Figure 4.21: Feature importance as determined by the Morris method for the
2019 dataset. Features are ranked by average importance, with the McIlwain
L-parameter and longitude coordinates at the top. Conversely, certain detector
flags indicating Earth’s FoV occultation emerge as almost irrelevant factors
in prediction. For a complete list of features and their descriptions, refer
to Tables 4.2 and 4.3. Figure 4.22: Feature importance as determined by the
Morris method for the 2014 dataset. Features are sorted by their average
importance, with the McIlwain L-parameter and longitude coordinates as the
most influential. In contrast to Figure 4.21, detector flags representing
Earth’s FoV occultation play a significant role in prediction. For a complete
list of features and their descriptions, refer to Tables 4.2 and 4.3.

####  4.7.2 Local

Local explanations focus on individual data points, and for this purpose,
Kernel SHAP has been utilized. It allows for the generation of explanations
that cover the entire NN, providing a feature importance list for each of the
36 outputs, aiding in debugging unusual predictions. For instance, Figure 4.24
showcases an unexpected peak for detector n8 in energy range r1. Kernel SHAP
in Figure 4.23 highlights the importance of the longitude feature which is
expressed in degrees, it gradually increases to $360^{\circ}$ and then returns
to $0^{\circ}$ as expected. However, due to a dataset issue (an interpolation
error in longitude estimation), the value jumps to around $100^{\circ}$,
invalidating the result. The solution involves rectifying the dataset, as the
NN strives to provide the best estimation possible. Nevertheless, this
behaviour has minimal impact on the trigger algorithm’s application.

Figure 4.23: Kernel SHAP feature importance for the peak datapoint. The
spacecraft’s geographical longitude is the most critical feature in
determining the sudden count rate increase from a baseline count rate of 346
to 398 (the peak). This baseline is constructed using 15 data points before
and after the peak, which are used for sampling values and perturbing the data
point to provide an explanation. Figure 4.24: Lightcurve of observed and
estimated count rates for detector n0 in energy range r1. An unexpected peak
appers in the estimated count rates due to an error in the dataset for the
longitude estimation.

Another significant error type, more severe due to its contribution to False
Positive detections during the 2014 period (discussed in the subsequent
chapter), involves sudden steps in the background estimation. Figure 4.26
illustrates this behaviour for detector n8 in energy range r1. In the SHAP
analysis (refer to Figure 4.25), the most influential features are related to
a flag feature: 1 indicates the detector is visible, while 0 signifies that
its FoV is obscured by the Earth. Interestingly, what stands out is that
detector n8’s behaviour seems influenced by the visibility of other detectors.
This peculiar pattern cannot be attributed to data errors, as their
correctness has been verified, but rather to a learned pattern within the
neural network. Figure 4.26 presents the values of these features,
demonstrating a clear correlation between the step behaviour and changes in
these flags.

Figure 4.25: Kernel SHAP feature importance for a datapoint in a step of the
estimated background in Figure 4.26. Notably, the most influential features
are those related to detector visibility, specifically whether the FoV is
obscured by Earth or not. The baseline for this method is established by using
15 datapoints before and after the datapoint to be explained. These datapoints
are used for value sampling and perturbation of the datapoint during the
explanation process. Figure 4.26: Observed and estimated count rate lightcurve
for detector n8 in energy range r1. The unexpected steps in estimated count
rates are attributed to variations in features such as n0_vis, n1_vis, and
n7_vis, with n8_vis remaining constant during this unusual estimation period.

A potential simple solution in this scenario could involve omitting these
features as inputs and evaluating whether model performance remains consistent
and this behaviour subsides. However, it’s worth noting that these features
might serve a purpose in correcting for solar activity effects. This contrasts
with the 2019 period, during which FoV occultation were deemed irrelevant in
background estimation (see Figure 4.21). Moreover, it is noted that the impact
of occultation on detectors facing the Sun (from n0 to n5), cause a decrease
in estimated count rates. In contrast, detectors from n6 to nb exhibit
behaviour similar to that depicted in Figure 4.26.

