_Subscriber_. The _Authors_ are issuing VOEvents. The _Brokers_ are
dispatching the VOEvents received from _Authors_ to _Subscribers_.
_Subscribers_ are receiving VOEvents from _Brokers_. The large-scale network
is composed of a series of _Brokers_ that are also _Subscribers_ of other
_Brokers_. The _Authors_ must assign a unique IVOA identifier Demleitner et
al. (2015) to each issued VOEvent. The _Subscribers_ are configured to only
take into account VOEvents with new identifiers. In order to update an event
(e.g., update the predicted time of an event, after improved processing), a
new VOEvent must be issued as an update of a previous VOEvent with reference
to the previous VOEvent identifier. This system ensures consistency and avoids
conflicting messages.

A VOEvent message contains the following tags: <who> Describing who is
responsible (the author and the publisher) for the information contained in
the message; <what> the data (such as source flux) associated with the
observations of the event; <wherewhen> description of the time and place where
the event was recorded. This draws from the Space-time Coordinate (STC)
recommendation to the IVOA; <how> a description of the instrumental setup on
where the data were obtained; <why> inferences about the nature of the event;
<description> a description of the event content. A well-formed VOEvent
message must validate against the VOEvent XML schema. A valid message may omit
most of the informational tags listed above, but since the creation of VOEvent
messages is done automatically, most opt to transmit the fullest content
available.

##  4 Adjusting VOEvent for Heliophysics and Planetary Sciences

The main difference between astronomical and Solar System events is the
reference frames in use. Sky coordinates (Right Ascension and Declination) are
the obvious choice for astronomy, and they have been implemented in the
VOEvent model. The <wherewhen> part of VOEvent is currently not fully usable
for the variety of reference frames required for Solar System events.

The current assessment of Solar System requirements for the evolution of the
<wherewhen> section revealed the following new needs: (a) Capability to
specify the only target name as a location. It can be a planet, a satellite, a
comet, a moon, a spacecraft, a rover, etc. Standard names should be used here,
such as IAU names for natural bodies. (b) Capability to specify the location
using coordinate frame on a body, or in a reference frame related to a object
in the Solar System. (c) Capability to specify a time range, with a start and
end time. The list of reference frames (including its time scale, its
coordinate system and its spatial origin) will be compiled by the VESPA and
PSWS teams.

When dealing with predictions, the usage of the <how> and <why> section is
proposed as follows: the <how> part covers tool or model used to derive the
prediction; while <why> concerns the original event or observation used as
input of how to derive prediction, as well as the possible threshold met on
predicted parameters for issuing the event.

Modifying the VOEvent data model (and XML schema implementation) to handle
target names and target types as well as any Solar System coordinate systems
is a task that requires coordination with several working groups of the IVOA.
The IVOA Solar System Interest Group (SSIG) is in charge on including Solar
System needs into IVOA models and will propose evolution of the standards.
Before this is fully implemented, Solar System events specific parameters are
handled inside the <What> part of VOEvent using the <group> capability of the
VOEvent XML schema. This is also the current implementation option selected by
high energy astrophysics and gravitational wave events, which do not fit
perfectly in the VOEvent model either. We propose here a specific
implementation solution to define Solar System parameters into VOEvents using
the current Schema version 2.0.

##  5 Europlanet PSWS and VESPA infrastructure for VOEvent

Figure 3: PSWS VOEvent Network Architecture (adapted from VOEvent
documentation available at
https://voevent.readthedocs.io/en/latest/receiving.html)

The PSWS team has build a redundant broker using Paris Astronomical Data
Centre (PADC) and IRAP/CDPP facilities. The broker is implemented using the
_Comet_ open-source server available from https://github.com/jdswinbank/Comet
Swinbank (2014). The author nodes can also be implemented with _Comet_ to
submit VOEvents. On the client side, a lighter listener using the _pygcn_
package (available from https://github.com/lpsinger/pygcn) is used to listen
to a specific broker.

As VOEvent is a message sent once and with no persistent trace, we use the
VESPA infrastructure to store event metadata together with a link to the
original VOEvent XML file. Each event will be referred to with its ivorn
identifier, which must be unique. Each VOEvent author source have a dedicated
EPNcore metadata catalogue table reachable with TAP (Table Access Protocol),
following VESPA (Virtual European Solar and Planetary Access) recommendation
Érard and et al (2016).

This allows to make data mining in previously posted VOEvents (observations or
predictions), e.g., look for meteor shower at Mars during the full 2020 year.
The corresponding ADQL Ortiz et al. (2008) query would be:

    SELECT * FROM meteor_shower.epn_core WHERE shower_year = ’2000’ AND
    target_name = ’Mars’

and should sent to the TAP service access point (see Table 1). The EPNcore
metadata catalogue table contains the standard EPNcore keywords as well as
extra parameters related to the VOEvent specificities. In the example shown
above, the target_name keyword is part of the EPNcore standard, whereas
shower_year is specific to the meteor_shower PSWS service.

##  6 Example Implementations

We present here four example implementations of various readiness levels
showcasing the variety of topics and needs for the planetary space weather
events. It is noticeable, that two of the examples are concerning Earth
related observations or predictions, but the teams are also considering this
infrastructure for broadcasting events.

Service Title | TAP server Access URL | schema name
---|---|---
Meteor Shower on Planets | http://voparis-tap-voevent.obspm.fr/tap | meteor_shower
Space-Weather Forecast | http://vespa.cbk.waw.pl/tap | rwcalerts
Table 1: Examples of prototype VOEvent EPN-TAP services

###  6.1 Meteor Showers on Planets

The Meteor Shower Prediction code is using the IMCCE celestial mechanics
models to predict the occurrence of meteor showers at various planets. The
ephemeris are produced by simulating the ejection of meteoroid from the sunlit
hemisphere of cometary nuclei, typically from 0 to 3 AU, followed by the
propagation of orbits of meteoroids in the Solar System, taking into account
the gravitation of the Sun, the 8 planets, Pluto and the Moon, as well as the
radiation pressure and the Poynting-Robertson drag. The showers are predicted
when a planet enters a large enough number of meteoroids, at a distance less
than typically 0.01 AU Vaubaillon et al. (2005a, b).

The Meteor Shower alerts contains information on the shower: its source (a
named comet), its target (a planet) and various shower parameters, including
the direction of arrival (in ICRS RA Dec coordinates) of the meteoroid stream
as seen from the center of the target body. The prediction also provides the
user with temporal information as well as a confidence index parameter
Vaubaillon (2017).

Appendix A.1 shows an example of a VOEvent derived from the simulation. The
<why> section is filled with references to the Unified Astronomy Thesaurus
Accomazzi et al. (2014). An alternative implementation is also presented in
Appendix A.1 , using the <WhereWhen> entity.

###  6.2 Space-Weather Forecast

The International Space Environment Services (ISES) is a space weather service
organization currently comprised of 16 Regional Warning Centers, which main
objective is to provide services to the scientific and user communities within
their own regions. These services usually consist of forecasts or warnings of
disturbances to the solar terrestrial environment.

Polish RWC Warsaw meets this task through the web based communication channels
like http and email protocols based on traditional codes and human readable
text messages, created by the Heliogeophysical Prediction Service Laboratory.
The data processing system (Helgeo2) is responsible for gathering data,
performing simulation and preparation of first/provisional forecast that will
be almost instantly manually reviewed by an Helgeo2 operator and published to
the users.

Users focused on system automatization (scientific and commercial, such as
single LOFAR stations, ionosonde, low orbit satellite and power grid
operators) requires data inputs easy to process by the IT system like well
formatted XML structure in VOEvent, therefore the daily alerts with forecast
of solar and geomagnetic activity have been translated.

