# Searching for long faint astronomical high energy transients: a data driven
approach

Riccardo Crupi
DMIF
Università di Udine
Via delle Scienze 206, Udine 33100
<EMAIL_ADDRESS>
&Giuseppe Dilillo
Istituto di Astrofisica e Planetologia Spaziali
INAF
via del Fosso del Cavaliere 100, Roma 00113
<EMAIL_ADDRESS>
Elisabetta Bissaldi
Dipartimento Interateneo di Fisica
Politecnico di Bari
via E. Orabona 4, Bari 70125

Sezione di Bari
Istituto Nazionale di Fisica Nucleare
via E. Orabona 4, Bari 70125
<EMAIL_ADDRESS>
&Fabrizio Fiore
Osservatorio Astronomico di Trieste
INAF
via Tiepolo 11, Trieste 34143
<EMAIL_ADDRESS>
&Andrea Vacchi
DMIF
Università di Udine
Via delle Scienze 206, Udine 33100

Sezione di Trieste
Istituto Nazionale di Fisica Nucleare
via Padriciano 99, Trieste 34149
<EMAIL_ADDRESS>

###### Abstract

HERMES (High Energy Rapid Modular Ensemble of Satellites) pathfinder is an in-
orbit demonstration consisting of a constellation of six 3U nano-satellites
hosting simple but innovative detectors for the monitoring of cosmic high-
energy transients. The main objective of HERMES Pathfinder is to prove that
accurate position of high-energy cosmic transients can be obtained using
miniaturized hardware. The transient position is obtained by studying the
delay time of arrival of the signal to different detectors hosted by nano-
satellites on low Earth orbits. To this purpose, particular attention is
placed on optimizing the time accuracy, with the goal of reaching an overall
accuracy of a fraction of a micro-second. In this context, we need to develop
novel tools to fully exploit the future scientific data output of HERMES
Pathfinder. In this paper, we introduce a new framework to assess the
background count rate of a space-born, high energy detector; a key step
towards the identification of faint astrophysical transients. We employ a
Neural Network (NN) to estimate the background lightcurves on different
timescales. Subsequently, we employ a fast change-point and anomaly detection
technique to isolate observation segments where statistically significant
excesses in the observed count rate relative to the background estimate exist.
We test the new software on archival data from the NASA Fermi Gamma-ray Burst
Monitor (GBM), which has a collecting area and background level of the same
order of magnitude to those of HERMES Pathfinder. The neural network
performances are discussed and analyzed over period of both high and low solar
activity. We were able to confirm events in the Fermi/GBM catalog, both Solar
Flares and Gamma-Ray Bursts (GRBs), and found events, not present in Fermi/GBM
database, that could be attributed to Solar Flares, Terrestrial Gamma-ray
Flashes, GRBs, Galactic X-ray flash. Seven of these are selected and analyzed
further, providing an estimate of localisation and a tentative classification.

##  1 Introduction

Gamma-Ray Bursts (GRBs) originate in extraordinarily energetic explosions
taking place in distant galaxies. They appear as irregular pulses of X and
$\gamma$-ray radiation in detectors of today work-horse satellites such as
SWIFT, INTEGRAL, Fermi, and Agile. The typical distribution of GRBs duration
is bimodal; ‘long bursts’, lasting longer than 2s, are associated with black
hole formation in collapsars, while ‘short bursts’, lasting less than 2s, are
associated to mergers of binary neutron stars Woosley (1993); Berger (2014).

Present instrumentation dedicated to GRBs and cosmic transients has been
launched during the 2010s. There is no guarantee that it will continue to
operate beyond the mid-2020s. For this reason, several proposals to NASA and
ESA have been already submitted to select the successors of these instruments.
The High Energy Rapid Modular Ensemble of Satellites (HERMES) concept is to
develop a constellation of nano-satellites to study high-energy transients
Fiore et al. (2020, 2021), thus providing a fast-track and affordable solution
bridging the gap between current X-ray monitors and the next generation. A
technological and scientific pathfinder (HERMES-TP, funded by ASI and HERMES-
SP funded by the European Commission, HERMES pathfinder hereafter) is in
preparation to prove the concept, that is the capability to detect and
localize GRBs with miniaturized instrumentation hosted by nano-satellites. The
first six HERMES Pathfinder spacecrafts are expected to be launched in low-
Earth, near-equatorial orbit during 2024. A seventh payload unit identical to
those hosted by HERMES Pathfinder is also hosted by the Australian SpiRIT
satellite, to be launched during 2023. The HERMES Pathfinder and SpIRIT
payload is a small yet innovative “siswich" detector providing broad-band
energy coverage (few keV - $1$ MeV) and very good temporal resolution (a few
hundreds ns) Fuschino et al. (2019); Evangelista et al. (2020); Fiore et al.
(2022); Evangelista et al. (2022).

GRBs manifest as transient increases in the count rates of detectors. The
activity of these phenomena appear as unexpected, and not explainable in terms
of background or any other known sources. Any automated procedure for
detecting GRBs is generally concerned with searching the time series of the
observations for statistically significant excesses in photon counts, relative
to a reference background estimate in the absence of $\gamma$/X-ray GRB
related events. The on-orbit physical background observed by GRB monitor
experiments is determined by factors inherent to the highly dynamical near-
Earth radiation environment, to the spacecraft geographic position and
attitude, as well as the spacecraft geometry, and the detector’s pointing,
design and response. Given the difficulty intrinsic to a real-time modelling
of the expected scientific background, algorithms dedicated to the ‘online’
search of GRBs often resort to extrapolate the background from recent
observations. For example, the trigger algorithms running on-board NASA
Fermi/GBM assess a background estimate from an average of the photon count
rate observed over the previous $17$ s excluding the most recent $4$ s of
observations Meegan et al. (2009); similar moving average approaches were used
by Compton-BATSE Paciesas et al. (1999) and BeppoSAX-GRBM Feroci et al.
(1997).

In ‘offline’ analysis, archival data are searched for GRB events that the
online and on-board algorithms may have missed. Examples of this approach can
be found in Kommers (1999), which uses the BATSE catalog, or in Kocevski et
al. (2018) and Hui et al. (2017) where they search for faint, short GRBs at
times compatible with known gravitational wave events. In Biltzinger et al.
(2020) for example, an estimate is assessed starting from detailed models of
the background expected for GBM, such as the detector response, the cosmic
$\gamma$-ray background, the solar activity, the geomagnetic environment, the
Earth albedo and the visibility of X and $\gamma$ point sources. The
background description so achieved has been shown to reproduce very well the
observations of Fermi/GBM and could potentially allow for the identification
of otherwise hard to detect GRBs such as long-weak events with slow raising
times. However, having been specifically tailored for the observations of
Fermi/GBM, this technique is not immediately applicable to other experiments.
In Sadeh (2019) a Recurrent Neural Network (RNN LeCun et al. (2015)) is used
to predict the background and, on top of it, classify or detect anomalies in
the observations of a count-rate detector. To recognize a GRB event, this RNN
is trained onto existing catalogues of burst observations. We believe such an
approach could inherit the detection biases of standard strategies for GRB
detection, ultimately leading to missing events which already defied previous
searches.

In Section 2 we introduce our approach to estimate the scientific background
of a gamma-ray burst monitor experiment using a Neural Network (NN). In
particular, we employ a Feed Forward Neural Network (Bishop et al. (1995)) to
estimate the count-rates expected from background sources over the 12 NaI
detectors of Fermi/GBM, in different energy bands and at regular time
intervals. Our model is designed to learn the dynamics of the background over
a timescale of months, enabling the detection of long-GRBs or even ultralong-
GRBs Gendre et al. (2019), as shown in an example in Section A. Moreover,
employing a robust loss function in the training phase, we are able to deal
with outliers in count-rate observations, such as transients due to
astronomical events or brief period of detector inactivity C. The choice of
applying our framework to archival data from Fermi was motivated by the facts
that (1) the HERMES Pathfinder spacecrafts are expected to be launched in a
low inclination orbit with altitude $500-550$ km, an orbit where the
background and its variations are expected to be smaller than those of
Fermi/GBM Meegan et al. (2009); and (2) the Fermi/GBM and HERMES Pathfinder
detectors both rely onto scintillators and have similar effective areas
Bissaldi et al. (2009); Campana et al. (2020); Dilillo et al. (2022) resulting
in background count-rates of the same order of magnitude. To estimate the
background observed by Fermi/GBM, we leverage on a large ensemble of
information, including features both intrinsic to the satellite and its
orbital setting such as the satellite attitude and geographic location in
time, the Sun visibility and so on. This idea is consistent with Fitzpatrick
et al. (2012), which describes a method that estimates the background at the
period of interest by using rates from adjacent days when the satellite has
similar geographical footprint. To retrieve these information’s we use the
Fermi/GBM Data Tools Goldstein et al. (2021) software package, an Application
Programming Interface (API) allowing to download, analyse and visualise GBM
data. Being completely data-driven, we believe our approach to be in principle
applicable to any GRB monitor experiment for which a similar dataset is
available.

The background estimates produced by the NN are compared with the observations
by mean of an efficient change-point detection technique called FOCuS-Poisson
Ward et al. (2022), aiming at the automatic identification of statistically
significant astrophysical transients. We tested the combination of the NN
background estimates and FOCuS-Poisson trigger on real Fermi/GBM data. We were
able to confirm known events, but we also find events with no counterpart in
the Fermi/GBM trigger
catalog111https://heasarc.gsfc.nasa.gov/W3Browse/fermi/fermigtrig.html (Von
Kienlin et al., 2020), yet with features resembling astronomical transients
such as GRBs and solar flares and other galactic high-energy sources.

The paper is organised as follows. In Section 2 we present the background
estimation in a supervised Machine Learning settings, the architecture of the
NN and the FOCuS-Poisson change-point detection technique. In Section 3 we
describe the data used and the pre-processing steps to build the dataset. In
Section 4 we report the performance of the NN estimator and the result of the
application of the trigger algorithm. A comparison between the background
estimated in a period of solar maxima and in a solar minima is described in
the Appendix C. In Section 5 we discuss the results of our search for
undiscovered astrophysical transients. We identify 110 events with no
counterpart in GBM trigger catalog over a period of about 9 months. We report
on a subset of seven events providing lightcurves, localization and
classification. Finally, in Section 6 we draw our conclusions and discuss
future prospects.

##  2 Methodology

The background assessment problem is expressed as a supervised Machine
Learning estimator, with the variables inherent to the satellite and its
orbital position as inputs and the count-rate observed by each detector in
three different energy bands as outputs. The background estimates so obtained
are compared against the actual observations using FOCuS-Poisson. The
significance of the excess in the count observations relative to the
background model is quantified in units of standard deviations and recorded as
a time series. Finally, these records are searched for intervals in the
observation where the excess significance exceeds a threshold over one or more
detector-energy band combinations.

###  2.1 Background estimation

We define $X$ as the input variables, see $col\\_sat\\_pos$ and
$col\\_det\\_pos$ in Section 3, and $Y$ as the output variables, see
$col\\_range$ in Section 3. We suppose that a function $f(X)$ exists which
predict $Y$ given $X$, that is the solution that minimize $L(f(x),Y)$
($\text{argmin}_{f}L(f(x),Y)$) where $L$ is the loss function that quantify
the error in the predictions. The model’s goal is to estimate a quantity
$F(x)$ such that $f(x)\approx F(x)$ Hastie et al. (2009). Here we are dealing
with a multi-output regression: $F:X\in\mathbb{R}^{k}\longrightarrow
Y\in\mathbb{R}^{m}$, where $k$ is the number of features into the model and
$m$ the number of outputs.
The model employed is a feed forward neural network with 3 hidden dense layers
(Figure 1). Each hidden layer is followed by a batch normalization layer Ioffe
and Szegedy (2015) and a dropout layer Srivastava et al. (2014). The NN is
implemented in Tensorflow Abadi et al. (2015). The input layer has dimension
$k=60$. Each of the first two hidden layers is composed of $2048$ neurons,
while the third hidden layer hosts $1024$ neurons. The last (output) layer has
$m=36$ neurons. Each of the output neurons is associated with a particular
detector-energy combination. The probability parameter for the drouputs is
$0.02$. The optimizer used is Nadam Ruder (2016) with learning rate $\eta$
varying accordingly to Equation 1, $\beta_{1}=0.9$, $\beta_{2}=0.99$ and
$\epsilon=10^{-7}$.

$\eta=\begin{cases}0.01&\text{if epoch $<4$}\\\ 0.0016&\text{if $4\geq$ epoch
$<12$}\\\ 0.0004&\text{if epoch $\geq 12$}\end{cases}$ (1)

We run the fitting for 64 epochs with a batch size of 2048.

Figure 1: The architecture of a feed forward neural network. The input has
dimension 60. The first two hidden layer have 2048 neurons, the third 1024.
The output layer has dimension 36.

In a pre-processing step, the input training dataset is standardised and
filtered. Data filtering takes place in two steps in which the following data
subsets are removed:

  * •

data collected while Fermi is transiting through the high radiation
environment of the South Atlantic Anomaly (SAA).

  * •

data acquired at times in which an event of the Fermi/GBM trigger catalog
occurred.

This latter choice isn’t strictly necessary, yet it is useful to better
understand the neural network performances over known events. The splitting
procedure divides the dataset in 75% train, 25% test; 30% of the training set
is further kept as validation set. The resulting splitting is: 52% train, 23%
validation and 25% test. The instances inside these sets are not sequential
but rather taken randomly.

The purpose of our framework is to evaluate the effectiveness of our model on
a known dataset, hence the choice of a loss function $L$ which is robust
against outliers is critical. The Mean Square Error loss function (MSE) is:

$\text{MSE}(x,y)=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-x_{i}\right)^{2}.$ (2)

MSE is very sensitive to the discrepancy between the prediction and the target
value, thus it is a bad choice when outliers are present in the training
dataset. We remark that the filtering of catalog events is not enough to
guarantee the optimization of the background estimator when using MSE. Indeed,
anomalous events, which are not present in the GBM catalog, may be over-fitted
when minimizing MSE; these events are the actual targets of our search.