Another potential solution could be integrating these features in the last
layer only if it associated to the detector (e.g., n8_r1 count rate and
n8_vis).

###  4.8 Background for GRB simulation

In this last section of this chapter, we explore how the background estimation
can be integrated into a simulation framework designed for modeling GRB
events. For an in-depth understanding and detailed implementation, refer to
the repository available at GitHub Repository
https://github.com/peppedilillo/synthburst. The primary objective of this
simulation framework is to generate comprehensive lightcurves that faithfully
capture the behaviour of GRBs under various background scenarios.

The framework introduces the concept of "background templates" which serve as
representative models for background radiation. These templates are
characterized by specific attributes, including the reference for a part of
the satellite’s orbit, the detector used, and the energy range considered. By
varying these characteristics, a range of different environmental conditions
can be simulated. These templates are then utilized as inputs to a trigger
algorithm to assess its robustness under diverse background conditions. It’s
worth noting that these templates have a resolution of 4 s, as previously
explained. However, users also have the flexibility to sample an Exponential
distribution within their chosen bin size. This sampling enables the
generation of photon arrival times, effectively creating an equivalent TTE
object. Figure 4.27 provides examples of background templates, each
corresponding to different parts of the satellite’s orbit and specific
detector and energy range settings. These templates are drawn from data
collected during the 2019 observation period.

Figure 4.27: Background templates for different parts of the orbit,
corresponding to detector n3 and energy range r1, estimated from data on 19
April 2019.

GRB event data can be downloaded from the Fermi GBM catalog in TTE format,
allowing users to select from various types of GRBs, including short and long
bursts, faint and bright, or other significant characteristics sensitive to
the trigger algorithm. The simulation pipeline give in output the TTE data
from GRB events are merged with the TTE background templates. By specifying a
chosen temporal bin size, a simulation is generated, as illustrated in Figure
4.28.

Figure 4.28: Example of the simulation process, specifically showcasing the
simulation of GRB120707 with background templates from a specific part of the
satellite’s orbit (as indicated by #3 in Figure 4.27). This simulation is
performed for detector n3 in energy range r1, utilizing data from the 2019
observation period.

##  Chapter 5 Framework for high-energy transient detection and event analysis

###  5.1 FOCuS

The background estimates so obtained in Chapter 4 are compared against the
actual observations using Poisson-FOCuS. 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.

####  5.1.1 Description

An efficient change-point and anomaly detection algorithm called FOCuS-Poisson
(Functional Online CUSUM) [263] is employed to find anomalous transients in
Fermi GBM data relative to the NN estimates of the background.
The FOCuS-Poisson algorithm is executed sequentially over the time series of
the observed count rate 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 rate 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 (time
intervals, rows segments) where a trigger condition is verified (e.g., the
times when two detector-energy range combinations exceed a pre-set threshold).
The second step is to cluster together segments whose start and end times are
closer than a pre-defined value. This value is fiducially set to 600 s, a
duration large enough to capture most long GRBs and equal to the duration of
the Fermi-GBM TTE files [260].
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 count rates in
relation to the integral of background values, is greater than a minimum
$\mu_{\text{min}}$.

Figure 5.1: A diagram representing the trigger algorithm pipeline’s component.
A Poisson-FOCuS container is given in input two tables. The first input table
contains the NN’s background count rate prediction, while the second reports
the actual observations. The container outputs a table with same dimension as
the inputs, and values representing statistical significance in unit of
standard deviations. All tables share the same dimension and organization:
columns are used to represent different combinations of detectors and energy
ranges, while rows are used to represent different times. The output table is
searched for time intervals in which statistical significance exceeds a
threshold value over the energy range 50 - 300 keV (r1). Then, intervals close
in time and exceeding the threshold are clustered together. Finally, clustered
over-threshold intervals are reported in a list. Source: Crupi et al. [67].