The RWC Warsaw VOEvent daily alerts contain Solar activity and geomagnetic
activity forecasts included in separated <what> groups. The Solar activity
forecast provides a predicted level of activity for the Sun with a scale of 5
flags (very low, low, moderate, high, very high), and a probability of Solar
flares in percent for C, M, X and P flare classes. The geomagnetic activity
forecast provides a predicted KP index. The <why> section contains the
rationale and observations that led to the forecast. In this section, the
ESPAS Belehaki et al. (2016) ontology is used for the concept definitions.

The database of RWC Warsaw VOEvent is available through the web page
http://helgeossa.cbk.waw.pl/DATA/rwc/alerts/ and by EPN-TAP service (see Table
1). An example of VOEvent file is displayed in Appendix A.2.

###  6.3 Prediction of Solar Wind Parameters

This prediction tool is developed by the IRAP/CDPP team. The model is based on
1D-MHD propagation code developed by Tao et al. (2005b). The code in using the
in-situ measurements of Solar Wind parameters in the Earth vicinity, as
available from the OMNI website (http://nssdc.gsfc.nasa.gov/omniweb/). The
predicted Solar Wind parameter profiles are available through the HelioPropa
tool developed by the CDPP (http://heliopropa.irap.omp.eu, see Fig., 4), and
events can be issued when a certain threshold is passed on predefined
quantities.

For the prototype available at the time of writing, users have to register and
setup the threshold value and the tested parameter in order to receive alerts.
The alerts are sent by email in a VOTable file Ochsenbein et al. (2013). The
service is currently available here: http://alerts-psws.irap.omp.eu/tao.

Figure 4: Screenshot of the HelioPropa tool developed by CDPP.

Appendix A.3 shows a VOEvent based on predefined thresholds selected by the
CDPP science team, containing the same information as the current VOTable
output. This VOEvent example makes use of the <table> element to define list
of values.

###  6.4 Optical Detection of Meteors and Transient Luminous Events

Optical counterparts of atmospheric transient events are subject of
observation and investigation of a growing community. Professional as well as
amateur observation networks are established in Europe and abroad to collect
data important not only for the scientific investigation of some physical
processes but also to assess their possible impacts on Earth’s environment. We
propose here an implementation of the VOEvent standard to the domain of
meteors and Transient Luminous Events (TLEs). A well established standard for
real-time alert on those domains will facilitate coordination between networks
and simplify the extraction of critical information. This would result in
improved collaboration between the amateur community and agencies, e.g., the
ESA Fireball Database555http://neo.ssa.esa.int/search-for-fireballs, or the
CNES TARANIS (Tool for the Analysis of RAdiations from lightNIngs and Sprites)
mission Blanc et al. (2007).

Transient Luminous Events (TLEs) Pasko et al. (2012) are large-scale optical
events occurring in the upper-atmosphere from the top of thunderclouds up to
the ionosphere. They are sometimes accompanied by terrestrial gamma-ray
flashes (TGFs). TLEs may have important effects in local, regional, and global
scales of the atmosphere, but many features of TLEs are not fully understood
yet. TARANIS is a CNES satellite project dedicated to the study of impulsive
transfers of energy between the Earth atmosphere and the space environment.
The TARANIS microsatellite will fly over thousands of TLEs and TGFs for at
least two years. Its scientific instruments will be capable of detecting these
events and recording their luminous and radiative signatures, as well as the
electromagnetic perturbations they set off in Earth’s upper atmosphere.
Coupling TLEs observation to the already existent meteor detection networks,
will allow the observation of TLEs over unprecedented space and time scales
Garnung and Celestin (2017), strongly increasing the probability of joint
detection and hence the scientific return of space missions such as TARANIS or
the ESA ASIM (Atmosphere-Space Interactions Monitor) instrument onboard the
ISS (International Space Station) Neubert (2009).

Several camera networks already exist in Europe and around the world, aiming
to detect and triangulate shooting stars, compute the trajectory of the
possible meteorite and constrain the orbital properties of the meteoroid.
Professional and amateur networks (see among others Oberst et al. (1998);
Jouin et al. (2013); Colas et al. (2015); Gardiol et al. (2016)) working
together will allow Europe to be completely independent in obtaining awareness
about Earth space environment and existing risks connected to atmospheric
reentries. European Space Situational Awareness national programs would
benefit from having a common and standard framework for sharing information on
meteor and fireball detections, and their contribution to the ESA Fireball
Information System would become more efficient. A Virtual Meteor Observatory
initiative is already existing in a European context Koschny et al. (2008);
Barentsen et al. (2010), ending up in the adoption of an XML-based
communication format. Its connection to VOEvent standard will guarantee its
sustainability and interoperability in a larger and well documented context.

Appendices A.4 and A.5 show preliminary VOEvent messages for a meteor and a
TLE detection, planned to be implemented in the meteor and TLE detection
FreeTure Audureau et al. (2014) software.

##  7 Discussion and Perspectives

VOEvent is used as a standard in Astronomy for fast advertising new event on
the sky. In this study, we show that Solar System sciences can take advantage
of this infrastructure without any change, even if the VOEvent data model has
not been designed for Solar and planetary sciences. The adjustments of the
VOEvent model for Solar System use case is minimal and should be harmless for
current implementations, as it just adds terms and concept, without changing
those current in use. The Solar System Interest Group of IVOA is working on
implementing these modifications with the Time Domain Interest Group, which is
managing the VOEvent standard.

Several projets have expressed interest in studying and possibly using VOEvent
for their alert system. We mention here the LOFAR4SW design study. The
LOFAR4SW project (funded by H2020 EC framework programme) is currently
conducting a design study for a future LOFAR measurement and processing
pipeline dedicated to Space Weather. This study includes hardware developments
for a Space Weather dedicated measurement backend, as well as all aspects of
the processing down to the data distribution. The team is studying the VOEvent
and EPN-TAP infrastructure proposed by the PSWS and VESPA teams for the future
LOFAR4SW alert system. The current implementation of RWC is proving the
feasibility in this domain.

As a final word, we plan to reach the general public with broadcasting part of
the VOEvent messages through Twitter, using the content of the <Description>
field. This would ensure a wider audience, especially with the amateur
community.

###### Acknowledgements.

The authors are supported by the Europlanet 2020 Research Infrastructure,
which has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 654208.