The Mean Absolute Error (MAE) loss function is less sensitive to residuals:

$\text{MAE}(x,y)=\frac{1}{n}\sum_{i=1}^{n}\mid y_{i}-x_{i}\mid$ (3)

In respect to MSE, MAE will result in better neural network performances when
anomalous events are included in the training dataset. In the settings of
multi-output regression, the overall loss $\mathcal{L}$ is define as the MAE
average of the NN outputs:

$\mathcal{L}=\frac{1}{m}\sum_{j=1}^{m}(\text{MAE}(F(X)_{j},Y_{j}))$ (4)

###  2.2 Trigger algorithm

An efficient change-point and anomaly detection algorithm called FOCuS-Poisson
(Functional Online CUSUM) Ward et al. (2022) is employed to find anomalous
transients in Fermi CSPEC data relative to the NN estimates of the background.
The FOCuS-Poisson algorithm is executed sequentially over the time series of
the observed count data and the background estimates, separately for each
combination of detectors and energy range. For a given detector-energy range
combination with label $i$ and a given time step $t$, FOCuS-Poisson outputs an
estimate of the maximum significance in the observed count excess relative to
the background, $m_{t}^{(i)}$. This value is computed over an optimal time
interval ending at $t$ and starting at a past time-step $t-d$. Crucially, the
interval length $d$ is not predetermined but rather assessed and optimized by
the algorithm itself, conditionally on the observations. The significance
values $m_{t}^{(i)}$ are recorded, in units of standard deviations, in a table
with dimensions $M\times N$, where $M$ equals the length of the input time
series and $N$ equals the number of detector-energy range combination. From
these table, candidate transients are extrapolated in two steps. The first
step is to identify vertical table slices (rows segments) where a trigger
condition is verified. The second step is to cluster together segments whose
start and end times are closer than a pre-defined value (nominally $600$ s).
The user controls the search’s output through three parameters. For the
trigger condition to be verified it is required that the significance values
exceed a threshold parameter $T$ over a minimum number detectors and energy
ranges. Additionally, the user can limit the choice of the best interval to
those whose length does not exceed a value $d_{\text{max}}$ or whose average
intensity, given as a multiplicative factor of the observed counts in relation
to the integral of background values, is greater than a minimum
$\mu_{\text{min}}$.

##  3 Data

The Fermi/GBM daily `CSPEC` data products were used for both the testing and
the training of the neural network and for searching astrophysical transient
events with FOCuS-Poisson. These data are photon counts with duration $4.096$
s, binned over $128$ logarithmically spaced energy channels spanning from
$\approx 8$ keV to $\approx 900$ keV Meegan et al. (2009). The time resolution
provided by `CSPEC` data is high enough to investigate long and ultra-long
GRBs, yet it is too low to reliably identify short GRBs and other transients
with characteristic duration shorter than a few seconds. This is unfortunate,
yet justified for our use-case. Indeed, the variability of background over
time intervals of duration comparable to the duration of short GRBs is
negligible, hence our method provides little benefits relative to simpler
approaches such as moving average or exponential smoothing. On the other hand,
an accurate description of the background become essential when searching for
long, faint events, in particular events whose duration is comparable to that
of the Fermi orbit. We consider data from all of the Fermi/GBM’s twelve NaI
detectors. Each detector is identified according to the standard GBM
nomenclature (ten detectors labelled with integers ranging from $0$ to $9$,
two detectors are identified by the letters $a$ and $b)$. In our analysis we
disregard the Fermi/GBM bismuth-germanate detectors. These instruments are in
fact sensible to energies much greater than the energies typically involved
with GRBs prompt emission and are mainly used for the detection and
observation of phenomena different from GRBs, such as Terrestrial Gamma-Ray
Flashes (TGF) Von Kienlin et al. (2020). To build the target variables $Y$,
the input `CSPEC` data from each detector are binned anew, this time over
three coarser energy ranges (28-50 keV, 50-300 keV and 300-500 keV, see Table
1). The resulting dataset is arranged in a table with $36$ columns, one for
each of the $36$ detector-energy combinations.

Range | Energy range (keV)
---|---
r0 | 28-50
r1 | 50-300
r2 | 300-500
Table 1:  Energy ranges table.

Beside the `CSPEC` data product, the neural network is trained using
information on the satellite geographical location and the detectors pointing
direction, as well as a number of auxiliary features such as the Earth
occultation status and the visibility of the Sun for each detector at a given
time. These informations are gathered from the Fermi/GBM `POSHIST` data
products. A detail of the orbital and detectors features used in the training
of the NN is given in Table 2 and Table 3. To access both the `CSPEC` and
`POSHIST` data products we use the Fermi Data Tools package, a python software
API to the HEASARC Fermi archival database. The resulting input datasets $X$
include a total of 60 different features, sampled with a step length of
$4.096$ s.

Feature label | Description
---|---
$pos\\_x$, $pos\\_y$, $pos\\_z$ | position of Fermi in Earth inertial coordinates
$a$, $b$, $c$, $d$ | Fermi attitude quaternions
$lat$ | Fermi geographical latitude
$lon$ | Fermi geographical longitude
$alt$ | Fermi orbital altitude
$vx$, $vy$, $vz$ | velocity of Fermi in Earth inertial coordinates
$w1$, $w2$, $w3$ | Fermi angular velocity
$sun\\_vis$ | Sun’s visibility boolean flag.
$sun\\_ra$ | Sun’s right ascension
$sun\\_dec$ | Sun’s declination
$earth\\_r$ | Earth’s apparent radius
$earth\\_ra$ | Earth center right ascension
$earth\\_dec$ | Earth center declination
$saa$ | SAA transit boolean flag
$l$ | approximate McIlwain L value
Table 2:  A table of the 24 orbital features used to form the NN’s input table. The features are obtained from the POSHIST files and processed by the library Fermi GBM Data Tools.  Features | Description
---|---
$ni\\_ra$ |  $i$-labelled detector pointing right ascension
$ni\\_dec$ |  $i$-labelled detector pointing declination
$ni\\_vis$ |  $i$-labelled detector Earth occulation boolean flag
Table 3:  A table of the 36 detector features used to form the NN’s input
table. The features are obtained from the POSHIST files and processed by the
library Fermi GBM Data Tools.

##  4 Results

In this section we present the results of the background estimator and the
trigger algorithm application. The open source code implementation is
available on github.com/rcrupi/DeepGRB.

###  4.1 Background estimator performance

To show the effectiveness of this approach, a NN is trained over 7 months of
data from January to July 2019. An excerpt of the resulting background
estimation is presented in Figure 3 for one detector-range combination. The
MAE values are reported in Table 4. The energy range bins are the same as
those used in Section 3 and are defined in Table 1.

det range | MAE train | MAE test
---|---|---
r0 | 4.942 $\pm$ 0.331 | 4.953 $\pm$ 0.328
r1 | 6.088 $\pm$ 0.167 | 6.098 $\pm$ 0.163
r2 | 1.790 $\pm$ 0.044 | 1.792 $\pm$ 0.045
average | 4.273 | 4.281
Table 4: The NN MAE loss function (within one standard deviation) per energy
range, over the training and the testing datasets, averaged over the 12
Fermi’s GBM NaI detectors.

In Appendix A, the NN background prediction over a dataset comprising
GRB091024 are compared to the results of an established physical Fermi/GBM
background model Biltzinger et al. (2020).

In Figure 4 we plot the NN predictions against the corresponding observed
value, in particular we filtered out the data points 150 s before and after
the SAA. Figure 4 depicts a light-curve with background counts greater than
those observed, we discuss this phenomenon later in this Section 5.

Figure 2:  Fermi/GBM NaI-4 detector photon count rates (crosses) in the energy
range $50$ \- $300$ keV versus the respective prediction from the Neural
Network (red solid lines). The lower panel shows the residuals between the two
quantities, with a black solid line denoting the reference of null residual.
Data span $1400$ s and one SAA crossing. Three data events appear to be
anomalously low, possibly due to lengthy instrument switch-on and switch-off
procedures.

The events of interest for this research should be found when the observed
count rates exceed the NN prediction, that are the points above the bisector
(see Figure 4) for detectors-range combination. Transient, bright events such
as GRBs may result in a temporary increase of the observed count rates and,
taking place at random times and directions, are not predictable from features
intrinsic to the Fermi spacecraft motion and attitude, which are the actual
inputs of the NN.

Figure 3:  The background estimation for the n4 detector, in the energy range
1, on one day orbit. The Fermi/GBM count rate observations are represented
over time as a black line, whereas the neural network estimation is plotted as
a red solid line. The lower panel shows the residuals between the two
quantities, with a black solid line denoting the reference of null residual.
Figure 4:  Fermi/GBM photons counts from NaI-8 detector in the energy range
$50$ \- $300$ keV versus the respective prediction from the NN over the same
combination of detector and energy range. Data spans from 1 January 2019 to 1
July 2019.

The three time periods chosen for the application of the trigger algorithm
spans 1 November 2010 to 19 February 2011, 1 January 2014 to 28 February 2014,
1 March 2019 to 9 July 2019. For the sake of brevity, we will refer to these
epochs as the ‘2010’, the ‘2014’ and the ‘2019’ _periods_. These _periods_ are
chosen to test the framework under a variety of conditions, including solar
activity intensities and potential detector degradation.

A separate NN is trained and tested for each of these periods to account for
variations in background count rates over long time scales (years), which may
be caused by factors such as the two previously mentioned. We report the
performance metrics in Table 5.

NN performance metrics on test set
---
Period |  Energy range |  MAE |  MeAE
2010 |  r0 |  7.730 $\pm$ 4.842 |  3.963 $\pm$ 0.232
2010 |  r1 |  6.469 $\pm$ 1.517 |  4.777 $\pm$ 0.100
2010 |  r2 |  1.864 $\pm$ 0.033 |  1.563 $\pm$ 0.028
2014 |  r0 |  19.79 $\pm$ 18.92 |  4.545 $\pm$ 0.409
2014 |  r1 |  13.29 $\pm$ 11.10 |  5.604 $\pm$ 0.196
2014 |  r2 |  1.949 $\pm$ 0.099 |  1.598 $\pm$ 0.082
2019 |  r0 |  4.831 $\pm$ 0.300 |  3.938 $\pm$ 0.245
2019 |  r1 |  5.640 $\pm$ 0.082 |  4.716 $\pm$ 0.070
2019 |  r2 |  1.804 $\pm$ 0.038 |  1.510 $\pm$ 0.032
Table 5:  Mean Absolute Error (MAE) and Median Absolute Error (MeAE), on the
test datasets, for each energy range and averaged for detectors within one
standard deviation.

Additional details on the neural network’s performance during times of both
high and low solar activity are provided in Appendix C.

###  4.2 Transients detection

With reference to the technique described in Section 2.2, the following
detection parameters were used to obtain the results discussed in this
section. The trigger condition was defined to resolve whenever at least one
detector observed enough counts for the significance level to exceed a
threshold $T=3\sigma$ over the range of energy spanning $50$ keV and $300$
keV.

To measure the consistency of the entire event it is computed the Standard
Score $z$:

$z=\frac{x-\mu}{\sigma}$ (5)

where $\mu$ is the mean and $\sigma$ the standard deviation of the
distribution $\mathcal{X}$. Since we are dealing with count rates that follows
the Poisson distribution, with sufficiently high count rates we can consider
$\mu\approx\sigma^{2}$. Then the Standard Score can be approximated to:

$S=\frac{N-B}{\sqrt{B}}$ (6)

where $N$ is the observed count rates integrated over an interval spanning the
event’s start time and end time222To avoid noise count rates and calculate the
significance around the event’s peak, only count rates greater than a
quantile-based threshold were included in the integral. and over each
triggered detectors. Analogously for $B$ but the total count rates comes from
the background estimated by the NN. Standard Score is determined independently
for each energy range $S_{r0}$, $S_{r1}$ and $S_{r2}$. The overall consistency
for the event is defined as:

$C=max(S_{r0},\;S_{r1},\;S_{r2}).$ (7)

The FOCuS-Poisson algorithm was executed with the parameters $d_{\text{max}}$
and $\mu_{\text{min}}$ set to the values $120.4$ s and $1.2$ s, respectively.
The choice of these parameters was driven by a trade-off between the need to
find most astrophysical transients in our dataset both known and potentially
unknown and the need to minimize the rate of false detection.
The transient search was performed over three distinct time periods, as
defined in the previous section. In the period spanning March 2019 and July
2019 a total of 100 events were identified. Of these, 75 events match the
trigger time of events already in the Fermi/GBM Trigger Catalog Von Kienlin et
al. (2020), one event is due to artifacts in the dataset, while the nature of
the remaining 24 events is uncertain. These results, along other from the
remaining test periods, have been summarized in Table 6. Over the same period,
the Fermi/GBM Burst Catalog Von Kienlin et al. (2020) reports on 96 known
GRBs. Of these bursts, 15 are missing a counterpart in our dataset due to the
clipping of data $150$ s before and after a SAA transit. Of the remaining 76
bursts, 68 have $T_{90}$ duration larger than the bin-length resolution of our
dataset ($4.096$ s). We were able to correctly identify $60$ of these bursts
($88\%$). Finally, we detected 5 out of 13 ($34\%$) GRBs with $T_{90}$
duration inferior to the the bin-lenght resolution of our dataset. These
results are summarized in Figure 5 and Table 7, the latter also reporting on
results from other periods.

Transient detection statistics
---
Period |  Total events |  Known |  Consistency median known |  Unknown |  Consistency median unknown |  False Detections
2010 |  81 |  58 |  $>10$ |  15 |  8.83 |  8
2014 |  195 |  81 |  $>10$ |  71 |  $>10$ |  43
2019 |  100 |  75 |  $>10$ |  24 |  8.71 |  1
Table 6:  Total number of transients identified, number of transients with
counterparts in the Fermi/GBM Trigger Catalog and its median consistency;
number of transients of uncertain origin with no counterparts in the Fermi/GBM
Trigger Catalog and its median consistency; false detection. Each table row
corresponds to a different time period. Known GRBs detection statistic
---
Period |  Total GRBs |  Missing (no data) |  $T_{90}>4.096$ s |  $T_{90}<4.096$ s
2010 |  77 |  11 |  39/52 (75 %) |  2/14 (14 %)
2014 |  36 |  8 |  17/18 (94 %) |  4/10 (40 %)
2019 |  96 |  15 |  60/68 (88 %) |  5/13 (34 %)
Table 7:  Total number of Fermi/GRBs in the Fermi/GBM Burst Catalog, number of
events with missing (no data) counterparts in our dataset due to SAA data
clipping (see Section 4.2), fraction of detected bursts with duration greater
than the bin-length time resolution of the tested dataset, fraction of
detected bursts with duration smaller than the bin-length time resolution of
the tested dataset. Each table row corresponds to a different time period.
Figure 5:  GRB detection performances. Each dot represents a gamma-ray burst
of the Fermi/GBM Burst Catalog discovered between March 1st and July 1st 2019
over the space spanned by the GRB’s duration $T_{90}$ and flux, the latter
computed as the ratio between the catalog’s GRB fluence in band 10-1000 keV
and $T_{90}$). Events in the shaded grey region have $T_{90}$ duration smaller
than the bin-length time resolution of the dataset tested with the present
framework ($4.096$ s, CSPEC data). Colors are used to identify the detection
status within our search. In red the events unidentified with our method.
Missing events (no data) are due to clipping of data 150 s before and after a
SAA transit or portion of data that could not be preprocessed.

##  5 Discussion

According to Tables 4 the test set and train set MAE values are similar up to
$1\%$ indicating no over-fitting and strong generalization across energy range
and detector. Table 5 shows that the neural network trained on data from the
2014 period has the highest (worst) MAE, which can be attributed to the
presence of strong solar activity. This is understandable since, during an
activity maximum, the background particle count rate is more unpredictable due
to the influence of the Sun on the local radiation environment (see Figure
6(b)). Nonetheless, MeAE shows similar performance with the other two periods,
thanks to its robustness against outliers. On the other hand, the 2019 period
has the lowest MAE most likely due to low solar activity and low background
variability. For more details on predictive robustness against high solar
activity see Appendix C.