####  5.1.2 Application on background estimate

With reference to the technique described in Section 5.1.1, 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 count rates for the significance level to exceed a
threshold $T=3\sigma$ over the range of energy spanning $50$ keV and $300$
keV. This choice was made to ensure comparability with the approach used by
the online search algorithm of Fermi-GBM and other major GRB monitoring
experiments [259, 149], as well as to filter out softer events such as solar
flares.

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.

A filter was applied to exclude data points within 150 seconds before and
after a SAA transit, specifically if the satellite remains in the SAA for at
least 500s. The purpose of filtering out data in proximity to the SAA is to
reduce false detections. This is necessary due to various factors including
the dynamic nature of the SAA environment, even on short time-scales [282],
the spacecraft’s apparent direct motion (Fermi enters the SAA at different
geographic locations during each orbit), and the presence of a discontinuity
in the observed data resulting from the instrument switch-off during the SAA
transit. These factors make estimating a reliable background count rate near
the SAA challenging, often resulting in an underestimation of the background
rate and, in turn, false detections by the trigger algorithm. Through
empirical analysis, we have determined that a filter duration of 150 seconds
is the minimum required to ensure accurate estimation of background count
rates. However, this precaution has the unfortunate consequence of preventing
the detection of transients that occur during these filtered periods.

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, 74 events match the
trigger time of events already in the Fermi/GBM Trigger Catalog [259], one
event is due to artifacts in the dataset, while the nature of the remaining 25
events is uncertain. These results, along other from the remaining test
periods, have been summarized in Table 5.1. Over the same period, the
Fermi/GBM Burst Catalog [259] 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 81 bursts (65 detected and 16
undetected), 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 5.4 and Table 5.2, the latter also reporting
on results from other periods.

To measure the significance of the events in Table 5.2 the Standard Score $z$
is computed:

$z=\frac{x-\mu}{\sigma}$ (5.1.1)

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:

$S=\frac{N-B}{\sqrt{B}}$ (5.1.2)

where $N$ is the observed count rates integrated over an interval spanning the
event’s start time and end time111To 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. $B$ is the total count rates comes from the background
estimated by the NN, over the same event time. 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:

$C=max(S_{r0},\;S_{r1},\;S_{r2}).$ (5.1.3)

Transient detection statistics
---
Period |  Total events |  Known |  Consistency median known |  Unknown |  Consistency median unknown |  False Detections
2010 |  81 |  55 |  $>10$ |  18 |  8.83 |  8
2014 |  195 |  81 |  $>10$ |  71 |  $>10$ |  43
2019 |  100 |  74 |  $>10$ |  25 |  8.71 |  1
Table 5.1:  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 |  Detected GRBs |  Undetected GRBs |  $T_{90}>4.096$ s |  $T_{90}<4.096$ s |  Missing (no data)
2010 |  41 |  25 |  39/52 (75 %) |  2/14 (14 %) |  11
2014 |  21 |  7 |  17/18 (94 %) |  4/10 (40 %) |  8
2019 |  65 |  16 |  60/68 (88 %) |  5/13 (34 %) |  15
Table 5.2:  Detected and undetected number of GRBs in the Fermi-GBM Burst
Catalog, fraction of detected bursts with duration greater than the bin-length
time resolution of the tested dataset, fraction of correctly detected bursts
with duration smaller than the bin-length time resolution of the tested
dataset. Each table row corresponds to a different time period. The last
column of the table displays the number of missing events in our dataset, due
to SAA data clipping (see Section 5.1.2). These missing events, along with the
counts of detected and undetected GRBs, contribute to the total number of GRBs
in the specific period as recorded in the Fermi-GBM Burst Catalog.

###  5.2 Event analysis

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 5.4.
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 5.2.

(a) (a) (a) Figure 5.4:  GRB detection performances. Each dot represents a
gamma-ray burst of the Fermi/GBM Burst Catalog discovered between March 1st
and July 1st 2019 (a), 1 November 2010 to 19 February 2011 (b), 1 January 2014
to 28 February 2014 (b) 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. Source: Crupi et al. [67].