## References

  * Accomazzi et al. (2014) Accomazzi, A., N. Gray, C. Erdmann, C. Biemesderfer, K. Frey, and J. Soles, 2014.  The Unified Astronomy Thesaurus.  In N. Manset and P. Forshay, eds., Astronomical Data Analysis Software and Systems XXIII, vol. 485, 461.  1403.6656.
  * Allan et al. (2016) Allan, A., R. B. Denny, and J. Swinbank, 2016.  VOEvent Transport Protocol.  _Proposed recommendation_ , IVOA.  URL http://www.ivoa.net/documents/VOEventTransport/index.html.
  * André et al. (2018) André, N., M. Grande, N. Achilleos, M. Barthélémy, M. Bouchemit, et al., 2018.  Virtual Planetary Space Weather Services offered by the Europlanet H2020 Research Infrastructure.  _Planet. Space Sci._ , 150, 50–59.  10.1016/j.pss.2017.04.020.
  * Audureau et al. (2014) Audureau, Y., C. Marmo, S. Bouley, M.-K. Kwon, F. Colas, et al., 2014\.  FreeTure: A Free software to capTure meteors for FRIPON.  In J.-L. Rault and P. Roggemans, eds., Proceedings of the International Meteor Conference, Giron, France, 18-21 September 2014, 39–41.
  * Barentsen et al. (2010) Barentsen, G., R. Arlt, D. Koschny, P. Atreya, J. Flohrer, et al., 2010\.  The VMO file format. I. Reduced camera meteor and orbit data.  _WGN, Journal of the International Meteor Organization_ , 38, 10–24.
  * Barthelmy (2008) Barthelmy, S., 2008.  GCN and VOEvent: A status report.  _Astronomische Nachrichten_ , 329, 340.  10.1002/asna.200710954.
  * Belehaki et al. (2016) Belehaki, A., S. James, M. Hapgood, S. Ventouras, I. Galkin, et al., 2016.  The ESPAS e-infrastructure: Access to data from near-Earth space.  _Advances in Space Research_ , 58(7), 1177–1200.
  * Blanc et al. (2007) Blanc, E., F. Lefeuvre, R. Roussel-Dupré, and J. A. Sauvaud, 2007.  TARANIS: A microsatellite project dedicated to the study of impulsive transfers of energy between the Earth atmosphere, the ionosphere, and the magnetosphere.  _Advances in Space Research_ , 40, 1268–1275.  10.1016/j.asr.2007.06.037.
  * Colas et al. (2015) Colas, F., B. Zanda, S. Bouley, J. Vaubaillon, C. Marmo, et al., 2015\.  FRIPON, the French fireball network.  In European Planetary Science Congress, EPSC2015–800.
  * Demleitner et al. (2015) Demleitner, M., R. Plante, T. Linde, R. Williams, K. Noddle, and the IVOA Registry Working Group, 2015.  IVOA Identifiers.  _Proposed recommendation_ , IVOA.  URL http://www.ivoa.net/documents/IVOAIdentifiers/20150709/index.html.
  * Érard and et al (2016) Érard, S., and et al, 2016.  Virtual European Solar and Planetary Access (VESPA).  _Planet. Space Sci._ , this issue.
  * Gardiol et al. (2016) Gardiol, D., A. Cellino, and M. Di Martino, 2016.  PRISMA, Italian network for meteors and atmospheric studies.  In A. Roggemans and P. Roggemans, eds., International Meteor Conference Egmond, the Netherlands, 2-5 June 2016, 76–79.
  * Garnung and Celestin (2017) Garnung, M. B., and S. J. Celestin, 2017.  Detecting TLEs using a massive all-sky camera network.  _AGU Fall Meeting Abstracts_.
  * Grossman (2013) Grossman, L., 2013.  Fiercest meteor shower on record to hit Mars via comet.  _New Scientist_ , 220(2947), 18.  10.1016/S0262-4079(13)62892-4.
  * Jouin et al. (2013) Jouin, S., T. Gulon, J. Brunet, and A. Leroy, 2013.  Using BOAM to post meteor data from UFOAnalyzer into the Virtual Meteor Observatory.  In M. Gyssens and P. Roggemans, eds., Proceedings of the International Meteor Conference, 31st IMC, La Palma, Canary Islands, Spain, 2012, 125–126.
  * Kantor (2014) Kantor, J., 2014.  Transient Alerts in LSST.  In P. R. Wozniak, M. J. Graham, A. A. Mahabal, and R. Seaman, eds., The Third Hot-wiring the Transient Universe Workshop, 19–26.
  * Koschny et al. (2008) Koschny, D., J. McAuliffe, and G. Barentsen, 2008.  The IMO Virtual Meteor Observatory (VMO): Architectural Design.  _Earth Moon and Planets_ , 102, 247–252.  10.1007/s11038-007-9216-9.
  * Lamy et al. (2012) Lamy, L., R. Prangé, K. C. Hansen, J. T. Clarke, P. ZArka, et al., 2012.  Earth-based detection of Uranus’ aurorae.  _Geophys. Res. Lett._ , 39(7).  10.1029/2012GL051312.
  * Malik (2012) Malik, T., 2012.  Explosion on Jupiter Spotted by Amateur Astronomers.  _Space.com_.  URL http://www.space.com/17534-jupiter-impact-explosion-amateur-astronomers.html.
  * Neubert (2009) Neubert, T., 2009.  ASIM-an Instrument Suite for the International Space Station.  In American Institute of Physics Conference Series, vol. 1118 of _American Institute of Physics Conference Series_ , 8–12.  10.1063/1.3137718.
  * Oberst et al. (1998) Oberst, J., S. Molau, D. Heinlein, C. Gritzner, M. Schindler, P. Spurny, Z. Ceplecha, J. Rendtel, and H. Betlem, 1998.  The ”European Fireball Network”: Current status and future prospects.  _Meteoritics and Planetary Science_ , 33, 49–56.  10.1111/j.1945-5100.1998.tb01606.x.
  * Ochsenbein et al. (2013) Ochsenbein, F., M. Taylor, R. Williams, C. Davenhall, M. Demleitner, et al., 2013.  VOTable Format Definition Version 1.3.  IVOA Recommendation 20 September 2013.
  * Ortiz et al. (2008) Ortiz, I., J. Lusted, P. Dowler, A. Szalay, Y. Shirasaki, et al., 2008.  IVOA Astronomical Data Query Language.  _Recommendation_ , IVOA.  URL http://www.ivoa.net/documents/latest/ADQL.html.
  * Pasko et al. (2012) Pasko, V. P., Y. Yair, and C.-L. Kuo, 2012.  Lightning Related Transient Luminous Events at High Altitude in the Earth’s Atmosphere: Phenomenology, Mechanisms and Effects.  _Space Science Reviews_ , 168(1), 475–516.  10.1007/s11214-011-9813-9, URL https://doi.org/10.1007/s11214-011-9813-9.
  * Phillips (2008) Phillips, J. A., 2008.  Amateur Astronomers See Perseids Hit the Moon.  _Science@NASA_.  URL https://science.nasa.gov/science-news/science-at-nasa/2008/02sep_lunarperseids.
  * Prangé et al. (2004) Prangé, R., L. Pallier, K. Hansen, R. Howard, A. Vourlidas, R. Courtin, and C. Parkinson, 2004.  An interplanetary shock traced by planetary auroral storms from the Sun to Saturn.  _Nature_ , 432, 78–81.  10.1038/nature02986.
  * Selsis et al. (2005) Selsis, F., M. T. Lemmon, J. Vaubaillon, and J. F. Bell, 2005.  Extraterrestrial meteors A martian meteor and its parent comet.  _Nature_ , 435, 581.  10.1038/435581a.
  * Somani et al. (2010) Somani, A., N. Hurlburt, C. J. Schrijver, C. Cheung, S. L. Freeland, et al., 2010\.  SDO Data Access And Analysis.  In AGU Fall Meeting Abstracts.
  * Swinbank (2014) Swinbank, J., 2014.  Comet: A VOEvent Broker.  _Astronomy and Computing_ , 7-8, 12–26.
  * Tao et al. (2005a) Tao, C., R. Kataoka, H. Fukunishi, Y. Takahashi, and T. Yokoyama, 2005a.  Magnetic field variations in the Jovian magnetotail induced by solar wind dynamic pressure enhancements.  _J. Geophys. Res._ , 110(A11), A11,208.  10.1029/2004JA010959.
  * Tao et al. (2005b) Tao, C., R. Kataoka, H. Fukunishi, Y. Takahashi, and T. Yokoyama, 2005b.  Magnetic field variations in the Jovian magnetotail induced by solar wind dynamic pressure enhancements.  _Journal of Geophysical Research: Space Physics_ , 110(A11).  10.1029/2004JA010959.
  * Tao et al. (2005c) Tao, C., R. Kataoka, H. Fukunishi, Y. Takahashi, and T. Yokoyama, 2005c.  Magnetic field variations in the Jovian magnetotail induced by solar wind dynamic pressure enhancements.  _J. Geophys. Res._ , 110(A11), A11,208.
  * van Haarlem et al. (2013) van Haarlem, M. P., M. W. Wise, Gunsing, G. Heald, J. P. McKean, et al., 2013.  LOFAR: The LOw-Frequency ARray.  _Astronomy and Astrophysics_ , 556, A2.
  * Vaubaillon (2017) Vaubaillon, J., 2017.  A confidence index for forecasting of meteor showers.  _Planet. Space Sci._ , 143, 78–82.  10.1016/j.pss.2017.02.003, 1702.01791.
  * Vaubaillon et al. (2005a) Vaubaillon, J., F. Colas, and L. Jorda, 2005a.  A new method to predict meteor showers. I. Description of the model.  _Astron. Astrophys._ , 439, 751–760.  10.1051/0004-6361:20041544.
  * Vaubaillon et al. (2005b) Vaubaillon, J., F. Colas, and L. Jorda, 2005b.  A new method to predict meteor showers. II. Application to the Leonids.  _Astron. Astrophys._ , 439, 761–770.  10.1051/0004-6361:20042626.
  * Zieger and Hansen (2008) Zieger, B., and K. Hansen, 2008.  Statistical validation of a solar wind propagation model from 1 to 10 AU.  _J. Geophys. Res._ , 113(A08107).  10.1029/2008JA013046.