In Figure 4, most of the data points are distributed along the plot bisector
$y=x$, indicating that most often the neural network estimate is in agreement
with the actual observations. Above the bisector, more counts are expected
than they are actually observed. From spot analysis of these datapoints it is
apparent that overestimates in NN prediction often happen when Fermi enters or
leaves the SAA, see for example Figure 2. A possible explanation of this
behaviour can be rooted in the Fermi/GBM experimental apparatus. Fermi/GBM is
composed of multiple photomultipliers. These instruments require high voltages
to operate. A “ramp-up" of a photomultiplier to operational voltage takes
place over a time span of several seconds. During this time, the
photomultiplier amplification factor (the gain) is hindered resulting in lower
than nominal count rates. The same effect takes place when “ramping-down"
before turning a photomultiplier off. In the lower part of the bisector the
observed counts rate exceed the counts estimated. The trigger algorithm’s goal
is to determine whether this exceeding amount is part of an event or simply a
random fluctuation.

In periods of high solar activity, Fermi/GBM data include a large number of
soft transient events of solar origin; thus, the soft ($25$ \- $50$ keV)
trigger conditions have been disabled on multiple occasions (e.g. see Table 4
in Bhat et al. (2016) for 2014). Likewise, we required that at least one
detector must be over threshold in the energy band spanning $50$ and $300$ keV
in order for the trigger condition to be satisfied. Still, Table 6 shows a
higher number of total events for the 2014 period. The majority of these
events are most likely associated to solar flares; indeed, 50 of the 81 events
in the GBM trigger catalog for this period are solar flares, and the majority
of the events we find with no counterpart in the Fermi/GBM trigger catalog are
triggered over Sun facing detectors (n0, n1, n2, n3, n4, n5). False detections
may be caused by artifacts in the background estimation. These are generally
easy to identify; most of the time these artifacts take the form of sudden
steps in the background estimate, simultaneously over all detector/range
combination. One of these events is represented in Figure 6(a). This behaviour
is less frequently present in the other two periods analyzed, indicating that
noisy background impacts on performance (see MAE) and therefore more false
positive are detected.

(a)

(b)

Figure 6: Photon counts from each triggered detector are plotted with step
lines, across three energy bands spanning $28-50$ keV, $50-300$ keV and
$300-500$ keV (Table 1), with a resolution of $4.096$ s. The neural network’s
prediction of background count rates is represented by solid lines. Different
detectors are identified using different colors. A red shaded area limits
FOCuS-Poisson’s best guess of the transient duration. Times are expressed in
units of seconds according to Fermi’s standard mission elapsed time (MET). (a)
Example of False Detection in which all the detector are triggered over an
imprecision of the Neural Network estimation. (b) Example of a solar flare in
the Fermi/GBM catalog detected by our approach. The event start and end MET
time, as reported in the Fermi/GBM trigger catalog, is represented by a grey
shaded area.

To further investigate the detected and undetected GRBs, we plot the flux
(total fluence divided by T90) vs T90 for our triggered events in Figure 5.
The red points are events reported in the Fermi/GBM catalog but undetected by
our method. The Fermi/GBM events with a duration less than our time binning
(4.096s) are often undetected in our analysis because of the too coarse
binning. We also miss a few longer events with low count rates. Reducing the
time binning by using data with higher time resolution, such as `CTIME` or
`TTE`, could be beneficial to capture shorter and fainter events. Despite the
unfavorable adopted time binning of 4.096s, we recovered $\geq 75\%$ of the
GRBs with $T_{90}$ greater than 4.096s, see Table 7.

We also detect many events not present in the Fermi/GBM catalog, and we use
the methodology outlined in Kommers (1999) to characterize these transients.
More specifically, we classify events as:

  * •

Solar flare (SF) when the majority of the counts are in the low-energy range
and the Sun is in the field of view of the triggered detectors.

  * •

Terrestrial Gamma-ray Flash (TGF) when most of the counts are in the high-
energy range and the event’s source reaches the detector from the Earth’s
horizon.

  * •

Gamma-ray burst (GRB) when most of the counts are in the $50-300$keV energy
range, and the source direction is not occulted by the Earth and is distant
from both the Sun and the galactic plane.

  * •

Galactic X-ray flash (GF) when the source direction is compatible with that of
the galactic plane.

  * •

Uncertain (UNC) in all other cases.

To determine the source direction, we employ a simple method based on the
evaluation of the pointing and the relative photon count rate of the
detectors. Further details can be found in Appendix B.

Two classes of transient events are discussed further in this section: events
already classified as GRBs in the Fermi/GBM trigger catalog; events not
present in the Fermi/GBM catalog but classified by us as candidate GRBs. We
report In Table 8 six more events that have no catalog counterpart, suggesting
one or more of the previously mentioned categories. All these events are a
cherry pick selection of the unknown events in Table LABEL:tab:catalog_unk.

#### GRB 190320A

At 01:14:16 UTC on March 20, 2019, the long GRB 190320052 triggered the
Fermi/GBM on board trigger algorithm across detectors n6 and n9. The estimated
$T_{90}$ duration is $43$ s, with the highest emission component in the
$50$-$300$ keV band. In our analysis, the detectors n6, n7, n8, n9 and na all
exceeded a $3.0$ $\sigma$ significance threshold during the period event
(Figure 7) with a resulting consistency greater than $10$ on energy range r1
and $5.74$ on r2. The background estimate is comparable to a second order
polynomial fitting in the soft energy range and first order polynomial fitting
in the $50$-$300$ keV energy range.

Figure 7:  The Fermi/GBM catalog GRB190320, as detected by our method. Photon
counts from each triggered detector are plotted with step lines, across three
energy bands spanning $28-50$ keV, $50-300$ keV and $300-500$ keV (Table 1),
with a resolution of $4.096$ s. The neural network’s prediction of background
count rates is represented by solid lines. Different detectors are identified
using different colors. The GRB start and end MET time, as reported in the
Fermi/GBM burst catalog, is represented by a grey shaded area. A red shaded
area limits FOCuS-Poisson’s best guess of the transient duration. Times are
expressed in units of seconds according Fermi’s standard mission elapsed time
(MET).

#### Event 190420939

Figure 8 shows an event not present in the GBM trigger catalog, similar to
GRB190320052 but with higher low-energy count rate. The event has been
triggered by detectors n6, n7, n8, na and nb in the low energy band with a
consistency greater than $10$. Two detectors provided a trigger in the
$50$-$300$ keV energy band, with a consistency of $8.4$.

Figure 8:  The 190420939 transient event with no direct counter part in the
Fermi/GBM trigger catalog. The event was classified as a candidate gamma-ray
burst, according to the discussion presented in Section 5. For the
corresponding localization see Figure 9. Photon counts from each triggered
detector are plotted with step lines, across three energy bands spanning
$28-50$ keV, $50-300$ keV and $300-500$ keV (Table 1), with a resolution of
$4.096$ s. The neural network’s prediction of background count rates is
represented by solid lines. Different detectors are identified using different
colors. A red shaded area limits FOCuS-Poisson’s best guess of the transient
duration. Times are expressed in units of seconds according Fermi’s standard
mission elapsed time (MET).

We can see from the localization estimate in Figure 9 that the event is far
from the galactic plane, the Sun, and the Earth’s horizon. With all of this
information, this event could be a long soft GRB. The localization algorithm
used is described in detail in Section B.

Figure 9:  Estimate of the candidate event’s source localization over the
celestial sphere at 2019-04-20 22:32:56 UTC.

#### Interesting events

We list in Table 8 a selection of interesting events, including the one
already discussed, which are not present in the GBM catalog and which deserve
further analysis. Appendix D present plots associated to these events. Events
1 and 2 are classified as Solar Flares because their location is close to the
Sun and the majority of the detectors triggered are in the energy range r0.
Because event 3 is far from the Sun yet close to the galactic plane and the
Earth’s horizon, it might be a Galactic X-ray flash or a Terrestrial Gamma-ray
Flash. Event 4 and 6 are categorized as GRBs for the same reasons as event 5,
however because they are near the galactic plane, event 6 might be a Galactic
X-ray burst. Finally, in event 7, nine detectors with roughly equal
intensities are triggered, suggesting that this event is likely due to Local
Particles. This is further validated by the satellite’s position at high
geomagnetic latitude (Figure 22), which is highly correlated with the
localization of charged particle events Von Kienlin et al. (2020); as a
result, the event is classified as uncertain.

It’s worth noting that GRB 190404B GCN Circular notice discovered by Monitor
of all-sky X-ray image (MAXI) satellite333http://maxi.riken.jp/grbs/190404b/
has location $(\text{RA}=221^{\circ},\text{Dec}=-22^{\circ})$, which is
similar to event 4, and trigger time 2019/04/04 13:14:34.00 UTC, which is six
minutes after event 4.

The complete catalog of unknown and known events for the three time periods
analyzed can be found in Appendix in Tables LABEL:tab:catalog_unk and
LABEL:tab:catalog_kn, respectively. The events are reported with the trigger
time, duration, the triggered detectors, the Standard Score for each energy
range, and a significance classification. Unknown events were assigned
tentative transient classes using the methodology described in this section.

ID | Trigger time | T (s) |  Detectors triggered | RA (∘) | Dec (∘) |  Transient class | C
---|---|---|---|---|---|---|---
1 | 2014-01-27 05:21:12 | 32.77 |  n0 n1 n2 n3 n4 n5 n8 nb | 306 | -22 |  SF | $>10$
2 | 2010-11-11 18:58:17 | 16.38 |  n2 n4 n5 | 230 | -20 |  SF | $>10$
3 | 2014-01-12 13:59:58 | 102.40 |  n6 n7 n8 n9 na nb | 105 | 10 |  GF/TGF | $>10$
4 | 2019-04-04 13:08:07 | 8.19 |  n9 na | 220 | -10 |  GRB | 4.93
5 | 2019-04-20 22:32:56 | 16.38 |  n6 n7 n8 n9 nb | 245 | 40 |  GRB | $>10$
6 | 2019-06-06 13:21:42 | 16.38 |  n7 n8 nb | 250 | 25 |  GRB/GF | 9.12
7 | 2011-02-15 15:59:02 | 118.79 |  n0 n1 n2 n5 n6 n7 n8 n9 nb | 208 | 62 |  UNC | $>10$

Table 8:  List of interesting events. We report the ID, the start time in MET
and UTC, the end time in MET, the detectors triggered during the event, the
localisation expressed in right ascension and declination, the proposed
transient class and the consistency of the event

.

##  6 Conclusion

A novel method for high-energy, transient event detection is presented,
integrating the precise estimation of a NN with an efficient trigger
algorithm. The method has been designed to be applied to HERMES Pathfinder
data, but it can be extended to analyze data from other space-based, high-
energy missions and we have presented here an application using Fermi/GBM
data. The first step is to estimate the background count rate with a NN using
satellite data that may be used to build a physical background model. The
accuracy of the background estimate is measured using Mean Absolute Error and
Median Absolute Error. An experiment is carried out to assess the robustness
of the background estimator during the periods of solar maxima (2014) and
solar minima (2020), demonstrating that the background estimation is stable
enough to have comparable performance in both periods. Because HERMES
Pathfinder will be deployed near the next solar maximum, a scenario of
expected count rates is provided in Appendix. The background is then used by
FOCuS-Poisson, an evolution of the CUSUM algorithm, to efficiently detect the
transient events. This method is tested using three periods of Fermi/GBM data
binned in time for 4s. We provide statistics on known and unknown transients
in the GBM catalog. We show that with our method we are able to recover known
events longer than 4s, and to selected events not included in the Fermi/GBM
catalog. Seven of the unknown events are discussed in details. Future work
will aim to improve neural network prediction, such as employing a Recurrent
Neural Network to provide a smoother signal or reducing time binning to detect
shorter events. Also, provide explainability methods to allow the user to
explain a specific prediction or debug the NN.

## Acknowledgement

We thank Kester Ward for providing a python implementation of FOCuS-Poisson
from which the version used through this work was derived. Special thanks to
Daniele Regoli for the useful recommendations in the introduction and method
sections. This research acknowledge support from the European Union Horizon
2018 and 2020 Research and Innovation Frame-work Programme under grant
agreements HERMES-Scientific Pathfinder n. 821896 and AHEAD2020 n. 871158, and
by ASI INAF Accordo Attuativo n. 2018-10-HH.1.2020 HERMES—Technologic
Pathfinder Attivita’ scientifiche. We also aknownledge the support of the INAF
RSN-5 mini-grant 1.05.12.04.05, "Development of novel algorithms for detecting
high-energy transient events in astronomical time series".