####  5.2.1 Rules for classification

We also detect many events not present in the Fermi/GBM catalog, and we use
the methodology outlined in [149] to characterize these transients. More
specifically, we classify events as:

  * •

Solar flare (SF) when the majority of the count rates 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 count rates 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 count rates 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 Section 5.2.2.

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 5.3 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.

####  5.2.2 Localization

For the standard reference for the localization of events found by GBM, look
[114].
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
optimiser222https://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 5.2.1

$\mathcal{I}(\psi_{d},\psi_{s})=max(cos(\psi_{d},\psi_{s}),0)$ (5.2.1)

The loss to optimise in Equation 5.2.2, 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 5.2.2
and minimized.

$L_{\text{localization}}=\frac{\sum_{i=1}^{D}(counts_{s}(\mathcal{I}(\psi_{i},\psi_{s}))-counts_{i})^{2}}{D}$
(5.2.2)

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

###  5.3 Events highlight

We list in Table 5.3 a selection of interesting events, including the one
already discussed, which are not present in the GBM catalog and which deserve
further analysis on lightcurves plot and localization. The Fermi satellite’s
position and event localization were straightforward to plot using the Fermi
GBM data tools library [115].

##### 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 5.5) 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 5.5:  The Fermi/GBM catalog GRB190320, as detected by our method.
Photon count rates 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 4.1), 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). Source: Crupi et al. [67].

##### Event 5 - 190420939

Figure 5.6 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 5.6:  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 5.2.1. For the
corresponding localization see Figure 5.7. Photon count rates 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 4.1), 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). Source: Crupi et al. [67].

We can see from the localization estimate in Figure 5.7 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 5.2.2.

Figure 5.7:  Estimate of the candidate event’s source localization over the
celestial sphere at 2019-04-20 22:32:56 UTC. Source: Crupi et al. [67].

##### Event 1 - 140127222

This event has all the solar faced detectors triggered (n0, n1, n2, n3, n4 and
n5) in the range r0 and its location is close to the Sun therefore is
classified as Solar Flares, Figure 5.8.

Figure 5.8: Lightcurve and localization for event id 1. The Sun is located
under the purple spot. We classify this event as SF. Source: Crupi et al.
[67].

##### Event 2 - 101111791

Events 101111791 is classified as Solar Flares because three of the solar
faced detector are triggered (n2, n4 and n5) their location is close to the
Sun and the count rates are quite significant in the energy range r0, Figure
5.9.

Figure 5.9: Lightcurve and localization for event id 2. The Sun is located
under the purple spot. We classify this event as SF. Source: Crupi et al.
[67].

##### Event 3 - 140112583

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, Figure 5.10.

Figure 5.10: Lightcurve and localization for event id 3. Because of its
location near the galactic plane and the Earth’s horizon, we could classify
this event as TGF or GF. Source: Crupi et al. [67].

##### Event 4 - 190404542

Event 6 is categorized as GRBs for the same reasons as event 5, Figure 5.11.
According to the GCN Circular notice, GRB 190404B was detected by the Monitor
of all-sky X-ray image (MAXI) satellite [171] in an energy range below 20 keV,
thus outside the lower energy band r0. It was estimated to be located at
$(\text{RA}=221^{\circ},\text{Dec}=-22^{\circ})$, which is near the
localization of event 4. This event occurred at 13:14:34.00 UTC on April 4,
2019, approximately six minutes after event 4.

Figure 5.11: Lightcurve (top left) and localization (top right) for event id
4. We classify this event as a GRB. Source: Crupi et al. [67]. The bottom
panel shows the lightcurve in four energy bands, all below the energy range
involved in this work, for GRB 190404B http://maxi.riken.jp/grbs/190404b/,
detected by the MAXI satellite, ©2009 JAXA/Riken/MAXI-team.