##  Appendix A VOEvent examples

###  A.1 Example for a Meteor Shower alert at Mars

VOEvent example for a Meteor Shower alert at Mars produced by a trail of comet
2006 AR3. The maximum of event is predicted to occur on 2018-09-08T18:25:00.

``

Possible implementation of the <WhereWhen> entity in the Meteor Shower
prediction example. This implementation is currently not valid because the
_astroCoordSystemID_ required here is not in the enumerated list of allowed
values in the VOEvent model

    <?xml version="1.0" encoding="UTF-8"?>
    <voe:VOEvent ivorn="ivo://vodpc.obspm/imcce/meteorshower/event#MAR_AR3_2018_1102_0::v1.0"
        role="prediction" version="2.0"
        xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
        xmlns:voe="http://www.ivoa.net/xml/VOEvent/v2.0"
        xsi:schemaLocation="http://www.ivoa.net/xml/VOEvent/v2.0 http://www.ivoa.net/xml/VOEvent/VOEvent-v2.0.xsd">
        [...]
        <WhereWhen>
            <ObsDataLocation>
                <ObservatoryLocation/>
                <ObservationLocation>
                    <AstroCoordSystem id="UTC-ICRS-MARS_C"/>
                    <AstroCoords>
                        <Time>
                            <TimeInstant>
                                <ISOTime>2018-09-08T18:25:00Z</ISOTime>
                            </TimeInstant>
                        </Time>
                        <Position2D unit="deg">
                            <Value2>
                                <C1>345.422</C1>
                                <C2>-16.112</C2>
                            </Value2>
                            <Error2Radius>0.204</Error2Radius>
                        </Position2D>
                    </AstroCoords>
                </ObservationLocation>
            </ObsDataLocation>
        </WhereWhen>
        [...]
    </voe:VOEvent>

###  A.2 Example for RWC daily forecast

VOEvent example for RWC daily forecast on September 7th 2017.

    <?xml version="1.0" encoding="utf-8"?>
    <voe:VOEvent xmlns:voe="http://www.ivoa.net/xml/VOEvent/v2.0"
       xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
       ivorn="ivo://src.pas/ssa/voe/wa2voe/170906wa/0" role="prediction" version="2.0"
       xsi:schemaLocation="http://www.ivoa.net/xml/VOEvent/v2.0 http://www.ivoa.net/xml/VOEvent/VOEvent-v2.0.xsd">
       <Who>
          <AuthorIVORN>ivo://src.pas/organization</AuthorIVORN>
          <Date>2017-09-06T09:52:31</Date>
       </Who>
       <What>
          <Description>Forecast of Solar-geophysical activity and propagation conditions</Description>
          <Reference uri="http://rwc.cbk.waw.pl/rwc/170906wa.dre"/>
          <Group name="Solar activity forecast">
             <Description>1 day forecasts</Description>
             <Param name="target_name" value="Sun" ucd="meta.id" utype="epn:target_name" dataType="string"/>
             <Param name="time_min" value="2017-09-07T00:00:00" ucd="time.start" dataType="string" utype="epn:time_min"/>
             <Param name="time_max" value="2017-09-08T00:00:00" ucd="time.end" dataType="string" utype="epn:time_max"/>
             <Param name="activity" ucd="meta.code.class" unit="" value="high(4)" dataType="string"/>
             <Param name="flares class C probability" ucd="stat.probability" unit="%" value="100"/>
             <Param name="flares class M probability" ucd="stat.probability" unit="%" value="100"/>
             <Param name="flares class X probability" ucd="stat.probability" unit="%" value="50"/>
             <Param name="flares class p probability" ucd="stat.probability" unit="%" value="95"/>
          </Group>
          <Group name="Magnetic activity forecast">
             <Description>1 day forecasts</Description>
             <Param name="target_name" value="Earth" ucd="meta.id" utype="epn:target_name" dataType="string"/>
             <Param name="time_min" value="2017-09-07T00:00:00" ucd="time.start" dataType="string" utype="epn:time_min"/>
             <Param name="time_max" value="2017-09-08T00:00:00" ucd="time.end" dataType="string" utype="epn:time_max"/>
             <Param name="Kp" ucd="meta.code.class;phys.magfield" unit="" value="5" dataType="string"/>
          </Group>
       </What>
       <WhereWhen/>
       <How>
          <Description>Solar-geophysical prediction service RWC Warsaw - Regional Warning Centre of ISES</Description>
          <Reference uri="http://rwc.cbk.waw.pl"/>
       </How>
       <Why importance="1.0" expires="2017-09-07T00:00:00">
          <Concept>Solar and Geomagnetic Observations</Concept>
          <Inference probability="1.0">
             <Name>Solar Activity</Name>
             <Concept>http://ontology.espas-fp7.eu/phenomenon/SolarActivity</Concept>
             <Description>
                Activity    : moderate(3)
                Flares      : 5 class m, m2.3/1n 17.37-17.51, m3.8 06.33-06.43,
                : m3.2 04.33-05.07, m4.2 01.03-01.11ut, c5.4 10.13-10.25ut,
                : 2 class 1, 16 class 0
             </Description>
          </Inference>
          <Inference probability="1.0">
             <Name>Geomagnetic activity</Name>
             <Concept>http://ontology.espas-fp7.eu/phenomenon/GeomagneticActivity</Concept>
             <Description>
                activity    : low k=2
                storm       : -
             </Description>
          </Inference>
       </Why>
    </voe:VOEvent>

###  A.3 Example for Solar Wind event at Jupiter

VOEvent example for Solar Wind dynamic pressure pulses at Jupiter, as modeled
by the Tao 1D MHD propagation code Tao et al. (2005c), for October 2018