## References

  * Woosley [1993] Stan E Woosley.  Gamma-ray bursts from stellar mass accretion disks around black holes.  _The Astrophysical Journal_ , 405:273–277, 1993.
  * Berger [2014] Edo Berger.  Short-duration gamma-ray bursts.  _Annual review of Astronomy and Astrophysics_ , 52:43–105, 2014.
  * Fiore et al. [2020] Fabrizio Fiore, Luciano Burderi, Michelle Lavagna, Roberto Bertacin, Yuri Evangelista, Riccardo Campana, Fabio Fuschino, Paolo Lunghi, Angel Monge, Barbara Negri, et al.  The hermes-technologic and scientific pathfinder.  In _Space Telescopes and Instrumentation 2020: Ultraviolet to Gamma Ray_ , volume 11444, pages 214–228. SPIE, 2020.
  * Fiore et al. [2021] Fabrizio Fiore, Norbert Werner, and Ehud Behar.  Distributed Architectures and Constellations for $\gamma$-ray Burst Science.  _Galaxies_ , 9(4):120, December 2021.  doi:10.3390/galaxies9040120.
  * Fuschino et al. [2019] Fabio Fuschino, RICCARDO Campana, CLAUDIO Labanti, Yuri Evangelista, MARCO Feroci, L Burderi, Fabrizio Fiore, Filippo Ambrosino, G Baldazzi, P Bellutti, et al.  Hermes: An ultra-wide band x and gamma-ray transient monitor on board a nano-satellite constellation.  _Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment_ , 936:199–203, 2019.
  * Evangelista et al. [2020] Yuri Evangelista, Fabrizio Fiore, Fabio Fuschino, Riccardo Campana, Francesco Ceraudo, Evgeny Demenev, Alejandro Guzman, Claudio Labanti, Giovanni La Rosa, Mauro Fiorini, et al.  The scientific payload on-board the hermes-tp and hermes-sp cubesat missions.  In _Space Telescopes and Instrumentation 2020: Ultraviolet to Gamma Ray_ , volume 11444, page 114441T. International Society for Optics and Photonics, 2020.
  * Fiore et al. [2022] Fabrizio Fiore, Alejandro Guzman, Riccardo Campana, and Yuri Evangelista.  HERMES-Pathfinder.  _arXiv e-prints_ , art. arXiv:2210.13842, October 2022.  doi:10.48550/arXiv.2210.13842.
  * Evangelista et al. [2022] Y. Evangelista, F. Fiore, R. Campana, F. Ceraudo, G. Della Casa, E. Demenev, G. Dilillo, M. Fiorini, M. Grassi, A. Guzman, P. Hedderman, E. J. Marchesini, G. Morgante, F. Mele, P. Nogara, A. Nuti, R. Piazzolla, S. Pliego Caballero, I. Rashevskaya, F. Russo, G. Sottile, C. Labanti, G. Baroni, P. Bellutti, G. Bertuccio, J. Cao, T. Chen, I. Dedolli, M. Feroci, F. Fuschino, M. Gandola, N. Gao, F. Ficorella, P. Malcovati, A. Picciotto, A. Rachevski, A. Santangelo, C. Tenzer, A. Vacchi, L. Wang, Y. Xu, G. Zampa, N. Zampa, and N. Zorzi.  Design, integration, and test of the scientific payloads on-board the HERMES constellation and the SpIRIT mission.  In Jan-Willem A. den Herder, Shouleh Nikzad, and Kazuhiro Nakazawa, editors, _Space Telescopes and Instrumentation 2022: Ultraviolet to Gamma Ray_ , volume 12181 of _Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series_ , page 121811G, August 2022\.  doi:10.1117/12.2628978.
  * Meegan et al. [2009] Charles Meegan, Giselher Lichti, PN Bhat, Elisabetta Bissaldi, Michael S Briggs, Valerie Connaughton, Roland Diehl, Gerald Fishman, Jochen Greiner, Andrew S Hoover, et al.  The fermi gamma-ray burst monitor.  _The Astrophysical Journal_ , 702(1):791, 2009\.
  * Paciesas et al. [1999] William S Paciesas, Charles A Meegan, Geoffrey N Pendleton, Michael S Briggs, Chryssa Kouveliotou, Thomas M Koshut, John Patrick Lestrade, Michael L McCollough, Jerome J Brainerd, Jon Hakkila, et al.  The fourth batse gamma-ray burst catalog (revised).  _The Astrophysical Journal Supplement Series_ , 122(2):465, 1999.
  * Feroci et al. [1997] Marco Feroci, Filippo Frontera, Enrico Costa, Daniele Dal Fiume, Lorenzo Amati, L Bruca, Maria Nerina Cinti, Alessandro Coletta, P Collina, C Guidorzi, et al.  In-flight performances of the bepposax gamma-ray burst monitor.  In _EUV, X-Ray, and Gamma-Ray Instrumentation for Astronomy VIII_ , volume 3114, pages 186–197. SPIE, 1997.
  * Kommers [1999] Jefferson Michael Kommers.  _Faint gamma-ray bursts and other high-energy transients detected with BATSE_.  PhD thesis, Massachusetts Institute of Technology, 1999.
  * Kocevski et al. [2018] D Kocevski, E Burns, A Goldstein, T Dal Canton, MS Briggs, L Blackburn, P Veres, CM Hui, R Hamburg, OJ Roberts, et al.  Analysis of sub-threshold short gamma-ray bursts in fermi gbm data.  _The Astrophysical Journal_ , 862(2):152, 2018\.
  * Hui et al. [2017] CM Hui, MS Briggs, P Veres, and R Hamburg.  Finding untriggered gamma-ray transients in the fermi gbm data.  In _Proceedings of the 7th International Fermi Symposium_ , page 129, 2017.
  * Biltzinger et al. [2020] Björn Biltzinger, Felix Kunzweiler, Jochen Greiner, Kilian Toelge, and J Michael Burgess.  A physical background model for the fermi gamma-ray burst monitor.  _Astronomy & Astrophysics_, 640:A8, 2020.
  * Sadeh [2019] Iftach Sadeh.  Deep learning detection of transients.  _arXiv preprint arXiv:1902.03620_ , 2019.
  * LeCun et al. [2015] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton.  Deep learning.  _nature_ , 521(7553):436–444, 2015.
  * Bishop et al. [1995] Christopher M Bishop et al.  _Neural networks for pattern recognition_.  Oxford university press, 1995.
  * Gendre et al. [2019] B Gendre, QT Joyce, NB Orange, G Stratta, JL Atteia, and M Boër.  Can we quickly flag ultra-long gamma-ray bursts?  _Monthly Notices of the Royal Astronomical Society_ , 486(2):2471–2476, 2019.
  * Bissaldi et al. [2009] Elisabetta Bissaldi, Andreas von Kienlin, G Lichti, Helmut Steinle, P Narayana Bhat, Michael S Briggs, Gerald J Fishman, Andrew S Hoover, R Marc Kippen, Michael Krumrey, et al.  Ground-based calibration and characterization of the fermi gamma-ray burst monitor detectors.  _Experimental Astronomy_ , 24(1-3):47–88, 2009\.
  * Campana et al. [2020] Riccardo Campana, Fabio Fuschino, Yuri Evangelista, Giuseppe Dilillo, and Fabrizio Fiore.  The hermes-tp/sp background and response simulations.  In _Space Telescopes and Instrumentation 2020: Ultraviolet to Gamma Ray_ , volume 11444, pages 817–824. SPIE, 2020.
  * Dilillo et al. [2022] Giuseppe Dilillo, Nicola Zampa, Riccardo Campana, Fabio Fuschino, Giovanni Pauletta, Irina Rashevskaya, Filippo Ambrosino, Marco Baruzzo, Diego Cauz, Daniela Cirrincione, et al.  Space applications of gagg: Ce scintillators: a study of afterglow emission by proton irradiation.  _Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms_ , 513:33–43, 2022.
  * Fitzpatrick et al. [2012] Gerard Fitzpatrick, Sheila McBreen, Valerie Connaughton, and Michael Briggs.  Background estimation in a wide-field background-limited instrument such as fermi gbm.  In _Space Telescopes and Instrumentation 2012: Ultraviolet to Gamma Ray_ , volume 8443, pages 965–973. SPIE, 2012.
  * Goldstein et al. [2021] Adam Goldstein, William H. Cleveland, and Daniel Kocevski.  Fermi gbm data tools: v1.1.0, 2021.  URL https://fermi.gsfc.nasa.gov/ssc/data/analysis/gbm.
  * Ward et al. [2022] Kes Ward, Giuseppe Dilillo, Idris Eckley, and Paul Fearnhead.  Poisson-focus: An efficient online method for detecting count bursts with application to gamma ray burst detection.  _arXiv preprint arXiv:2208.01494_ , 2022.
  * Von Kienlin et al. [2020] A Von Kienlin, CA Meegan, WS Paciesas, PN Bhat, E Bissaldi, MS Briggs, E Burns, WH Cleveland, MH Gibby, MM Giles, et al.  The fourth fermi-gbm gamma-ray burst catalog: A decade of data.  _The Astrophysical Journal_ , 893(1):46, 2020\.
  * Hastie et al. [2009] Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman.  _The elements of statistical learning: data mining, inference, and prediction_ , volume 2.  Springer, 2009.
  * Ioffe and Szegedy [2015] Sergey Ioffe and Christian Szegedy.  Batch normalization: Accelerating deep network training by reducing internal covariate shift.  In _International conference on machine learning_ , pages 448–456. PMLR, 2015.
  * Srivastava et al. [2014] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov.  Dropout: A simple way to prevent neural networks from overfitting.  _Journal of Machine Learning Research_ , 15(56):1929–1958, 2014.  URL http://jmlr.org/papers/v15/srivastava14a.html.
  * Abadi et al. [2015] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.  TensorFlow: Large-scale machine learning on heterogeneous systems, 2015\.  URL https://www.tensorflow.org/.  Software available from tensorflow.org.
  * Ruder [2016] Sebastian Ruder.  An overview of gradient descent optimization algorithms.  _CoRR_ , abs/1609.04747, 2016.  URL http://arxiv.org/abs/1609.04747.
  * Bhat et al. [2016] P Narayana Bhat, Charles A Meegan, Andreas Von Kienlin, William S Paciesas, Michael S Briggs, J Michael Burgess, Eric Burns, Vandiver Chaplin, William H Cleveland, Andrew C Collazzi, et al.  The third fermi gbm gamma-ray burst catalog: the first six years.  _The Astrophysical Journal Supplement Series_ , 223(2):28, 2016.
  * Gruber et al. [2011] D Gruber, T Krühler, S Foley, M Nardini, D Burlon, A Rau, Elisabetta Bissaldi, A Von Kienlin, S McBreen, J Greiner, et al.  Fermi/gbm observations of the ultra-long grb 091024-a burst with an optical flash.  _Astronomy & Astrophysics_, 528:A15, 2011.
  * Goldstein et al. [2020] Adam Goldstein, Corinne Fletcher, Peter Veres, Michael S Briggs, William H Cleveland, Melissa H Gibby, C Michelle Hui, Elisabetta Bissaldi, Eric Burns, Rachel Hamburg, et al.  Evaluation of automated fermi gbm localizations of gamma-ray bursts.  _The Astrophysical Journal_ , 895(1):40, 2020\.
  * Oceanic and Administration [2019] Space Weather Prediction Center National Oceanic and Atmospheric Administration.  Solar cycle 25 forecast update, 2019.  URL https://www.swpc.noaa.gov/news/solar-cycle-25-forecast-update.
  * Biesecker and Upton [2019] Douglas Alan Biesecker and Lisa Upton.  Solar cycle 25 consensus prediction update.  In _AGU Fall Meeting Abstracts_ , volume 2019, pages SH13B–03, 2019\.
  * Hathaway et al. [1994] David H Hathaway, Robert M Wilson, and Edwin J Reichmann.  The shape of the sunspot cycle.  _Solar Physics_ , 151(1):177–190, 1994.
  * Upton and Hathaway [2018] Lisa A Upton and David H Hathaway.  An updated solar cycle 25 prediction with aft: The modern minimum.  _Geophysical Research Letters_ , 45(16):8091–8095, 2018.
  * Bhowmik and Nandy [2018] Prantika Bhowmik and Dibyendu Nandy.  Prediction of the strength and timing of sunspot cycle 25 reveal decadal-scale space environmental conditions.  _Nature communications_ , 9(1):1–10, 2018.

##  Appendix A Background estimate for GRB 091024

To demonstrate the potential of the background estimator in the presence of a
long event, a background estimation is performed in a period containing the
ultra-long GRB 091024 Gruber et al. [2011], for which a similar evaluation is
provided in Biltzinger et al. [2020].

In Figure 10 are shown detectors n0, n6 and n8 in the three energy band
specified in Table 1. The data and background estimation of a Neural Network
trained and tested during a three-month period, from September 1 to November
30, 2009, are presented in black and red, respectively. The dataset consists
of 1.63 million of samples and the hyperparameters are the same used in
Section 2.1 except for the learning rate

$\eta=\begin{cases}0.025&\text{if epoch $<4$}\\\ 0.004&\text{if $4\geq$ epoch
$<12$}\\\ 0.001&\text{if epoch $\geq 12$}\end{cases}.$

The event emerges clearly from the residuals of all the detectors in range r1
and r2, in detector n0 and range r2 it is still visible a peak probably
belonging to the end of it. In detector n6 range r0, in the first part of the
time series before the peaks of the event, the background estimation
underestimates the foreground (data observed). This could be due to a too
short period of training dataset, a non optimal parameter settings of the NN,
a different event such as Local Particles or, more interestingly, the first
part of the GRB, where photon counts were too low to be detected due to
background variability.

Figure 10: Observed and background estimate count rates around the event GRB
091024. From left to right the plots refer respectively to range r0, r1, r2,
from top to bottom the plots refer respectively to detectors n0, n6, n8. This
figure can be compared with Figure 18 in Biltzinger et al. [2020].

##  Appendix B Localization

For the standard reference for the localization of events found by GBM, look
Goldstein et al. [2020].
In this work the localization is done by a simple geometric reasoning, but in
future we hope to use more sophisticated algorithm of localization. To
optimise the function loss it is employed a particle swarm
optimiser444https://github.com/tisimst/pyswarm.

Consider two vectors in the equatorial coordinates $\psi_{d}=(ra_{d},dec_{d})$
and $\psi_{s}=(ra_{s},dec_{s})$, respectively the pointing of a detector and
the localization of the event source. The incidence intensity is modeled as
the cosine between the angle $cos(\psi_{d},\psi_{s})$ is:

$\displaystyle cos(\psi_{d},\psi_{s})$
$\displaystyle=cos(\psi_{d,ra})cos(\psi_{d,dec})cos(\psi_{s,ra})cos(\psi_{s,dec})+$
$\displaystyle+sin(\psi_{d,ra})cos(\psi_{d,dec})sin(\psi_{s,ra})cos(\psi_{s,dec})+$
$\displaystyle+sin(\psi_{d,dec})sin(\psi_{s,dec})$

If the angle of incidence is grater than $\pi/2$ than the incident intensity
must be set to 0. Finally we have 8

$\mathcal{I}(\psi_{d},\psi_{s})=max(cos(\psi_{d},\psi_{s}),0)$ (8)

The loss to optimise in Equation 9, where $i$ is a particular detector in $D$
detectors (in our case 12). The energy range chosen is the one with the
biggest residuals among detectors/energy ranges, then the count rates
corresponding to the timestamp of the maximum value is given to the loss 9 and
minimized.

$\frac{\sum_{i=1}^{D}(counts_{s}(\mathcal{I}(\psi_{i},\psi_{s}))-counts_{i})^{2}}{D}$
(9)

where $\psi_{s}$ and $counts_{s}$ are the unknown variables.

##  Appendix C Solar minima and maxima

Hermes Pathfinder will be launched in 2024 that is near the next solar maximum
forecast in 2025 Oceanic and Administration [2019], Biesecker and Upton
[2019]. This analysis is interesting because reveals what background is
expected and how the NN background estimation performs in the two periods. The
most sensitive detector for the solar activity is the Sun-facing n5 Meegan et
al. [2009]. In this analysis are considered background binned in a GBM period
orbits (about 96m) and 16 GBM period orbits, for range 0, the most sensitive
for solar flares, in the year of the last solar minima, 2014, and the local
minima, 2020. The Figures 12, 13, 15 and 11, 14 are obtained considering
respectively years 2014 and 2020, a NN per each year is trained. One orbit
time binning for 2020 11 and 2014 13 are not comparable due to the high values
of the latter but if we zoom the estimated background part we see a comparable
counts rate value 12. The same reasoning applies for 16 orbits in 2020 14 and
2014 15. In Table 9 are presented the performance of the background estimation
for the year 2014 and 2020. If we use a more robust metric than MAE, such
MeAE, we can conclude that the estimations have comparable performance, so the
the training procedure of the NN is not influenced by a huge solar activity.

Some reference for the solar cycle prediction can be found in Hathaway et al.
[1994], Upton and Hathaway [2018], Bhowmik and Nandy [2018]. For evaluation
purposes, the Median Absolute Error (MeAE)

$\text{MeAE}(x,y)=\text{median}(\\{\mid y_{i}-x_{i}\mid\\})$

is a robust metric against the outliers.