##### Event 6 - 190606555

Event 6 is categorized as GRBs for the same reasons as event 5, however
because it is near the galactic plane, event 6 might be a Galactic X-ray
burst, Figure 5.12.

Figure 5.12: Lightcurve and localization for event id 6. We could classify
this event as a GRB or, because of its proximity to the galactic plane, a GF.
Source: Crupi et al. [67].

##### Event 7 - 110215667

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 5.13), which is highly correlated with the localization of charged
particle events as discussed in [144, 259] and illustrated in Figure 5.14; as
a result, the event is classified as uncertain. While these events are not the
primary focus of this research, it should be noted as potential precursors to
earthquakes in previous studies such as [206, 15].

Figure 5.13: In the first two figures (top), the lightcurve and localization
for event id 7 are shown. The third one (bottom) 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. Source: Crupi et al. [67].  Figure 5.14:  Earth’s latitude and
longitude coordinates of Fermi. Each point on the map represents the
occurrence of a transient event, with red points indicating an L McIlwain
parameter greater than 1.3, and blue points indicating otherwise. The upper
figure displays Fermi’s positions at the time of Local Particles (LOCLPAR)
occurrences, while the lower figure shows Fermi’s positions at the time of GRB
occurrences. [144] ©Keskin (2021). Reproduced with permission.

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/ GRB | $>10$
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 | 187 | 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 5.3:  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.

###  5.4 Automatic classification

While the initial classification was conducted manually, as indicated by the
transient count in Table 5.4, within the HERMES mission and several years of
data collection it is particularly useful an automated classification system.
Several strategies can be considered for this purpose. The chosen approach
focuses on addressing a relatively small database of around 400 events sourced
from the catalog in Appendix Contributions. The primary goal is to produce
interpretable results, similar to those obtained from rule-based methods. The
goal of this method is to provide a identify potential biases in the
classification process. To accomplish this, a feature engineering procedure
was used to help to reveal underlying patterns through rule extraction. The
goal of this automated classification process is to categorize events quickly
while also providing insights, primarily through rule-based explanations.
These findings are meant to aid future investigations into the true nature of
these transient events.

type of transient | num. known | num. unidentified | total
---|---|---|---
GRB | 127 | 30 | 157
SF | 78 | 24 | 102
UNC(LP) | 3 | 57 | 60
GF | 0 | 10 | 10
TGF | 1 | 8 | 9
UNC | 1 | 3 | 4
FP | 0 | 52 | 52
Table 5.4: Number of transients classes according to the GBM classification
LABEL:tab:catalog_kn and manual classification in Tables
LABEL:tab:catalog_unk. FP refers to False Positive events.

The primary focus of this section will be on the three majority classes: GRB,
SF, UNC(LP). It’s important to note that the "FP" category (False Positives)
is included in the analysis to explore the possibility of excluding events
during the classification phase.

Firstly, features have been defined based on the resulting catalog, derived
from event significance and detector triggers, as presented in Table 5.5.
These features include:

  1. 1.

Parameters like significance in various energy ranges and statistics related
to the triggered detectors. Additionally, features have been extracted based
on satellite information and event localization, as outlined in Table 5.6.

  2. 2.

Data such as right ascension, declination, solar and Earth coordinates,
localization in galactic coordinates, and flags indicating Earth and Sun
visibility.

  3. 3.

Feature engineering involving statistics and wavelet transforms applied to the
event’s lightcurve over energy ranges with higher significance. These features
are detailed in Table 5.7 and include metrics such as peak counts, peak-to-
peak distances, kurtosis, skewness, maxima, minima, medians, means, standard
deviations, and various wavelet-based features. The computations have been
performed using the TSFEL software [24].