    <?xml version="1.0"?>
    <voe:VOEvent ivorn="ivo://psws.irap/VOEvent/Tao_Jupiter_2018-10-02T17_34_45::v1.0" role="prediction"
       version="2.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
       xmlns:voe="http://www.ivoa.net/xml/VOEvent/v2.0"
       xsi:schemaLocation="http://www.ivoa.net/xml/VOEvent/v2.0 http://www.ivoa.net/xml/VOEvent/VOEvent-v2.0.xsd">
       <Who>
          <AuthorIVORN>ivo://psws</AuthorIVORN>
          <Author>
<EMAIL_ADDRESS>             <contactName>Michel Gangloff</contactName>
          </Author>
          <Date>2018-10-02T17:34:45</Date>
       </Who>
       <What>
          <Description>Time intervals in which dynamic pressure is greater than 0.08 nPa at Jupiter</Description>
          <Param name="event_type" value="Solar Wind dynamic pressure pulse" ucd="meta.id"/>
          <Group name="event">
             <Param name="tested_parameter" value="Dynamic Pressure" ucd="meta.id" dataType="string"/>
             <Param name="threshold" value="0.08" ucd="phys.pressure" unit="nPa" dataType="string"/>
          </Group>
          <Group name="target">
             <Param name="target_name" value="Jupiter" ucd="meta.id" utype="epn:target_name"/>
             <Param name="target_class" value="planet" ucd="meta.id" utype="epn:target_class"/>
             <Param name="target_region" value="Magnetosphere" ucd="meta.id" utype="epn:target_region"/>
          </Group>
          <Table>
             <Description>Time intervals found by the propagation code</Description>
             <Field dataType="string" name="Start Time" ucd="time.begin" utype="epn:time_min">
                <Description>time tag for beginning of interval</Description>
             </Field>
             <Field dataType="string" name="Stop Time" ucd="time.end" utype="epn:time_max">
                <Description>time tag for end of interval</Description>
             </Field>
             <Data>
                <TR>
                   <TD>2018-10-03T10:00:00.000</TD>
                   <TD>2018-10-04T01:00:00.000</TD>
                </TR>
                <TR>
                   <TD>2018-10-04T07:00:00.000</TD>
                   <TD>2018-10-04T11:00:00.000</TD>
                </TR>
                <TR>
                   <TD>2018-10-08T01:00:00.000</TD>
                   <TD>2018-10-08T07:00:00.000</TD>
                </TR>
             </Data>
          </Table>
       </What>
       <WhereWhen/>
       <How>
          <Description>This prediction has been computed by the Heliospheric propagation 1D MHD model for solar wind
             prediction at planets, probes and comets.
          </Description>
          <Reference uri="https://onlinelibrary.wiley.com/doi/10.1029/2004JA010959/abstract"/>
       </How>
       <Why>
          <Inference probability="1.0">
             <Concept>http://astrothesaurus.org/uat/1534</Concept>
             <Name>Solar Wind measured at 1 AU</Name>
             <Reference uri="https://omniweb.gsfc.nasa.gov"/>
          </Inference>
          <Inference>
             <Concept>http://astrothesaurus.org/uat/1966</Concept>
             <Name>MHD simulation of propagated Solar Wind at Jupiter</Name>
             <Reference uri="https://onlinelibrary.wiley.com/doi/10.1029/2004JA010959/abstract"/>
          </Inference>
       </Why>
       <Description>3 Solar Pressure pulses predicted at Jupiter between 2018-10-03 and 2018-10-09</Description>
    </voe:VOEvent>

###  A.4 Example for a meteor detection

VOEvent example for a meteor detection using the meteor and TLE detection
FreeTure Audureau et al. (2014) software.

    <?xml version="1.0" encoding="UTF-8"?>
    <voe:VOEvent xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
        xmlns:voe="http://www.ivoa.net/xml/VOEvent/v2.0"
        xsi:schemaLocation="http://www.ivoa.net/xml/VOEvent/v2.0
        http://www.ivoa.net/xml/VOEvent/VOEvent-v2.0.xsd"
        version="2.0" role="test" ivorn="ivo://io.freeture.myobs/meteor_events#123">
        <Who>
            <AuthorIVORN>ivo://freeture.io.myobs</AuthorIVORN>
            <Date>2018-03-20T00:03:21</Date>
            <Author>
                <title>FreeTure Meteor VOEvent</title>
                <contactName>Pinco Pallino</contactName>
            </Author>
        </Who>
        <What>
            <Group name="source_flux">
                <Param dataType="float" name="ADU_peak_flux" ucd="phot.count" unit="count"
                    value="0.0015">
                    <Description>Instrumental Peak Flux</Description>
                </Param>
                <Param dataType="float" name="cal_peak_mag" ucd="phot.mag" unit="mag" value="10.8">
                    <Description>Calibrated Peak Magnitude</Description>
                </Param>
            </Group>
            <Table name="light_curve">
                <Description>Detection properties</Description>
                <Param name="zero_point" dataType="float" ucd="phot.calib;arith.zp" unit="mag" value="23.6"/>
                <Param name="color_term" dataType="float" ucd="phot.color;arith.diff" unit="mag" value="0.037"/>
                <Param name="optical_center" dataType="float" ucd="pos.cartesian;pos.wcs.crpix" unit="pixel" value="648,440"/>
                <Param name="center_coords" dataType="float" ucd="pos.eq;pos.wcs.crval" unit="deg" value="18.34,44.52"/>
                <Param name="cd_matrix" dataType="float" ucd="pos.angResolution;pos.wcs.cdmatrix" unit="deg/pixel" value="1.17e-1,0,0,1.17e-1"/>
                <Param name="event_duration" dataType="float" ucd="time.duration" unit="s" value="0.04"/>

                <Field name="x" ucd="pos.cartesian.x;meta.main" dataType="float" unit="pixel"/>
                <Field name="y" ucd="pos.cartesian.y;meta.main" dataType="float" unit="pixel"/>
                <Field name="RA" ucd="pos.eq.ra;meta.main" dataType="float" unit="deg"/>
                <Field name="Dec" ucd="pos.eq.dec;meta.main" dataType="float" unit="deg"/>
                <Field name="alt" ucd="pos.az.alt;meta.main" dataType="float" unit="deg"/>
                <Field name="azi" ucd="pos.az.azi;meta.main" dataType="float" unit="deg"/>
                <Field name="Flux" ucd="phot.count;meta.main" dataType="float" unit="count"/>
                <Field name="Mag" ucd="phot.mag;meta.main" dataType="float" unit="mag"/>
                <Field name="Time" ucd="time.epoch;meta.main" dataType="string"/>
                <Field name="FrameId" ucd="meta.id;meta.main" dataType="int"/>
                <Data>
                    <TR>
                        <TD>780.68</TD>
                        <TD>410.27</TD>
                        <TD>18.34</TD>
                        <TD>44.16</TD>
                        <TD>35.45</TD>
                        <TD>223.46</TD>
                        <TD>225.78</TD>
                        <TD>23.15</TD>
                        <TD>2018-03-19T23:55:21.2702</TD>
                        <TD>31</TD>
                    </TR>
                    <TR>
                        <TD>781.68</TD>
                        <TD>410.27</TD>
                        <TD>18.34</TD>
                        <TD>44.16</TD>
                        <TD>35.45</TD>
                        <TD>223.46</TD>
                        <TD>225.78</TD>
                        <TD>23.15</TD>
                        <TD>2018-03-19T23:55:21.2802</TD>
                        <TD>32</TD>
                    </TR>
                    <TR>
                        <TD>782.68</TD>
                        <TD>410.27</TD>
                        <TD>18.34</TD>
                        <TD>44.16</TD>
                        <TD>35.45</TD>
                        <TD>223.46</TD>
                        <TD>225.78</TD>
                        <TD>23.15</TD>
                        <TD>2018-03-19T23:55:21.2902</TD>
                        <TD>33</TD>
                    </TR>
                    <TR>
                        <TD>783.68</TD>
                        <TD>410.27</TD>
                        <TD>18.34</TD>
                        <TD>44.16</TD>
                        <TD>35.45</TD>
                        <TD>223.46</TD>
                        <TD>225.78</TD>
                        <TD>23.15</TD>
                        <TD>2018-03-19T23:55:21.3002</TD>
                        <TD>34</TD>
                    </TR>
                    <TR>
                        <TD>784.68</TD>
                        <TD>410.27</TD>
                        <TD>18.34</TD>
                        <TD>44.16</TD>
                        <TD>35.45</TD>
                        <TD>223.46</TD>
                        <TD>225.78</TD>
                        <TD>23.15</TD>
                        <TD>2018-03-19T23:55:21.3102</TD>
                        <TD>35</TD>
                    </TR>
                </Data>
            </Table>
        </What>
        <WhereWhen>
            <ObsDataLocation>
                <ObservatoryLocation id="myobs">
                    <AstroCoordSystem id="UTC-GEOD-TOPO"/>
                    <AstroCoords coord_system_id="UTC-GEOD-TOPO">
                        <Position3D unit="deg deg m">
                            <Value3>
                                <C1>248.4056</C1>
                                <C2>31.9586</C2>
                                <C3>2158</C3>
                            </Value3>
                        </Position3D>
                    </AstroCoords>
                </ObservatoryLocation>
                <ObservationLocation>
                    <AstroCoordSystem id="UTC-ICRS-TOPO"/>
                    <AstroCoords coord_system_id="UTC-ICRS-TOPO">
                        <Time unit="s">
                            <TimeInstant>
                                <ISOTime>2018-03-19T23:55:21.3657</ISOTime>
                            </TimeInstant>
                        </Time>
                    </AstroCoords>
                </ObservationLocation>
            </ObsDataLocation>
        </WhereWhen>
        <How>
            <Description>FreeTure Temporal Detection Method</Description>
        </How>
        <Why>
            <Concept>process.variation.meteor;atmosphere.entry</Concept>
            <Description>Possible meteor event</Description>
        </Why>
    </voe:VOEvent>

###  A.5 Example for a Trans-Luminous Event detection

VOEvent example for a meteor detection using the meteor and TLE detection
FreeTure Audureau et al. (2014) software.