Figure 11:  The background estimation in year 2020 for detector n5 (Sun-facing) in the energy range r0. The count rates with a time bin of 4.096s are the averaged over 1 period orbit (96m). Figure 12:  The background estimation in year 2014 for detector n5 (Sun-facing) in the energy range r0. The count rates with a time bin of 4.096s are the averaged over 1 period orbit (96m). A zoom-in is applied to avoid the outliers shown in Figure 13. Figure 13:  The background estimation in year 2014 for detector n5 (Sun-facing) in the energy range r0. The solar activity in this year is tremendously high. The count rates with a time bin of 4.096s are the averaged over 1 period orbit (96m). Figure 14:  The background estimation in year 2020 for detector n5 (Sun-facing) in the energy range r0. The count rates with a time bin of 4.096s are the averaged over 16 period orbits ($25.6$h). Figure 15:  The background estimation in year 2014 for detector n5 (Sun-facing) in the energy range r0. The count rates with a time bin of 4.096s are the averaged over 16 period orbits ($25.6$h). year | MAE train | MAE test | MeAE train | MeAE test
---|---|---|---|---
2014 | 10.601 | 10.467 | 3.940 | 3.944
2020 | 4.897 | 4.914 | 3.901 | 3.909
Table 9: For each year are presented the metrics averaged per detector and
range.

##  Appendix D Interesting events

Figure 16: Lightcurve and localization for id 1. The Sun is located under the
purple spot. We classify this event as SF.

Figure 17: Lightcurve and localization for id 2. The Sun is located under the
purple spot. We classify this event as SF.

Figure 18: Lightcurve and localization for id 3. Because of its location near
the galactic plane and the Earth’s horizon, we could classify this event as
TGF or GF.

Figure 19: Lightcurve and localization for id 4. We classify this event as a
GRB.

Figure 20: Lightcurve and localization for id 5. We classify this event as a
GRB.

Figure 21: Lightcurve and localization for id 6. We could classify this event
as a GRB or, because of its proximity to the galactic plane, a GF.

Figure 22: In the first two figures, the lightcurve and localization for id 7
are shown. The third one shows where the GBM satellite is located on Earth.
Local Particles events like LOCLPAR1905205 and LOCLPAR1904085 have occurred in
this region. We classify this event as uncertain.