Furthermore, These comprehensive sets of features provide a rich dataset for
further analysis and classification of transient events.

feature name |  description
---|---
$S_{r0}$, $S_{r1}$, $S_{r2}$ |  Significance of the event per each energy range defined as in Equation 5.1.2
duration |  Duration of the event defined by the trigger algorithm
qtl_cut_r0, qtl_cut_r1, qtl_cut_r2 |  Quantile-based threshold chosen to in event significance calculation per each energy range
sigma_r0_ratio, sigma_r1_ratio, sigma_r2_ratio |  $S_{r0}/\text{duration}$, $S_{r1}/\text{duration}$, $S_{r2}/\text{duration}$
HR10, HR21 |  $S_{r0}/S_{r1}$ and $S_{r1}/S_{r2}$
num_det_rng, num_det |  Number of detector/range and detector triggered, number of detector triggered per energy range
$\text{flg}_{r_{j}}$ |  Flag, equal to 1 if range $r_{j}$ has been triggered
$\text{flg}_{n_{i}}$ |  Flag, equal to 1 if detector $n_{i}$ has been triggered
avg_det_sun |  Average value of the $\text{flg}_{n_{i}}$, only for the detectors solar faced
avg_det_noSun |  Average value of the $\text{flg}_{n_{i}}$, only for the detectors not solar faced
num_anti_coincidence |  Number of opposite detectors triggered for an event
Table 5.5: List of features included in the complete catalog version. These features refer to the significance of the event in various energy ranges as well as statistics on the detectors that were triggered. A higher significance in energy range r1 is expected for GRB, making these features critical for event characterization. feature name |  description
---|---
ra, dec |  Right ascension and declination of the event localization estimate
ra_montecarlo, dec_montecarlo |  Right ascension and declination average over the localizations estimated over Poisson perturbed count rates
ra_std, dec_std |  Right ascension and declination standard deviation over the localizations estimated over Poisson perturbed count rates
ra_earth, dec_earth |  Right ascension and declination of the center of Earth
ra_sun, dec_sun |  Right ascension and declination of the Sun
diff_sun |  Distance of the Sun respect the event defined as $\mid ra-ra\\_sun\mid+\mid dec-dec\\_sun\mid$
diff_earth |  Distance of the Earth respect the event defined as $\mid ra-ra\\_earth\mid+\mid dec-dec\\_earth\mid$
l_galactic, b_galactic |  Localization of the event in galactic coordinates
earth_vis |  Flag indicator if the localization is in the direction of the Earth
sun_vis |  Flag indicator if the Sun is visible at the event time
l |  L McIlwain parameter
lat_fermi, lon_fermi, alt_fermi |  Position of Fermi in latitude, longitude and altitude
dist_saa_lon |  Distance of Fermi longitude to the nearest point of SAA in longitude
dist_saa_lat |  Distance of Fermi latitude to the nearest point of SAA in latitude
dist_saa |  Sum of Distance of Fermi to SAA in longitude and latitude
dist_pole_nord_lon |  Fermi’s longitude distance to the northernmost zone with a high L McIlwain value
dist_pole_nord_lat |  Fermi’s latitude distance to the northernmost zone with a high L McIlwain value
dist_pole_sud_lon |  Fermi’s longitude distance to the southernmost zone with a high L McIlwain value
dist_pole_sud_lat |  Fermi’s latitude distance to the southernmost zone with a high L McIlwain value
Table 5.6: List of features related to satellite positioning and the localization of the Sun, Earth, and event estimation. These features play a crucial role in discerning the nature of the event, whether its localization is in proximity to the Sun (potential SF), the SAA (potential LP), or other conditions. They include right ascension, declination, distance calculations, galactic coordinates, and more, contributing valuable insights into event characteristics. feature name |  description
---|---
fe_np, fe_bkg_np |  Number of peaks from a defined neighbourhood of the lightcurve and background estimation
fe_pp, fe_bkg_pp |  Peak to peak distance in the lightcurve and background estimation
fe_kur, fe_bkg_kur |  Kurtosis of the lightcurve and background estimation
fe_skw, fe_bkg_skw |  Skewness of the lightcurve and background estimation
fe_max, fe_bkg_max |  Max value of the lightcurve and background estimation
fe_min, fe_bkg_min |  Min value of the lightcurve and background estimation
fe_med, fe_bkg_med |  Median value of the lightcurve and background estimation
fe_mea, fe_bkg_mea |  Average value of the lightcurve and background estimation
fe_std, fe_bkg_std |  Standard deviation of the lightcurve and background estimation
fe_wam |  CWT absolute mean value of each wavelet scale
fe_wen |  CWT energy of each wavelet scale
fe_wstd |  CWT std value of each wavelet scale
fe_wet |  CWT entropy
fe_bkg_step_max |  Maximum of the differences of values of the lightcurve with respect to the previous timestep
fe_bkg_step_min |  Minimum of the differences of values of the lightcurve with respect to the previous timestep
fe_bkg_step |  $max(\mid fe\\_bkg\\_step\\_min\mid,\mid fe\\_bkg\\_step\\_max\mid)$
fe_bkg_step_med |  Median of the differences of values of the lightcurve with respect to the previous timestep
_ratio_* |  list of features determined by the ratio of the previous ones over fe_med, fe_std, fe_min, fe_max, fe_bkg_max, fe_bkg_min or a combination of those
Table 5.7: List of features extracted from the event’s lightcurve and
background estimation. These features encompass statistical measures and
attributes derived from the Continuous Wavelet Transform (CWT), employing the
Ricker wavelet with scale parameters ranging from 1 to 10. The features offer
insights into the event’s temporal characteristics and statistical properties.