    <?xml version="1.0" encoding="UTF-8"?>
    <voe:VOEvent xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
        xmlns:voe="http://www.ivoa.net/xml/VOEvent/v2.0"
        xsi:schemaLocation="http://www.ivoa.net/xml/VOEvent/v2.0 http://www.ivoa.net/xml/VOEvent/VOEvent-v2.0.xsd"
        version="2.0" role="test" ivorn="ivo://freeture.io.myobs/tle_events#123">

        <Who>
            <AuthorIVORN>ivo://freeture.io.myobs</AuthorIVORN>
            <Date>2018-03-20T00:03:21</Date>
            <Author>
                <title>FreeTure TLE VOEvent</title>
                <contactName>Matthieu Garnung</contactName>
<EMAIL_ADDRESS>                <contributor>S.Celestin<EMAIL_ADDRESS>            </Author>
        </Who>

        <What>
            <Group name="event parameters">
                <Param dataType="float" name="Median position x" ucd="pos.cartesian.x;meta.main"
                    unit="pixel" value="307.56"/>
                <Param dataType="float" name="Median position y" ucd="pos.cartesian.y;meta.main"
                    unit="pixel" value="258.97"/>
            </Group>
            <Group name="instrument">
                <Param dataType="string" name="camera_model" ucd="meta.id" value="DMK"/>
                <Param dataType="string" name="network" ucd="meta.ref" value="mycameranetwork"/>
                <Param dataType="string" name="sensor_model" ucd="instr.det;meta.id" value="ICX"/>
                <Param dataType="float" name="pixel_size" ucd="instr.pixel" value="3.5e-6" unit="m"/>
                <Param dataType="int" name="frame_size" ucd="instr.param" value="1280,960" unit="px"/>
                <Param dataType="int" name="bit_depth" ucd="instr.param" value="12" unit="bits"/>
                <Param dataType="string" name="color_flag" ucd="instr.para;meta.code" value="False"/>
            </Group>
        </What>

        <WhereWhen>
            <ObsDataLocation>
                <ObservatoryLocation id="myobs">
                    <AstroCoordSystem id="UTC-GEOD-TOPO"/>
                    <AstroCoords coord_system_id="UTC-GEOD-TOPO">
                        <Position3D unit="deg deg m">
                            <Value3>
                                <C1>248.4056</C1>
                                <C2>31.9586</C2>
                                <C3>2158</C3>
                            </Value3>
                        </Position3D>
                    </AstroCoords>
                </ObservatoryLocation>
                <ObservationLocation>
                    <AstroCoordSystem id="UTC-ICRS-TOPO"/>
                    <AstroCoords coord_system_id="UTC-ICRS-TOPO">
                        <Time unit="s">
                            <TimeInstant>
                                <ISOTime>2018-03-19T23:55:21.3657</ISOTime>
                            </TimeInstant>
                        </Time>
                    </AstroCoords>
                </ObservationLocation>
            </ObsDataLocation>
        </WhereWhen>
        <How>
            <Description>DMK camera from mycameranetwork, using ICX sensor.</Description>
        </How>
        <Why>
            <Description>Possible transient luminous event</Description>
        </Why>
    </voe:VOEvent>


# TweetBLM: A Hate Speech Dataset and Analysis of Black_Lives_Matter-related
Microblogs on Twitter

Sumit Kumar
Birla Institute of Technology
Mesra, India
<EMAIL_ADDRESS>
&Raj Ratn Pranesh11footnotemark: 1
Birla Institute of Technology
Mesra, India
<EMAIL_ADDRESS>
equal contribution

###### Abstract

In the past few years, there has been a significant rise in toxic and hateful
content on various social media platforms. Recently Black Lives Matter
movement came into the picture, causing an avalanche of user-generated
responses on the internet. In this paper, we have proposed a Black Lives
Matter-related tweet hate speech dataset- TweetBLM. Our dataset comprises 9165
manually annotated tweets that target the Black Lives Matter movement. We
annotated the tweets into two classes, i.e., “HATE” and “NON-HATE” based on
their content related to racism erupted from the movement for black community.
In this work, we also generated useful statistical insights on our dataset and
performed a systematic analysis of various machine learning models such as
Random Forest, CNN, LSTM, Bi-LSTM, Fasttext, $BERT_{base}$ and $BERT_{large}$
for the classification task on our dataset. Through our work, we aim at
contributing to the substantial efforts of the research community for the
identification and mitigation of hate speech on the internet. The dataset is
publicly available.

##  1 Introduction

The rapid development of social media networks and blogging websites has
surpassed 3.8 billion active users who use text as a primary mode of digital
communication. Researchers have been using Twitter as a pool of evidence for
hate speech Garland et al. (2020) since it has 330 million monthly active
users. It also enables one to identify trends within ethnically diverse and
disadvantaged audiences. A small percentage of users use discriminatory speech
to ridicule and harass particular groups of people based on their gender,
ethnicity, sexual identity, or other attributes that have become an
obstructive byproduct of social media’s development.

The global outbreak of Black Lives Matter protest has resulted in a general
disturbance in the personal and social lives of the people. The disruption has
resulted in an increased level of anxiety, fear, and an outbreak of sturdy
emotions Ahorsu et al. (2020). This has led to bitter incidents across the
world, for instance, acts of verbal and physical abuse, online harassment,
aggression for black communities Ziems et al. (2020). The protest started on
May 26, 2020, the day after an African-American man, was killed during a
police arrest. The Movement peaked on June 6, 2020, and is still undergoing,
Reports show that around half a million people turned out in nearly 550 places
across the United States.

While it has made efforts to educate about racial justice and counter hate via
social media campaigns (e.g. the #BLM campaign), but their success,
effectiveness, and reach remain unclear. Online hate speech has a severe
negative impact on the victims, often deteriorating their mental health and
causing anxiety Saha et al. (2019).

Thus, it is critical to study the prevalence and impact of online hate and
counter hate speech. In this paper, we presented a new hate speech
classification tweet dataset focusing on the Black Life Matter movement. The
proposed dataset is consist of tweets having two labels: hate and non-hate.

Contribution The three key contribution in this paper is:

  * •

In this work we have published a novel hate speech detection dataset 3
comprising over 9165 manually annotated Black Life Matter tweets in two
classes- ‘Hate’ and ‘Non-Hate’. We presented a statistical analysis on the
proposed dataset 3.3.

  * •

We performed a systematic comparative analysis 6 of various deep learning
models for hate detection task on our dataset.

##  2 Related Work

In the past few years, a lot of research has been done in hate speech
detection through natural language processing, which involves analyzing and
exploring Hate speech hidden in textual representations. For instance,
previous works relied on binary classification such as Kwok and Wang (2013),
Djuric et al. (2015) and by Nobata et al. (2016).