##  Appendix E Catalog tables

Table 10:  Catalog of the unknown events for the three periods analysed. # is an incremental identifier for the events, ID is composed by the referring period and the identifier for its period (YYYY_ID). In catalog_triggers are reported “UNKNOWN: tentative transient class(es)”. $S_{r0}$, $S_{r1}$, $S_{r2}$, are the consistency of the signal for the energy range r0, r1 and r2, respectively. For the tentative transient classes are considered Gamma-Ray Bursts (GRB), Solar Flares (SF) — where the localisation of the event is near the Sun and the trigger time aligns with the GOES X-ray satellite data lightcurve peaks —, Galactic X-ray flash (GF) — where the localisation of the event is near the Galactic Plane —, Terrestial Gamma Flashes (TGF) — where the localisation of the event is near the Earth’s horizon —, Uncertain (UNC), Uncertain but resembling Local Particles (UNC(LP)). In the last column CE is reported the classification of the event, Robust (R) where more than 1 detector and more than 1 energy range is triggered, Solid (S) more than 1 detector but only 1 energy range is triggered, Probable (P) in all other cases. The events with a * in the ID are reported in table 8. # | ID |  Trigger time | T (s) |  Detectors triggered |  Catalog trigger name | S r0 | S r1 | S r2 | CE
---|---|---|---|---|---|---|---|---|---
1 | 2010_1 |  2010-11-02 14:16:36 | 8.19 |  n2 |  UNKNOWN: GRB/GF | 3.99 | 3.38 | 0 | P
6 | 2010_6 |  2010-11-11 13:04:23 | 32.77 |  n2 |  UNKNOWN: GRB | 4.29 | 5.81 | 0 | P
7 | 2010_7* |  2010-11-11 18:58:17 | 16.38 |  n2 n4 n5 |  UNKNOWN: SF | $>10$ | 4.57 | 0 | R
10 | 2010_10 |  2010-11-12 23:46:52 | 19.73 |  n1 |  UNKNOWN: GRB/GF | 0 | 9.18 | 5.31 | P
14 | 2010_14 |  2010-11-22 13:23:34 | 8.19 |  n5 nb |  UNKNOWN: UNC(LP)/GRB | 0 | 7.45 | 5.41 | R
33 | 2010_34 |  2010-12-28 18:22:27 | 106.50 |  na |  UNKNOWN: TGF | 0 | 8.08 | 0 | P
38 | 2010_40 |  2011-01-16 11:40:21 | 20.48 |  n2 n5 |  UNKNOWN: GRB | 0 | 5.36 | 0 | S
44 | 2010_50 |  2011-01-31 15:52:43 | 8.19 |  n5 n6 nb |  UNKNOWN: UNC(LP) | 0 | 7.08 | 4.27 | R
47 | 2010_53 |  2011-02-04 22:17:06 | 16.38 |  n6 n9 |  UNKNOWN: UNC(LP) | $>10$ | 7.85 | 0 | R
48 | 2010_54 |  2011-02-04 23:07:13 | 102.40 |  n0 n3 n4 n5 n6 n7 n8 |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
50 | 2010_56 |  2011-02-06 18:08:11 | 28.67 |  n8 nb |  UNKNOWN: GRB | 5.64 | 6.83 | 0 | R
52 | 2010_59 |  2011-02-09 04:08:03 | 16.86 |  na |  UNKNOWN: GRB/TGF | 0 | 6.08 | 0 | P
56 | 2010_63 |  2011-02-13 17:43:34 | 569.35 |  n0 n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | 9.17 | 0 | R
62 | 2010_70 |  2011-02-15 12:46:47 | 32.77 |  n0 n1 n3 |  UNKNOWN: GRB | 0 | 8.48 | 0 | S
63 | 2010_71* |  2011-02-15 15:59:02 | 118.79 |  n0 n1 n2 n5 n6 n7 n8 n9 nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
67 | 2010_75 |  2011-02-16 17:31:41 | 233.48 |  n0 n1 n2 n6 n7 n8 |  UNKNOWN: UNC(LP) | 0 | $>10$ | 0 | S
71 | 2010_79 |  2011-02-18 13:01:50 | 111.48 |  n0 n1 n2 n3 n4 n5 na |  UNKNOWN: SF | $>10$ | $>10$ | 3.63 | R
72 | 2010_80 |  2011-02-18 14:07:48 | 8.19 |  n0 n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | 3.78 | 0 | R
73 | 2014_0 |  2014-01-01 03:21:00 | 147.46 |  n0 n1 n2 n3 n4 n5 n6 n7 n9 na nb |  UNKNOWN: UNC(LP) | 0 | $>10$ | $>10$ | R
95 | 2014_26 |  2014-01-05 01:18:47 | 204.80 |  n0 n1 n2 n3 n5 n6 n7 n8 n9 |  UNKNOWN: UNC(LP)/GRB | $>10$ | $>10$ | 0 | R
97 | 2014_28 |  2014-01-05 16:04:17 | 73.73 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 nb |  UNKNOWN: UNC | 0 | $>10$ | $>10$ | R
98 | 2014_29 |  2014-01-06 00:53:53 | 593.93 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | $>10$ | R
101 | 2014_34 |  2014-01-07 18:40:36 | 581.64 |  n0 n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | $>10$ | $>10$ | R
102 | 2014_35 |  2014-01-07 18:51:31 | 12.29 |  n0 n1 n3 |  UNKNOWN: SF | $>10$ | 6.96 | 0 | R
107 | 2014_42 |  2014-01-08 19:27:13 | 16.38 |  n4 |  UNKNOWN: TGF | 0 | 4.67 | 0 | P
108 | 2014_51 |  2014-01-09 22:36:09 | 20.48 |  n7 nb |  UNKNOWN: GRB/TGF | 0 | 4.73 | 3.8 | R
111 | 2014_54 |  2014-01-10 22:26:58 | 126.98 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | 0 | $>10$ | $>10$ | R
112 | 2014_55 |  2014-01-10 22:42:45 | 319.49 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
113 | 2014_56 |  2014-01-12 12:04:27 | 8.19 |  n4 |  UNKNOWN: GF/GRB | 0 | 3.65 | 0 | P
114 | 2014_57* |  2014-01-12 13:59:58 | 102.40 |  n6 n7 n8 n9 na nb |  UNKNOWN: GF/TGF | $>10$ | $>10$ | 0 | R
117 | 2014_61 |  2014-01-13 20:37:08 | 45.06 |  n3 n4 na |  UNKNOWN: GRB/TGF | 9.52 | $>10$ | 0 | R
118 | 2014_62 |  2014-01-14 20:08:58 | 143.36 |  n0 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | $>10$ | R
120 | 2014_64 |  2014-01-16 08:34:49 | 16.38 |  n0 n1 n3 n4 n8 n9 nb |  UNKNOWN: GF | $>10$ | $>10$ | 6.71 | R
122 | 2014_67 |  2014-01-18 17:51:57 | 8.19 |  n2 nb |  UNKNOWN: GRB/TGF | 0 | 5.52 | 4.39 | R
123 | 2014_68 |  2014-01-18 23:59:49 | 16.38 |  n2 n5 |  UNKNOWN: GRB | 7.44 | 7.78 | 0 | R
124 | 2014_70 |  2014-01-19 08:53:20 | 12.29 |  n5 nb |  UNKNOWN: TGF/GF/GRB | 0 | 6.47 | 4.07 | R
125 | 2014_71 |  2014-01-19 17:41:35 | 114.69 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | 8.07 | $>10$ | $>10$ | R
126 | 2014_72 |  2014-01-19 17:57:01 | 188.42 |  n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
127 | 2014_73 |  2014-01-21 15:59:44 | 4.10 |  nb |  UNKNOWN: GRB | 0 | 3.06 | 0 | P
130 | 2014_77 |  2014-01-24 15:14:16 | 176.13 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | 0 | $>10$ | $>10$ | R
131 | 2014_78 |  2014-01-25 18:55:46 | 8.19 |  n0 n3 n4 |  UNKNOWN: GRB/GF | 4.91 | 6.04 | 0 | R
134 | 2014_81* |  2014-01-27 05:21:12 | 32.77 |  n0 n1 n2 n3 n4 n5 n8 nb |  UNKNOWN: SF | $>10$ | 9.7 | 0 | R
136 | 2014_83 |  2014-01-28 01:28:34 | 4.10 |  n4 n5 |  UNKNOWN: SF/GRB | 8.96 | 4.51 | 0 | R
140 | 2014_88 |  2014-01-28 12:43:39 | 12.29 |  n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | 2.89 | 2.39 | R
145 | 2014_93 |  2014-01-29 12:46:45 | 163.84 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | 8.72 | $>10$ | $>10$ | R
151 | 2014_99 |  2014-02-01 01:24:58 | 47.10 |  n0 n1 n2 n3 n4 n5 n8 nb |  UNKNOWN: SF | $>10$ | 7.84 | 0 | R
152 | 2014_101 |  2014-02-01 07:18:31 | 237.57 |  n0 n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | $>10$ | 0 | R
155 | 2014_104 |  2014-02-13 01:37:19 | 368.65 |  n0 n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | $>10$ | $>10$ | R
160 | 2014_116 |  2014-02-14 02:54:08 | 139.27 |  n0 n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | $>10$ | 6.23 | R
165 | 2014_121 |  2014-02-15 06:23:21 | 12.29 |  n6 n9 |  UNKNOWN: GF/GRB | 4.7 | 4.48 | 0 | R
166 | 2014_124 |  2014-02-16 00:23:41 | 888.85 |  n3 n4 n5 n6 n7 n8 nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
167 | 2014_125 |  2014-02-16 02:54:45 | 299.01 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
168 | 2014_126 |  2014-02-16 03:16:11 | 65.54 |  n1 n2 n3 n4 n5 n6 n9 na |  UNKNOWN: GRB/UNC | 0 | $>10$ | 9.68 | R
169 | 2014_127 |  2014-02-16 03:28:45 | 40.96 |  n0 n1 n7 |  UNKNOWN: GF | 5.49 | 8.09 | 0 | R
170 | 2014_128 |  2014-02-16 04:29:47 | 16.38 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
171 | 2014_129 |  2014-02-16 04:31:09 | 548.87 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
174 | 2014_132 |  2014-02-17 01:41:56 | 20.48 |  n0 n1 n2 n3 n5 n6 n7 n8 n9 |  UNKNOWN: GF/UNC(LP) | $>10$ | $>10$ | 0 | R
175 | 2014_133 |  2014-02-17 03:06:02 | 151.55 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | 0 | $>10$ | $>10$ | R
177 | 2014_138 |  2014-02-18 12:27:19 | 4.10 |  nb |  UNKNOWN: GRB | 0 | 3.05 | 0 | P
178 | 2014_139 |  2014-02-18 23:06:28 | 172.04 |  n0 n1 n2 n5 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | $>10$ | R
179 | 2014_140 |  2014-02-19 00:32:33 | 12.29 |  n0 n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | 8.24 | 0 | R
182 | 2014_144 |  2014-02-19 22:57:13 | 118.79 |  n0 n1 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
183 | 2014_145 |  2014-02-19 23:49:23 | 90.11 |  n4 |  UNKNOWN: UNC(LP)/TGF | $>10$ | 9.12 | 0 | P
184 | 2014_147 |  2014-02-20 12:25:01 | 12.29 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
185 | 2014_148 |  2014-02-20 22:48:09 | 126.98 |  n0 n1 n2 n3 n4 n5 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
186 | 2014_149 |  2014-02-21 00:48:43 | 45.06 |  n2 n3 n5 n6 n8 n9 nb |  UNKNOWN: GRB/GF | 0 | $>10$ | 9.67 | R
187 | 2014_150 |  2014-02-21 01:03:36 | 270.34 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 na |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
188 | 2014_151 |  2014-02-21 22:39:25 | 4.10 |  n9 |  UNKNOWN: GRB | 0 | 3.28 | 0 | P
189 | 2014_152 |  2014-02-22 00:37:51 | 155.65 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | 0 | $>10$ | $>10$ | R
190 | 2014_153 |  2014-02-23 06:02:32 | 401.42 |  n0 n1 n2 n3 n4 n5 n6 n7 |  UNKNOWN: SF | $>10$ | $>10$ | $>10$ | R
192 | 2014_155 |  2014-02-23 18:14:59 | 503.82 |  n0 n1 n3 n4 n5 n8 n9 |  UNKNOWN: SF | $>10$ | $>10$ | 9.67 | R
193 | 2014_156 |  2014-02-23 19:49:32 | 589.83 |  n0 n1 n2 n3 n4 n5 n7 n8 |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
194 | 2014_157 |  2014-02-23 20:38:16 | 77.83 |  n2 na |  UNKNOWN: TGF/GRB | 7.96 | 8.92 | 0 | R
195 | 2014_158 |  2014-02-23 21:28:07 | 606.22 |  n0 n1 n3 n4 n5 n8 |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
196 | 2014_159 |  2014-02-23 21:38:17 | 32.77 |  n3 n4 n5 n8 |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
197 | 2014_160 |  2014-02-23 22:19:39 | 139.27 |  n0 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
199 | 2014_163 |  2014-02-24 09:37:28 | 8.19 |  n0 n1 n2 n3 n4 n5 |  UNKNOWN: SF | $>10$ | 5.75 | 0 | R
200 | 2014_164 |  2014-02-24 11:15:00 | 135.17 |  n2 n4 n5 |  UNKNOWN: SF | $>10$ | $>10$ | 0 | R
203 | 2014_167 |  2014-02-24 22:10:05 | 126.98 |  n0 n1 n2 n4 n5 n8 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
210 | 2014_176 |  2014-02-25 22:20:16 | 81.92 |  n2 n5 n6 n8 n9 na nb |  UNKNOWN: UNC(LP) | 0 | $>10$ | $>10$ | R
211 | 2014_177 |  2014-02-25 22:33:47 | 131.07 |  n4 n5 |  UNKNOWN: GF/GRB | 7.63 | 9.78 | 4.88 | R
216 | 2014_184 |  2014-02-27 19:09:13 | 942.09 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 |  UNKNOWN: SF | $>10$ | $>10$ | $>10$ | R
217 | 2014_185 |  2014-02-27 19:58:42 | 204.80 |  n1 n2 n4 n7 n8 n9 na nb |  UNKNOWN: GRB/TGF | $>10$ | $>10$ | 0 | R
218 | 2014_186 |  2014-02-27 20:48:00 | 131.07 |  n0 n1 n2 n3 n4 n5 n6 |  UNKNOWN: SF | $>10$ | $>10$ | 8.03 | R
219 | 2014_187 |  2014-02-27 20:50:23 | 593.93 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 |  UNKNOWN: SF | $>10$ | $>10$ | $>10$ | R
220 | 2014_189 |  2014-02-27 22:25:42 | 581.64 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 |  UNKNOWN: SF | $>10$ | $>10$ | 9.82 | R
221 | 2014_190 |  2014-02-27 23:15:57 | 499.72 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | $>10$ | R
222 | 2014_191 |  2014-02-27 23:25:46 | 8.19 |  n0 n1 n2 n3 n4 n5 n8 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 7.14 | R
224 | 2014_193 |  2014-02-28 20:25:58 | 4.10 |  n0 |  UNKNOWN: GRB | 0 | 3.68 | 0 | P
225 | 2019_0 |  2019-03-01 09:28:28 | 8.19 |  n6 |  UNKNOWN: UNC(LP) | 0 | 3.63 | 0 | P
228 | 2019_3 |  2019-03-06 06:45:30 | 77.83 |  n1 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 7.49 | R
234 | 2019_9 |  2019-03-15 05:09:11 | 61.44 |  n6 n9 |  UNKNOWN: GRB | 7.62 | 8.71 | 0 | R
236 | 2019_11 |  2019-03-17 01:08:06 | 12.29 |  nb |  UNKNOWN: GRB | 3.97 | 3.84 | 0 | P
246 | 2019_21 |  2019-03-27 06:36:04 | 8.19 |  n1 |  UNKNOWN: GRB | 0 | 3.4 | 0 | P
249 | 2019_24 |  2019-04-04 12:24:17 | 49.15 |  n0 n1 n2 n3 n5 |  UNKNOWN: SF/GRB | 9.19 | $>10$ | 0 | R
250 | 2019_25* |  2019-04-04 13:08:07 | 8.19 |  n9 na |  UNKNOWN: GRB | 0 | 4.93 | 0 | S
251 | 2019_26 |  2019-04-04 13:45:40 | 102.40 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
261 | 2019_36 |  2019-04-20 15:08:24 | 69.63 |  n4 n8 |  UNKNOWN: GRB/GF | 0 | 9.75 | 0 | S
262 | 2019_37* |  2019-04-20 22:32:56 | 16.38 |  n6 n7 n8 n9 nb |  UNKNOWN: GRB | $>10$ | 8.4 | 0 | R
266 | 2019_42 |  2019-04-28 00:16:26 | 61.44 |  n0 n1 n2 n3 n5 n6 n7 n8 n9 nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
275 | 2019_51 |  2019-05-07 17:15:13 | 7.42 |  na |  UNKNOWN: TGF | 0 | 4.85 | 0 | P
282 | 2019_58 |  2019-05-14 07:38:41 | 8.19 |  na |  UNKNOWN: TGF | 0 | 3.09 | 0 | P
283 | 2019_59 |  2019-05-14 10:31:29 | 196.61 |  n0 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
284 | 2019_60 |  2019-05-14 11:59:58 | 507.91 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