The foremost challenge in creating a robust hate speech detection system for
social media is to have a high-quality dataset. For a machine learning model
to understand and generalize on a classification task, it is very important to
have an accurately annotated dataset with minimum noise. Most commonly
available hate speech classification datasets are comprise binary classes
(hate and non-hate) Davidson et al. (2017), Gao and Huang (2017), Ribeiro et
al. (2018), Qian et al. (2019). They collected these datasets from large
social media platforms such as Gab, Twitter, Facebook, Reedit.

Apart from binary classification datasets, researchers have also worked toward
the idea of more fine-grain classification and presented datasets with more
specific classes. For example, Rezvan et al. (2018) focuses on the creation of
a quality tweet dataset related to five types of harassment content like
sexual, racial, appearance, intellectual, and politics. Waseem (2016). Jha and
Mamidi (2017) proposed the datasets with classes belonging to sexist, racist,
benevolent sexism, and hostile sexism.

Multilingual Ousidhoum et al. (2019), Chung et al. (2019) and multimodal Gomez
et al. (2020) hate speech datasets also played a very crucial role in
understanding the diversity and extent of hate speech across different
languages and modality.

Although previously proposed datasets uniquely contributed to the ongoing hate
speech research, much less attention has been given to the detection and
analysis of hate speech focusing on the black people’s community specifically
related to Black Life Matter movement. Therefore, we created TweetBLM to
address this challenge.

##  3 Dataset

This section will explain the generation process and description of the
dataset that we introduced in this paper. We condense the approach for
collecting and Pre-processing the user-generated dataset through the tweets to
come up with a final dataset. We have summarised the features of the dataset
through some examples 1, along with the data annotation schemes and
guidelines.

###  3.1 Data Collection

We crawled Twitter data using the Tweepy111https://www.tweepy.org/ which is a
Python library for accessing Twitter Application Programming Interface
(API222https://developer.twitter.com/en/docs/twitter-api/v1/tweets/search/api-
reference/get-search-tweets), and collected a sample of tweets between 27th
May 2020 and 26th July 2020. The sample comprised an average of 1,025,286
million tweets per day. Out of the 7,346,842 tweets, English-written tweets
were considered for the dataset, and we selected users having over 150
followers for further steps in order to remove the spam tweets written by
bots.

To extract Black Lives Matter movement-related tweets, we build a set of
keywords related to the usage of hashtags in the semantic sentence (e.g.,
#BLM, #BlackLivesMatter) in both lowercase and uppercase. The following
keywords such as #Atlanta protest, #BLM, #ChangeTheSystem,
#JusticeForGeorgeFloyd, #BlueLivesMatter were used to collect hate speech
specific tweets. Hence, the final collected dataset contains 9165 tweets.

Label | Examples
---|---
Hate |  | “Between 50-75, #BlackLivesMatter members surrounded Officer Joseph Mensah’s girlfriend’s house,
---
assaulted and physical”
“#BlackLivesMatter are a bunch of #terrorists, Trump must win #BlueLivesMatte
#All-
LivesMatter @realDonaldTrump”
“#BlackLivesMatter Wake Up you are Pawns Being used by the #Democrats Your
Lives
mean nothing to them! They are insane”
Non Hate |  | “#BlackLivesMatter This should not be a controversial statement. #BLM”
---
“#BlackLivesMatter Trump supporters are seeding violence. This is also
happening in
Seattle, Portland, and the areas”
“#BlackLivesMatter #CorneliusFredericks We Demand Justice For Cornelius
Fredericks”
Table 1: Example tweets from our collected dataset

###  3.2 Data Annotation

We annotated the gathered data based on two categories: Hate and Non-Hate. The
categories were chosen based on the frequency of the occurrence of hate and
non-hate text associated with tweets.

We give a general description of each class below.
Mention of Hateful text: This category of tweets containing information that
is broadly defined as a form of expression that “attacks or diminishes, that
incites violence or hate against groups, based on specific characteristics
such as physical appearance, religion, descent, national or ethnic origin,
sexual orientation, gender identity, or other,and it can occur with different
linguistic styles, even in subtle forms or when humor is used Fortuna and
Nunes (2018)”.

Mention of Non-Hateful text: This category of tweets contains texts that have
information that is neutral and doesn’t follow the above category or doesn’t
harm or demeans the emotion and sentiment of a person, group, community, and
culture.

Five human annotators of socio-linguistic background and proficiency did
annotation of the dataset to detect hate speech related to Black Lives Matter.
In this work, we use 5 annotators to annotate the gathered dataset. The
annotators were males having age between 20-25, out of which 3 are
undergraduate students and 2 are Master’s students. To begin with annotations,
we collected 11029 tweets from the crawling process. Three annotators
annotated each tweet separately. We considered those tweets for our dataset
which have 100% agreement between at least two annotators among the three
annotators. And we decided on the final label by the 100% agreement of the
remaining other two annotators. The tweets were deleted if there is no
agreement between the remaining other 2 annotators. This gathered data is
reliable for performing experiments. Finally, we get 9165 tweets comprising
3084 hate speech and 6081 non-hate speech. The class distribution of the
TweetBLM dataset is given in Table 2.

Class | Number of sentence text
---|---
Hate | 3084
Non-Hate | 6081
Total | 9165
Table 2: Class distribution of TweetBLM datset

###  3.3 Dataset Analysis

In this section, we analyzed our collected dataset in order to generate some
useful insights. We discovered the top 10 most frequent unigrams, bi-grams,
and tri-grams present in the dataset. We also extracted the most frequent
hashtags present in the tweets.

Model | Accuracy | F1 | Precision | Recall
---|---|---|---|---
Random Forest | 0.773 | 0.775 | 0.785 | 0.767
LSTM | 0.762 | 0.767 | 0.775 | 0.759
BiLSTM | 0.775 | 0.789 | 0.855 | 0.733
CNN | 0.794 | 0.796 | 0.802 | 0.791
FastText | 0.827 | 0.829 | 0.833 | 0.825
$BERT_{base}$ | 0.874 | 0.869 | 0.921 | 0.823
$BERT_{large}$ | 0.891 | 0.889 | 0.934 | 0.850
Table 3: Performance score of various models.

#### Data Preprocessing :

Before conducting the analysis and experiments, we preprocessed tweets by
firstly converting them to lowercase representation. We also made the tweets
free from any unnecessary elements such as username, mentions, links,
retweets. We used NLTK333https://www.nltk.org/, a Python module for text
processing that removed the English stop words and performed lemmatization of
tweets.

#### Method :

Scikit-learn’s CountVectorizer444https://scikit-
learn.org/stable/modules/generated/sklearn
.feature_extraction.text.CountVectorizer.html module could convert a
collection of text documents to a vector of term/token counts. Using
CountVectorizer we tokenized the text of the tweet, build a vocabulary of
known words, and extracted features from the text of the tweet. As shown in
the figure 1 and 2, we then extracted and visualised the top 10 most frequent
unigrams (black, matter, blm, life, live, people, police, protest, say, white)
and bi-grams(life matter, black life, live matter, blacklives matter, donald
trump, matter protest, matter blm, black people, matter black) present in our
tweet dataset respectively. We also extracted the top 5 most frequently used
hashtags(blacklivesmatter, blm, metoo, portlandprotests and portlandpolice)
present in the tweets(see figure 3).

##  4 Methodology

In this section, we have sequentially discussed the architecture of various
classification models used in our experiment. We used the Random Forest-based
classification model. CNN and RNN(LSTM and BiLSTM) based deep learning models.
We also used FastText and BERT($BERT_{base}$ and $BERT_{large}$) pretrained
encoder for building classification model.

##  5 Experiments

In this section, we described the experiment setup for various classification
models used in the experiment. We divided the tweets into train and test
dataset. Out of total 9165 tweets, we used 80% of the data for training the
models and rest 20% of the data was used for the validation. The hyper-
parameters were fine-tuned on the validation dataset. For the Random Forest,
we set the n_estimators = 100, max_depth = 5, max_features = ’auto’ and rest
of the parameters were set to default configuration. For CNN and RNN models,
we set the max length(max_length) of input sequence as 120 and used GloVe
embedding Pennington et al. (2014) of size 300 at embedding layer. In CNN, the
convolution layer had filters = 300, kernel_size = 3, stride = 1 with relu
activationNair and Hinton (2010). For RNN models, we used a SpatialDropout(p =
0.2), single LSTM(with dropout = 0.2) and BiLSTM(with dropout = 0.2) layer
were for the LSTM and BiLSTM model, respectively. In both CNN and RNN model, a
fully connected layer classify the tweet into hate and non-hate class. We used
categorical_crossentropy loss with Adam optimizerKingma and Ba (2014). For the
BERT models- $BERT_{base}$ and $BERT_{large}$, the model had the learning rate
of 1e-4 with Adam optimizerKingma and Ba (2014) and
textitcategorical_crossentropy as loss function. For the experiment, the model
was fine-tuned for 5 epochs. The Random Forest, CNN and RNN models were
trained for 25 epochs.

##  6 Results and Discussion

In this section, we have summarised the result obtained in our experiment and
also discussed the performance of various classification models on our
dataset. As seen in the table 3, the $BERT_{base}$ and $BERT_{large}$ was the
best performing models. They could surpass the other models with the highest
accuracy score of 87.45% and 89.13% respectively. The explanation is that
because of the large data used for pretraining of the $BERT_{base}$ and
$BERT_{large}$ models and transfer learning increased their contextual
understanding and models could generalize better. Following the BERT model,
the FastText model could achieve an accuracy of 82.77. With RNN models, BiLSTM
performed better than the LSTM model by achieving higher accuracy of 77.58%
which was 1.37% more than LSTM. The CNN model outperformed both RNN models
with an accuracy score of 79.46%. Random Forest reported an accuracy score of
77.35%, which was a slight improvement of 1.14% over the LSTM model.

##  7 Conclusions

In this paper, we presented the Black Lives Matter dataset: TweetBLM. We
collected 9165 user-generated Black Lives Matter movement-related tweet data
which were manually labeled into two classes. We presented statistical
insights into our dataset by extracting frequent n-grams and hashtags. Using
our dataset, we also performed a comparative analysis of various machine
learning models as an attempt to understand how well these models are able to
generalize and perform classification on our dataset. The experiment results
show that one application of our approach might be the identification and
filtering of hateful textual contents on social media platforms, but there is
still room for improvement. We can do future work such as (i) Expanding our
data for annotating more tweets that would be beneficial for the research
community, (ii) Providing additional features for fine-grained classification.

Figure 1: 10 most frequent unigram Figure 2: 10 most frequent bi-gram Figure
3: 5 most frequent hashtags

## References

  * Ahorsu et al. (2020) Daniel Kwasi Ahorsu, Chung-Ying Lin, Vida Imani, Mohsen Saffari, Mark D Griffiths, and Amir H Pakpour. 2020.  The fear of covid-19 scale: development and initial validation.  _International journal of mental health and addiction_.
  * Chung et al. (2019) Yi-Ling Chung, Elizaveta Kuzmenko, Serra Sinem Tekiroglu, and Marco Guerini. 2019\.  Conan–counter narratives through nichesourcing: a multilingual dataset of responses to fight online hate speech.  _arXiv preprint arXiv:1910.03270_.
  * Davidson et al. (2017) Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017.  Automated hate speech detection and the problem of offensive language.  In _Proceedings of the International AAAI Conference on Web and Social Media_ , volume 11.
  * Djuric et al. (2015) Nemanja Djuric, Jing Zhou, Robin Morris, Mihajlo Grbovic, Vladan Radosavljevic, and Narayan Bhamidipati. 2015.  Hate speech detection with comment embeddings.  In _Proceedings of the 24th international conference on world wide web_ , pages 29–30.
  * Fortuna and Nunes (2018) Paula Fortuna and Sérgio Nunes. 2018.  A survey on automatic detection of hate speech in text.  _ACM Computing Surveys (CSUR)_ , 51(4):1–30.
  * Gao and Huang (2017) Lei Gao and Ruihong Huang. 2017.  Detecting online hate speech using context aware models.  _arXiv preprint arXiv:1710.07395_.
  * Garland et al. (2020) Joshua Garland, Keyan Ghazi-Zahedi, Jean-Gabriel Young, Laurent Hébert-Dufresne, and Mirta Galesic. 2020.  Countering hate on social media: Large scale classification of hate and counter speech.  _arXiv preprint arXiv:2006.01974_.
  * Gomez et al. (2020) Raul Gomez, Jaume Gibert, Lluis Gomez, and Dimosthenis Karatzas. 2020.  Exploring hate speech detection in multimodal publications.  In _Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision_ , pages 1470–1478.
  * Jha and Mamidi (2017) Akshita Jha and Radhika Mamidi. 2017.  When does a compliment become sexist? analysis and classification of ambivalent sexism using twitter data.  In _Proceedings of the second workshop on NLP and computational social science_ , pages 7–16.
  * Kingma and Ba (2014) Diederik P Kingma and Jimmy Ba. 2014.  Adam: A method for stochastic optimization.  _arXiv preprint arXiv:1412.6980_.
  * Kwok and Wang (2013) Irene Kwok and Yuzhou Wang. 2013.  Locate the hate: Detecting tweets against blacks.  In _Twenty-seventh AAAI conference on artificial intelligence_.
  * Nair and Hinton (2010) Vinod Nair and Geoffrey E Hinton. 2010.  Rectified linear units improve restricted boltzmann machines.  In _ICML_.
  * Nobata et al. (2016) Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. 2016\.  Abusive language detection in online user content.  In _Proceedings of the 25th international conference on world wide web_ , pages 145–153.
  * Ousidhoum et al. (2019) Nedjma Ousidhoum, Zizheng Lin, Hongming Zhang, Yangqiu Song, and Dit-Yan Yeung. 2019\.  Multilingual and multi-aspect hate speech analysis.  _arXiv preprint arXiv:1908.11049_.
  * Pennington et al. (2014) Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014.  Glove: Global vectors for word representation.  In _Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)_ , pages 1532–1543.
  * Qian et al. (2019) Jing Qian, Anna Bethke, Yinyin Liu, Elizabeth Belding, and William Yang Wang. 2019\.  A benchmark dataset for learning to intervene in online hate speech.  _arXiv preprint arXiv:1909.04251_.
  * Rezvan et al. (2018) Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie L Shalin, and Amit Sheth. 2018.  A quality type-aware annotated corpus and lexicon for harassment research.  In _Proceedings of the 10th ACM Conference on Web Science_ , pages 33–36.
  * Ribeiro et al. (2018) Manoel Ribeiro, Pedro Calais, Yuri Santos, Virgílio Almeida, and Wagner Meira Jr. 2018.  Characterizing and detecting hateful users on twitter.  In _Proceedings of the International AAAI Conference on Web and Social Media_ , volume 12.
  * Saha et al. (2019) Koustuv Saha, Eshwar Chandrasekharan, and Munmun De Choudhury. 2019.  Prevalence and psychological effects of hateful speech in online college communities.  In _Proceedings of the 10th ACM Conference on Web Science_ , pages 255–264.
  * Waseem (2016) Zeerak Waseem. 2016.  Are you a racist or am i seeing things? annotator influence on hate speech detection on twitter.  In _Proceedings of the first workshop on NLP and computational social science_ , pages 138–142.
  * Ziems et al. (2020) Caleb Ziems, Bing He, Sandeep Soni, and Srijan Kumar. 2020.  Racism is a virus: Anti-asian hate and counterhate in social media during the covid-19 crisis.  _arXiv preprint arXiv:2005.12423_.