285 | 2019_61 |  2019-05-14 13:45:47 | 569.35 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
287 | 2019_63 |  2019-05-15 16:04:20 | 16.38 |  n4 |  UNKNOWN: GRB | 4.9 | 4.03 | 0 | P
290 | 2019_66 |  2019-05-20 05:34:01 | 20.48 |  n7 n9 na nb |  UNKNOWN: GRB | 5.61 | 8.58 | 0 | R
292 | 2019_68 |  2019-05-24 11:15:53 | 24.58 |  n3 n7 |  UNKNOWN: GRB | 0 | 6.04 | 0 | S
293 | 2019_69 |  2019-05-28 23:32:56 | 16.38 |  n4 |  UNKNOWN: GRB/TGF | 3.54 | 3.91 | 0 | P
301 | 2019_77* |  2019-06-06 13:21:42 | 16.38 |  n7 n8 nb |  UNKNOWN: GRB/GF | 9.12 | 6.4 | 0 | R
304 | 2019_80 |  2019-06-08 20:22:43 | 8.19 |  n0 n1 n2 n3 n4 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
305 | 2019_81 |  2019-06-08 20:23:03 | 626.70 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  UNKNOWN: UNC(LP) | $>10$ | $>10$ | 0 | R
318 | 2019_94 |  2019-06-28 04:23:34 | 8.19 |  n8 |  UNKNOWN: GRB | 0 | 3.4 | 0 | P
320 | 2019_96 |  2019-07-02 15:52:26 | 8.19 |  n3 |  UNKNOWN: GRB | 0 | 3.15 | 0 | P
Table 11:  Catalog of the known events for the three periods analysed. # is an incremental identifier for the events, ID is composed by the referring period and the identifier for its period (YYYY_ID). In catalog_triggers are reported the name of the event in the Fermi/GBM trigger catalog. $S_{r0}$, $S_{r1}$, $S_{r2}$, are the consistency of the signal for the energy range r0, r1 and r2, respectively. In the last column CE is reported the classification of the event, Robust (R) where more than 1 detector and more than 1 energy range is triggered, Solid (S) more than 1 detector but only 1 energy range is triggered, Probable (P) in all other cases. # | ID |  Trigger time | T (s) |  Detectors triggered |  Catalog trigger name | S r0 | S r1 | S r2 | CE
---|---|---|---|---|---|---|---|---|---
0 | 2010_0 |  2010-11-01 21:34:05 | 34.81 |  n3 n4 n5 |  GRB101101899 | 5.54 | 8.51 | 0 | R
2 | 2010_2 |  2010-11-02 20:10:09 | 41.47 |  n6 n7 n8 n9 nb |  GRB101102840 | 5.9 | $>10$ | 0 | R
3 | 2010_3 |  2010-11-03 12:13:28 | 114.69 |  n0 n1 n2 n3 n4 n5 n9 na nb |  SFLARE10110350 | $>10$ | $>10$ | 0 | R
4 | 2010_4 |  2010-11-04 19:26:16 | 4.43 |  n2 |  GRB101104810 | 0 | 3.22 | 0 | P
5 | 2010_5 |  2010-11-07 00:16:24 | 341.51 |  n0 n1 n3 n4 n5 |  GRB101107011 | 8.84 | $>10$ | 0 | R
8 | 2010_8 |  2010-11-12 22:10:31 | 17.92 |  n2 n5 |  GRB101112924 | 5.36 | 7.77 | 0 | R
9 | 2010_9 |  2010-11-12 23:36:52 | 96.26 |  n3 n6 n7 n8 |  GRB101112984 | $>10$ | $>10$ | 0 | R
11 | 2010_11 |  2010-11-13 11:35:33 | 202.24 |  n0 n1 n2 n6 n7 n8 n9 na nb |  GRB101113483 | $>10$ | $>10$ | 0 | R
12 | 2010_12 |  2010-11-14 17:53:19 | 229.38 |  n2 n4 n5 n6 n7 n9 na nb |  TRANSNT1011147 | $>10$ | $>10$ | 0 | R
13 | 2010_13 |  2010-11-17 11:54:42 | 108.54 |  n6 n7 n8 na nb |  GRB101117496 | $>10$ | $>10$ | 4.04 | R
15 | 2010_15 |  2010-11-23 22:52:10 | 204.80 |  n0 n1 n2 n5 n6 n7 n8 n9 na nb |  GRB101123952 | $>10$ | $>10$ | $>10$ | R
16 | 2010_16 |  2010-11-26 04:44:26 | 112.13 |  n3 n4 n5 n6 n7 n8 n9 na nb |  GRB101126198 | $>10$ | $>10$ | 7.12 | R
17 | 2010_18 |  2010-11-27 02:27:34 | 45.06 |  n6 n7 n9 nb |  GRB101127102 | 6.34 | $>10$ | 0 | R
18 | 2010_19 |  2010-12-01 10:01:49 | 108.03 |  n0 n1 n2 n4 n5 n7 n8 n9 na nb |  GRB101201418 | $>10$ | $>10$ | 4.87 | R
19 | 2010_20 |  2010-12-06 00:52:19 | 61.70 |  n0 n1 n2 n9 na |  GRB101206036 | 7.17 | $>10$ | 0 | R
20 | 2010_21 |  2010-12-07 12:51:46 | 94.21 |  n0 n1 n2 n3 n4 n5 n6 n7 |  GRB101207536 | $>10$ | $>10$ | 0 | R
21 | 2010_22 |  2010-12-13 20:22:28 | 12.58 |  n0 n1 n3 n5 |  GRB101213849 | 7.4 | 7.26 | 0 | R
22 | 2010_23 |  2010-12-14 23:50:09 | 4.10 |  n4 |  GRB101214993 | 0 | 3.12 | 0 | P
23 | 2010_24 |  2010-12-16 17:17:48 | 43.30 |  n0 n1 n2 n3 n4 n5 |  GRB101216721 | $>10$ | $>10$ | 0 | R
24 | 2010_25 |  2010-12-19 16:28:12 | 30.21 |  n3 n4 n6 n7 n8 |  GRB101219686 | $>10$ | $>10$ | 0 | R
25 | 2010_26 |  2010-12-20 13:50:02 | 106.50 |  n0 n1 n2 n3 n4 n5 |  GRB101220576 | $>10$ | $>10$ | 0 | R
26 | 2010_27 |  2010-12-20 20:44:14 | 45.06 |  n3 n4 n5 n6 n7 n8 |  GRB101220864 | $>10$ | $>10$ | 0 | R
27 | 2010_28 |  2010-12-23 20:00:17 | 30.21 |  n3 n4 |  GRB101223834 | 4.65 | 7.02 | 0 | R
28 | 2010_29 |  2010-12-24 13:53:00 | 57.41 |  n0 n1 n2 n3 n5 |  GRB101224578 | $>10$ | $>10$ | 0 | R
29 | 2010_30 |  2010-12-24 14:43:32 | 30.21 |  n2 n5 |  GRB101224614 | 7.59 | 6.51 | 0 | R
30 | 2010_31 |  2010-12-25 09:04:12 | 106.50 |  n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB101225377 | $>10$ | $>10$ | 0 | R
31 | 2010_32 |  2010-12-27 09:45:08 | 168.07 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB101227406 | $>10$ | $>10$ | 0 | R
32 | 2010_33 |  2010-12-27 12:51:44 | 58.88 |  n6 n7 n8 n9 nb |  GRB101227536 | 0 | $>10$ | 0 | S
34 | 2010_35 |  2010-12-31 01:36:48 | 108.03 |  n0 n1 n2 n6 n7 n8 n9 na nb |  GRB101231067 | $>10$ | $>10$ | 0 | R
35 | 2010_36 |  2011-01-05 21:02:32 | 134.15 |  n0 n1 n6 n7 n8 n9 na nb |  GRB110105877 | $>10$ | $>10$ | 0 | R
36 | 2010_37 |  2011-01-06 21:26:18 | 25.09 |  n9 na nb |  GRB110106893 | 0 | 8.16 | 0 | S
37 | 2010_39 |  2011-01-07 21:15:03 | 146.43 |  n0 n1 n2 n5 |  GRB110107886 | $>10$ | $>10$ | 0 | R
39 | 2010_41 |  2011-01-17 15:01:25 | 46.59 |  n6 n7 n9 na nb |  GRB110117626 | 0 | $>10$ | 0 | S
40 | 2010_42 |  2011-01-19 22:20:59 | 214.53 |  n0 n1 n2 n3 n4 n5 n6 n7 na |  GRB110119931 | $>10$ | $>10$ | 0 | R
41 | 2010_43 |  2011-01-20 15:59:35 | 211.14 |  n0 n1 n2 n4 n5 n6 n7 n8 n9 na nb |  GRB110120666 | 7.87 | $>10$ | 9.93 | R
42 | 2010_46 |  2011-01-23 19:17:42 | 386.57 |  n0 n1 n2 n3 n4 n5 n6 n7 na |  GRB110123804 | $>10$ | $>10$ | $>10$ | R
43 | 2010_49 |  2011-01-30 05:31:55 | 12.80 |  n7 n8 nb |  GRB110130230 | 4.24 | 6.14 | 0 | R
45 | 2010_51 |  2011-02-01 09:35:09 | 17.92 |  n4 |  GRB110201399 | 0 | 4.4 | 0 | P
46 | 2010_52 |  2011-02-04 04:17:10 | 38.40 |  n0 n3 n4 n5 |  GRB110204179 | 7.9 | $>10$ | 0 | R
49 | 2010_55 |  2011-02-05 14:07:16 | 161.79 |  n5 |  GRB110205588 | 0 | 6.05 | 0 | P
51 | 2010_57 |  2011-02-07 11:17:51 | 16.38 |  n3 n4 |  GRB110207470 | 0 | 5.23 | 0 | S
53 | 2010_60 |  2011-02-10 06:56:19 | 225.28 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 nb |  SFLARE11021028 | $>10$ | $>10$ | 0 | R
54 | 2010_61 |  2011-02-13 05:17:13 | 98.82 |  n1 n2 n3 n4 n5 n6 n7 n8 na nb |  GRB110213220 | $>10$ | $>10$ | 0 | R
55 | 2010_62 |  2011-02-13 17:33:04 | 584.52 |  n0 n1 n2 n3 n4 n5 n6 na |  SFLARE11021373 | $>10$ | $>10$ | $>10$ | R
57 | 2010_64 |  2011-02-14 17:24:26 | 540.68 |  n0 n1 n2 n3 n4 n5 n6 |  SFLARE11021472 | $>10$ | $>10$ | 5.33 | R
58 | 2010_65 |  2011-02-14 19:26:52 | 237.57 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE11021481 | $>10$ | $>10$ | 0 | R
59 | 2010_66 |  2011-02-15 01:48:19 | 447.50 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE11021507 | $>10$ | $>10$ | $>10$ | R
60 | 2010_68 |  2011-02-15 03:09:16 | 61.44 |  n1 n2 n3 n4 n5 na nb |  SFLARE11021513 | $>10$ | $>10$ | 0 | R
61 | 2010_69 |  2011-02-15 07:57:42 | 106.50 |  n0 n1 n2 n3 n4 n5 n8 |  SFLARE11021533 | $>10$ | $>10$ | 0 | R
64 | 2010_72 |  2011-02-16 01:36:25 | 114.69 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE11021606 | $>10$ | $>10$ | 0 | R
65 | 2010_73 |  2011-02-16 07:41:09 | 155.65 |  n2 n4 n5 |  SFLARE11021631 | $>10$ | 5.46 | 0 | R
66 | 2010_74 |  2011-02-16 14:23:21 | 164.00 |  n0 n1 n2 n3 n4 n5 n7 n8 n9 na nb |  SFLARE11021660 | $>10$ | $>10$ | 2.48 | R
68 | 2010_76 |  2011-02-17 12:35:08 | 40.96 |  n0 n1 n2 n3 n4 n5 n9 na nb |  SFLARE11021752 | $>10$ | 8.17 | 0 | R
69 | 2010_77 |  2011-02-17 14:10:53 | 12.29 |  n1 na |  GRB110217591 | 0 | 4.74 | 0 | S
70 | 2010_78 |  2011-02-18 06:30:43 | 117.76 |  n0 n1 n2 n3 n4 n5 n9 na nb |  SFLARE11021827 | $>10$ | $>10$ | 0 | R
74 | 2014_1 |  2014-01-01 18:44:35 | 445.44 |  n0 n1 n2 n3 n4 n5 n9 na |  SFLARE14010178 | $>10$ | $>10$ | $>10$ | R
75 | 2014_2 |  2014-01-01 23:10:59 | 20.48 |  n0 n1 n2 n3 n4 n5 n8 |  SFLARE14010196 | $>10$ | 6.91 | 0 | R
76 | 2014_3 |  2014-01-02 02:02:21 | 22.50 |  n0 n1 n2 n3 n4 n5 |  SFLARE14010208 | $>10$ | 7.24 | 0 | R
77 | 2014_4 |  2014-01-02 02:29:16 | 302.08 |  n0 n1 n2 n3 n4 n5 n7 n8 n9 nb |  SFLARE14010210 | $>10$ | $>10$ | 0 | R
78 | 2014_5 |  2014-01-02 05:44:45 | 113.66 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 nb |  SFLARE14010224 | $>10$ | $>10$ | 0 | R
79 | 2014_6 |  2014-01-02 08:43:28 | 89.09 |  n0 n1 n2 n3 n4 n5 n8 nb |  SFLARE14010236 | $>10$ | $>10$ | 0 | R
80 | 2014_7 |  2014-01-02 21:17:34 | 108.51 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB140102887 | $>10$ | $>10$ | 8.43 | R
81 | 2014_8 |  2014-01-02 21:43:18 | 102.40 |  n0 n1 n2 n3 n4 n5 n7 n8 |  SFLARE14010290 | $>10$ | $>10$ | 0 | R
82 | 2014_12 |  2014-01-03 02:25:57 | 45.06 |  n0 n1 n2 n3 n4 n5 n7 n8 |  SFLARE14010310 | $>10$ | 9.01 | 0 | R
83 | 2014_13 |  2014-01-03 03:58:56 | 102.40 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 nb |  SFLARE14010316 | $>10$ | $>10$ | 0 | R
84 | 2014_14 |  2014-01-03 12:48:39 | 45.25 |  n2 n4 n5 n9 na |  SFLARE14010353 | $>10$ | 9.64 | 7.17 | R
85 | 2014_15 |  2014-01-03 15:17:03 | 12.29 |  n1 n2 n3 n4 n5 |  SFLARE14010363 | $>10$ | 7.59 | 0 | R
86 | 2014_16 |  2014-01-03 20:06:11 | 108.03 |  n0 n1 n2 n3 n4 n5 |  SFLARE14010383 | $>10$ | $>10$ | 0 | R
87 | 2014_17 |  2014-01-03 21:11:59 | 113.66 |  n0 n1 n2 n3 n4 n5 n8 na nb |  SFLARE14010388 | $>10$ | $>10$ | 0 | R
88 | 2014_18 |  2014-01-04 10:19:09 | 401.41 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 nb |  SFLARE14010443 | $>10$ | $>10$ | $>10$ | R
89 | 2014_20 |  2014-01-04 17:32:08 | 594.45 |  n2 n3 n5 n6 n7 n8 n9 na nb |  GRB140104731 | $>10$ | $>10$ | 0 | R
90 | 2014_21 |  2014-01-04 19:05:40 | 121.86 |  n0 n1 n2 n3 n4 n5 |  SFLARE14010479 | $>10$ | $>10$ | 0 | R
91 | 2014_22 |  2014-01-04 19:08:02 | 1522.65 |  n0 n1 n2 n3 n4 n5 n9 na |  SFLARE14010479 | $>10$ | $>10$ | $>10$ | R
92 | 2014_23 |  2014-01-04 19:33:37 | 593.93 |  n0 n1 n3 n5 |  SFLARE14010479 | $>10$ | $>10$ | $>10$ | R
93 | 2014_24 |  2014-01-04 19:45:42 | 393.22 |  n0 n1 n3 n4 n5 |  SFLARE14010479 | $>10$ | $>10$ | 0 | R
94 | 2014_25 |  2014-01-04 19:57:31 | 208.90 |  n4 n5 |  SFLARE14010479 | $>10$ | 9.54 | 0 | R
96 | 2014_27 |  2014-01-05 01:33:03 | 8.30 |  n9 |  GRB140105065 | 0 | 3.49 | 0 | P
99 | 2014_31 |  2014-01-06 08:16:45 | 28.93 |  n2 n5 |  GRB140106345 | 5.2 | 7.69 | 0 | R
100 | 2014_32 |  2014-01-06 22:45:12 | 145.26 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE14010694 | $>10$ | $>10$ | 7.23 | R
103 | 2014_36 |  2014-01-07 22:18:30 | 45.06 |  n0 n1 n2 n3 n4 n5 n8 nb |  SFLARE14010792 | $>10$ | 9.58 | 0 | R
104 | 2014_37 |  2014-01-08 03:06:45 | 86.02 |  n0 n1 n2 n3 n4 n5 n7 n8 na nb |  SFLARE14010813 | $>10$ | $>10$ | 0 | R
105 | 2014_38 |  2014-01-08 12:57:46 | 55.22 |  n1 n2 n3 n4 n5 n8 |  SFLARE14010854 | $>10$ | $>10$ | 5.97 | R
106 | 2014_40 |  2014-01-08 17:18:45 | 184.58 |  n0 n1 n2 n3 n4 n5 n7 n8 n9 na nb |  GRB140108721 | $>10$ | $>10$ | 7.65 | R
109 | 2014_52 |  2014-01-10 06:18:34 | 63.10 |  n6 n7 n8 n9 na nb |  GRB140110263 | 6.29 | $>10$ | 7.6 | R
110 | 2014_53 |  2014-01-10 19:31:26 | 72.71 |  n8 |  GRB140110814 | 7.09 | 8.55 | 0 | P
115 | 2014_58 |  2014-01-13 04:23:54 | 22.02 |  n7 n9 nb |  GRB140113183 | 6.92 | 5.79 | 0 | R
116 | 2014_60 |  2014-01-13 15:04:12 | 40.96 |  n0 n1 n2 n3 n4 n5 n8 |  GRB140113624 | $>10$ | $>10$ | 0 | R
119 | 2014_63 |  2014-01-15 20:43:17 | 71.17 |  n2 n3 n4 n5 n6 n7 n8 |  GRB140115863 | 9.5 | $>10$ | 0 | R
121 | 2014_65 |  2014-01-17 16:06:58 | 110.59 |  n0 n1 n2 n3 n4 n5 n8 |  SFLARE14011767 | $>10$ | $>10$ | 0 | R
128 | 2014_74 |  2014-01-22 14:19:45 | 9.73 |  n8 |  GRB140122597 | 0 | 3.35 | 0 | P
129 | 2014_76 |  2014-01-24 12:38:24 | 171.01 |  n0 n1 n2 n3 n4 n5 n6 n9 |  GRB140124527 | $>10$ | $>10$ | 9.56 | R
132 | 2014_79 |  2014-01-26 19:32:48 | 130.05 |  n3 n4 n6 n7 n8 |  GRB140126815 | $>10$ | $>10$ | 0 | R
133 | 2014_80 |  2014-01-27 02:04:16 | 129.02 |  n0 n1 n2 n3 n4 n5 n7 n8 n9 na nb |  SFLARE14012708 | $>10$ | $>10$ | 0 | R
135 | 2014_82 |  2014-01-28 00:34:02 | 13.82 |  n0 n1 n2 n3 n4 n5 n7 n8 nb |  SFLARE14012802 | $>10$ | $>10$ | 0 | R
137 | 2014_85 |  2014-01-28 05:27:05 | 112.13 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 nb |  SFLARE14012822 | $>10$ | $>10$ | 0 | R
138 | 2014_86 |  2014-01-28 11:34:34 | 287.20 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE14012848 | $>10$ | $>10$ | 4.64 | R
139 | 2014_87 |  2014-01-28 11:45:17 | 20.48 |  n0 n1 n2 n3 n4 n5 |  SFLARE14012848 | $>10$ | 7.08 | 0 | R
141 | 2014_89 |  2014-01-28 12:57:05 | 69.63 |  n0 n2 n5 n7 n9 nb |  SFLARE14012853 | 0 | $>10$ | 9.72 | R
142 | 2014_90 |  2014-01-28 22:15:33 | 557.07 |  n0 n1 n2 n3 n4 n5 |  SFLARE14012892 | $>10$ | $>10$ | 7.83 | R
143 | 2014_91 |  2014-01-29 03:33:07 | 36.86 |  n0 n1 n2 n3 n4 n5 n7 n8 nb |  SFLARE14012914 | $>10$ | $>10$ | 0 | R
144 | 2014_92 |  2014-01-29 09:40:45 | 16.86 |  n0 n1 n2 n3 n4 n5 nb |  SFLARE14012940 | $>10$ | 7.25 | 0 | R
146 | 2014_94 |  2014-01-30 06:16:33 | 13.82 |  n0 n1 n2 n3 n4 n5 na |  SFLARE14013026 | $>10$ | 7.88 | 0 | R
147 | 2014_95 |  2014-01-30 06:35:48 | 499.72 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE14013027 | $>10$ | $>10$ | $>10$ | R
148 | 2014_96 |  2014-01-30 06:51:07 | 41.28 |  n1 n2 n3 n4 n5 |  SFLARE14013028 | $>10$ | 7.65 | 0 | R
149 | 2014_97 |  2014-01-30 07:52:24 | 572.43 |  n0 n1 n2 n3 n4 n5 n7 n8 n9 na nb |  SFLARE14013033 | $>10$ | $>10$ | 8.18 | R
150 | 2014_98 |  2014-01-30 08:02:25 | 195.61 |  n0 n1 n2 n3 n4 n5 na |  SFLARE14013033 | $>10$ | $>10$ | 7.42 | R
153 | 2014_102 |  2014-02-01 10:45:09 | 32.77 |  n0 n1 n2 n3 n4 n5 n9 na nb |  SFLARE14020144 | $>10$ | 8.37 | 0 | R
154 | 2014_103 |  2014-02-01 18:23:09 | 30.72 |  n1 n2 n3 n4 n5 |  SFLARE14020176 | $>10$ | $>10$ | 0 | R
156 | 2014_105 |  2014-02-13 06:05:07 | 156.11 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE14021325 | $>10$ | $>10$ | 7.81 | R
157 | 2014_106 |  2014-02-13 15:54:50 | 187.40 |  n0 n1 n2 n3 n4 n5 |  SFLARE14021366 | $>10$ | $>10$ | 5.78 | R
158 | 2014_107 |  2014-02-13 19:21:29 | 562.70 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB140213807 | $>10$ | $>10$ | $>10$ | R
159 | 2014_109 |  2014-02-13 19:33:55 | 1326.10 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE14021382 | $>10$ | $>10$ | $>10$ | R
161 | 2014_117 |  2014-02-14 03:04:13 | 221.36 |  n0 n1 n2 n3 n4 n5 |  SFLARE14021412 | $>10$ | $>10$ | 0 | R
162 | 2014_118 |  2014-02-14 04:27:53 | 28.67 |  n0 n1 n2 n3 n4 n5 n9 na nb |  SFLARE14021418 | $>10$ | 7.06 | 0 | R
163 | 2014_119 |  2014-02-14 12:33:52 | 537.64 |  n0 n1 n2 n3 n4 n5 n7 n8 |  SFLARE14021452 | $>10$ | $>10$ | 8.86 | R
164 | 2014_120 |  2014-02-14 16:35:36 | 335.88 |  n0 n1 n2 n3 n4 n5 |  SFLARE14021469 | $>10$ | $>10$ | 0 | R
172 | 2014_130 |  2014-02-16 13:50:59 | 111.10 |  n0 n1 n2 n3 n4 n5 n9 na nb |  SFLARE14021657 | $>10$ | $>10$ | 0 | R
173 | 2014_131 |  2014-02-17 01:01:44 | 28.80 |  n0 n1 n3 n6 n9 |  GRB140217043 | $>10$ | 8.74 | 0 | R
176 | 2014_136 |  2014-02-18 10:08:43 | 449.54 |  n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB140218427 | $>10$ | $>10$ | $>10$ | R
180 | 2014_142 |  2014-02-19 19:46:34 | 61.70 |  n8 nb |  GRB140219824 | 7.55 | 8.7 | 0 | R
181 | 2014_143 |  2014-02-19 22:22:20 | 41.05 |  n0 n1 n2 n3 n4 n5 |  SFLARE14021993 | $>10$ | $>10$ | 0 | R
191 | 2014_154 |  2014-02-23 11:53:08 | 41.22 |  n5 n6 n7 n8 n9 nb |  GRB140223495 | 0 | $>10$ | 0 | S
198 | 2014_162 |  2014-02-24 09:10:19 | 4.54 |  n2 |  GRB140224382 | 0 | 3.55 | 0 | P
201 | 2014_165 |  2014-02-24 18:55:19 | 38.40 |  n3 n4 n6 n7 n8 |  GRB140224788 | 6.35 | $>10$ | 0 | R
202 | 2014_166 |  2014-02-24 21:35:34 | 116.74 |  n0 n1 n2 n3 n4 n5 n8 na |  SFLARE14022490 | $>10$ | $>10$ | 0 | R
204 | 2014_168 |  2014-02-25 00:41:27 | 1612.29 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFLARE14022502 | $>10$ | $>10$ | $>10$ | R
205 | 2014_169 |  2014-02-25 01:08:27 | 495.63 |  n0 n1 n2 n3 n4 n5 n9 |  SFLARE14022502 | $>10$ | $>10$ | 5.21 | R
206 | 2014_170 |  2014-02-25 01:16:59 | 434.18 |  n0 n1 n3 n5 |  SFLARE14022502 | $>10$ | $>10$ | $>10$ | R
207 | 2014_171 |  2014-02-25 01:27:26 | 294.92 |  n0 n1 n2 n3 n4 n5 |  SFLARE14022502 | $>10$ | $>10$ | 6.64 | R
208 | 2014_173 |  2014-02-25 06:18:56 | 32.77 |  n1 n2 n3 n4 n5 |  SFLARE14022526 | $>10$ | 7.66 | 0 | R
209 | 2014_175 |  2014-02-25 15:06:38 | 106.82 |  n0 n1 n2 n3 n4 n5 n8 |  SFLARE14022563 | $>10$ | $>10$ | 0 | R
212 | 2014_180 |  2014-02-27 05:56:09 | 78.24 |  n0 n1 n2 n3 n4 n5 |  SFLARE14022724 | $>10$ | $>10$ | 0 | R
213 | 2014_181 |  2014-02-27 09:04:25 | 83.46 |  n0 n1 n2 n3 n4 n5 na |  SFLARE14022737 | $>10$ | $>10$ | 0 | R
214 | 2014_182 |  2014-02-27 10:44:42 | 106.59 |  n0 n1 n2 n3 n4 n5 |  SFLARE14022744 | $>10$ | $>10$ | 0 | R
215 | 2014_183 |  2014-02-27 17:43:06 | 13.82 |  n0 n3 n4 n5 na |  GRB140227738 | $>10$ | 4.99 | 0 | R
223 | 2014_192 |  2014-02-28 00:47:28 | 108.03 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  SFL140228033 | $>10$ | $>10$ | 0 | R
226 | 2019_1 |  2019-03-03 05:45:19 | 88.06 |  n0 n1 n2 n5 |  GRB190303240 | 8.96 | $>10$ | 0 | R
227 | 2019_2 |  2019-03-04 19:37:21 | 9.73 |  n4 |  GRB190304818 | 4.4 | 6.29 | 0 | P
229 | 2019_4 |  2019-03-06 22:37:42 | 185.86 |  n0 n1 n6 n7 n8 n9 na nb |  GRB190306943 | $>10$ | $>10$ | 0 | R
230 | 2019_5 |  2019-03-07 03:37:19 | 152.06 |  n0 n1 n2 n3 n4 n5 n9 na nb |  GRB190307151 | $>10$ | $>10$ | 0 | R
231 | 2019_6 |  2019-03-10 09:32:35 | 152.07 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190310398 | $>10$ | $>10$ | 0 | R
232 | 2019_7 |  2019-03-11 14:23:37 | 9.73 |  n8 |  GRB190311600 | 0 | 3.36 | 0 | P
233 | 2019_8 |  2019-03-12 10:42:13 | 29.18 |  n0 n1 n3 n4 |  GRB190312446 | $>10$ | $>10$ | 0 | R
235 | 2019_10 |  2019-03-15 12:17:44 | 106.75 |  n0 n1 n4 n5 n6 n7 n8 n9 na nb |  GRB190315512 | $>10$ | $>10$ | 4.57 | R
237 | 2019_12 |  2019-03-20 01:14:21 | 49.15 |  n6 n7 n8 n9 na nb |  GRB190320052 | 5.74 | $>10$ | 0 | R
238 | 2019_13 |  2019-03-21 14:42:05 | 45.42 |  n2 n4 n5 |  SFLARE19032161 | $>10$ | 7.83 | 0 | R
239 | 2019_14 |  2019-03-23 07:16:50 | 108.03 |  n0 n1 n6 n7 n8 n9 na nb |  GRB190323303 | $>10$ | $>10$ | 5.54 | R
240 | 2019_15 |  2019-03-23 21:05:18 | 113.15 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190323879 | $>10$ | $>10$ | $>10$ | R
241 | 2019_16 |  2019-03-24 08:21:13 | 114.69 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190324348 | $>10$ | $>10$ | 8.23 | R
242 | 2019_17 |  2019-03-24 22:44:17 | 106.50 |  n0 n2 n6 n7 n8 n9 na nb |  GRB190324947 | $>10$ | $>10$ | 6 | R
243 | 2019_18 |  2019-03-25 23:58:59 | 341.12 |  n0 n1 n2 n3 n4 n5 n9 na nb |  GRB190325999 | $>10$ | $>10$ | 6.87 | R
244 | 2019_19 |  2019-03-26 23:24:43 | 41.22 |  n0 n1 n6 n9 na |  GRB190326975 | 3.98 | $>10$ | 0 | R
245 | 2019_20 |  2019-03-27 02:39:13 | 45.57 |  n1 n2 n3 n4 n6 n7 n8 n9 na |  GRB190327111 | 0 | $>10$ | 6.32 | R
247 | 2019_22 |  2019-03-30 16:39:28 | 108.16 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190330694 | $>10$ | $>10$ | 8.91 | R
248 | 2019_23 |  2019-04-01 03:20:19 | 108.03 |  n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190401139 | $>10$ | $>10$ | 0 | R
252 | 2019_27 |  2019-04-06 10:47:22 | 4.61 |  n3 n4 |  GRB190406450 | 5.13 | 4.58 | 0 | R
253 | 2019_28 |  2019-04-06 17:52:31 | 103.94 |  n0 n2 n3 n4 n5 n6 n7 |  GRB190406745 | $>10$ | $>10$ | 6.23 | R
254 | 2019_29 |  2019-04-07 13:48:39 | 12.67 |  n3 n6 n9 |  GRB190407575 | 4.92 | 7.12 | 0 | R
255 | 2019_30 |  2019-04-07 16:07:29 | 20.99 |  n6 n7 n9 na |  GRB190407672 | 8 | 8.22 | 0 | R
256 | 2019_31 |  2019-04-08 12:51:22 | 363.53 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  LOCLPAR1904085 | $>10$ | $>10$ | 0 | R
257 | 2019_32 |  2019-04-11 09:45:46 | 22.02 |  n0 n1 n2 n6 n8 n9 na |  GRB190411407 | 0 | $>10$ | $>10$ | R
258 | 2019_33 |  2019-04-11 13:53:56 | 79.36 |  n0 n1 n2 n5 |  GRB190411579 | 8.76 | $>10$ | 0 | R
259 | 2019_34 |  2019-04-15 04:09:46 | 112.18 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190415173 | $>10$ | $>10$ | $>10$ | R
260 | 2019_35 |  2019-04-19 09:55:40 | 262.53 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190419414 | $>10$ | $>10$ | 6.32 | R
263 | 2019_38 |  2019-04-20 23:32:27 | 8.38 |  n6 |  GRB190420981 | 0 | 3.62 | 0 | P
264 | 2019_39 |  2019-04-22 16:05:09 | 20.48 |  na nb |  GRB190422670 | 0 | 6.94 | 0 | S
265 | 2019_41 |  2019-04-22 22:56:09 | 183.30 |  n6 n7 n8 nb |  GRB190422957 | 8.83 | $>10$ | 0 | R
267 | 2019_43 |  2019-04-28 18:48:11 | 62.98 |  n0 n1 n2 n3 n4 n5 na nb |  GRB190428783 | $>10$ | $>10$ | 0 | R
268 | 2019_44 |  2019-04-29 17:49:54 | 20.48 |  n9 na nb |  GRB190429743 | 0 | 6.94 | 0 | S
269 | 2019_45 |  2019-05-02 04:01:32 | 24.64 |  n0 n3 n4 n6 n7 |  GRB190502168 | 9.77 | 9.94 | 0 | R
270 | 2019_46 |  2019-05-04 09:57:36 | 20.99 |  n3 |  GRB190504415 | 0 | 4.44 | 0 | P
271 | 2019_47 |  2019-05-06 05:07:05 | 176.13 |  n0 n1 n2 n3 n4 n5 |  SFLARE19050621 | $>10$ | $>10$ | 0 | R
272 | 2019_48 |  2019-05-06 13:53:21 | 49.15 |  n0 n1 n2 n3 n4 n5 |  SFLARE19050657 | $>10$ | 3 | 0 | R
273 | 2019_49 |  2019-05-06 17:47:45 | 89.09 |  n1 n2 n3 n4 n5 |  SFLARE19050674 | $>10$ | $>10$ | 0 | R
274 | 2019_50 |  2019-05-07 06:28:19 | 47.62 |  n1 n8 n9 na |  GRB190507270 | 5.75 | 9.53 | 0 | R
276 | 2019_52 |  2019-05-07 23:16:29 | 108.03 |  n0 n1 n6 n7 n8 n9 na nb |  GRB190507970 | $>10$ | $>10$ | 0 | R
277 | 2019_53 |  2019-05-08 19:22:49 | 79.36 |  n6 n7 n8 nb |  GRB190508808 | 9.3 | $>10$ | 0 | R
278 | 2019_54 |  2019-05-09 05:44:02 | 299.01 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 na nb |  SFL190509239 | $>10$ | $>10$ | 0 | R
279 | 2019_55 |  2019-05-10 10:12:57 | 393.22 |  n1 n4 na |  GRB190510430 | 4.03 | 5.44 | 0 | R
280 | 2019_56 |  2019-05-11 07:14:26 | 107.01 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190511302 | $>10$ | $>10$ | 0 | R
281 | 2019_57 |  2019-05-12 14:40:04 | 20.48 |  n0 n3 |  GRB190512611 | 4.72 | 6.05 | 0 | R
286 | 2019_62 |  2019-05-14 21:41:40 | 12.29 |  n9 |  TGF190514901 | 6.65 | 5.19 | 0 | P
288 | 2019_64 |  2019-05-17 19:30:08 | 62.98 |  n0 n1 n7 n8 n9 na nb |  GRB190517813 | $>10$ | $>10$ | 0 | R
289 | 2019_65 |  2019-05-19 07:24:53 | 145.92 |  n0 n1 n2 n3 n4 n5 n7 n8 n9 na nb |  GRB190519309 | $>10$ | $>10$ | 0 | R
291 | 2019_67 |  2019-05-20 14:21:05 | 150.53 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  LOCLPAR1905205 | $>10$ | $>10$ | 0 | R
294 | 2019_70 |  2019-05-30 10:19:05 | 116.99 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190530430 | $>10$ | $>10$ | $>10$ | R
295 | 2019_71 |  2019-05-31 07:29:10 | 87.55 |  n0 n1 n2 n3 n4 n5 n9 |  GRB190531312 | $>10$ | $>10$ | 0 | R
296 | 2019_72 |  2019-05-31 20:10:02 | 126.98 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190531840 | $>10$ | $>10$ | $>10$ | R
297 | 2019_73 |  2019-06-03 19:04:31 | 12.29 |  n0 n3 n4 n5 |  GRB190603795 | 7.3 | 6.8 | 0 | R
298 | 2019_74 |  2019-06-04 10:42:33 | 108.80 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 |  GRB190604446 | $>10$ | $>10$ | $>10$ | R
299 | 2019_75 |  2019-06-05 23:22:29 | 16.58 |  n4 n7 n8 |  GRB190605974 | 0 | 6.33 | 0 | S
300 | 2019_76 |  2019-06-06 01:55:04 | 46.98 |  n0 n1 n2 n3 n4 n5 n9 na |  GRB190606080 | 6.96 | $>10$ | 5.45 | R
302 | 2019_78 |  2019-06-07 01:42:46 | 45.31 |  n6 n7 n9 na nb |  GRB190607071 | 8.88 | $>10$ | 0 | R
303 | 2019_79 |  2019-06-08 00:12:20 | 106.75 |  n6 n7 n8 n9 na nb |  GRB190608009 | 9.44 | $>10$ | 0 | R
306 | 2019_82 |  2019-06-08 20:35:08 | 400.39 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  LOCLPAR1906088 | $>10$ | $>10$ | 0 | R
307 | 2019_83 |  2019-06-09 07:33:36 | 138.24 |  n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190609315 | $>10$ | $>10$ | $>10$ | R
308 | 2019_84 |  2019-06-10 18:00:05 | 102.40 |  n0 n1 n2 n5 n6 n7 n8 n9 na nb |  GRB190610750 | $>10$ | $>10$ | 0 | R
309 | 2019_85 |  2019-06-11 22:47:46 | 178.05 |  n0 n1 n2 n3 n4 n5 n9 |  GRB190611950 | $>10$ | $>10$ | $>10$ | R
310 | 2019_86 |  2019-06-12 03:57:24 | 22.02 |  n4 n8 |  GRB190612165 | 4.6 | 5.27 | 0 | R
311 | 2019_87 |  2019-06-13 04:07:21 | 45.06 |  n0 n1 n3 n8 |  GRB190613172 | $>10$ | $>10$ | 0 | R
312 | 2019_88 |  2019-06-13 10:46:58 | 38.40 |  n3 n4 n5 n6 n7 n8 n9 nb |  GRB190613449 | 8.28 | $>10$ | 0 | R
313 | 2019_89 |  2019-06-15 15:16:26 | 79.36 |  n4 n6 n7 n8 n9 nb |  GRB190615636 | 8.23 | $>10$ | 0 | R
314 | 2019_90 |  2019-06-19 00:24:24 | 154.63 |  n0 n1 n3 n6 n9 |  GRB190619018 | $>10$ | $>10$ | 0 | R
315 | 2019_91 |  2019-06-19 14:15:55 | 146.43 |  n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190619595 | $>10$ | $>10$ | 5.83 | R
316 | 2019_92 |  2019-06-20 12:10:10 | 116.23 |  n0 n1 n2 n3 n4 n5 n6 n7 n8 n9 na nb |  GRB190620507 | $>10$ | $>10$ | $>10$ | R
317 | 2019_93 |  2019-06-26 06:06:24 | 4.61 |  n2 |  GRB190626254 | 3.18 | 3.12 | 0 | P
319 | 2019_95 |  2019-06-28 12:30:54 | 26.11 |  na nb |  GRB190628521 | 5.32 | 7.16 | 0 | R
321 | 2019_97 |  2019-07-07 06:50:04 | 108.03 |  n0 n2 n3 n4 n5 n6 n7 n8 nb |  GRB190707285 | $>10$ | $>10$ | 0 | R
322 | 2019_98 |  2019-07-07 07:22:58 | 30.72 |  n3 n4 |  GRB190707308 | 6.24 | 7.25 | 0 | R
323 | 2019_99 |  2019-07-08 08:45:14 | 16.89 |  n3 n4 n7 n8 |  GRB190708365 | 0 | 6.87 | 0 | S