Each event class is trained using a one-versus-all machine learning
classifier. Alternatively, a multiclassification model could be employed, but
this approach tends to be more complex to interpret. Another possibility
involves training one class against a different class (one-versus-one), but
this entails managing a greater number of models with less data for each
model. Decision Trees, Random Forest, and RuleFit333Implemented via the GitHub
library imodels based on [233]. are employed for this classification task,
with the latter employing a L1-regularized linear model (Lasso) and boolean
output rules derived from decision trees within a Random Forest, enhancing the
interpretability of the model [102].

The inclusion of Decision Trees serves multiple purposes: firstly, with a
relatively small dataset of approximately 400 events, a generic machine
learning model might encounter issues with overfitting or insufficient data
points; secondly, employing a logically explainable model aids in uncovering
potential biases; thirdly, the decision process underlying this kind of model
is more likely to be human interpretable compared to a model which operates
through summations like regression; and finally, the identified rules can
incorporate domain knowledge, enhancing the robustness and physical
interpretability of the classification model.

On the other hand, Random Forest, an ensemble of Decision Trees, is known as a
"black box" model, as its predictions are more challenging to interpret but
typically result in improved classification performance. To provide
interpretability, additional explainers such as SHAP for feature importance-
based explanations or Anchor for rule-based explanations can be applied.
RuleFit, in addiction, offers an output of coefficients corresponding to the
most significant rules extracted from the Decision Trees within the Random
Forest, providing a comprehensive global explanation of the classification
problem.

The majority of events in the catalog fall into the categories of GRBs (157)
and SF (102), with a third class LP (60), frequently occurring in the catalog
of unknown events. Furthermore, FP (52) are introduced to be distinguished
during the classification process.

The dataset is constructed with a list of features $X$ for each event, as
previously discussed, and a target variable $y\in\\{0,1\\}$, where 1 indicates
membership in the considered event class, while 0 indicates otherwise. It is
important to note that because of the inherent uncertainty in unknown events,
an event may be associated with multiple classes at the same time. This
classification flexibility accounts for the ambiguity that frequently occurs
when dealing with unknown or uncharacterized events.

The dataset is divided into training (80%) and testing (20%) subsets,
maintaining proportional labeling between the sets. Feature selection involves
utilizing a Lasso logistic regressor, including only features with
coefficients distinct from 0. The models and their respective hyperparameters
are as follows:

  1. 1.

Decision Tree classifier (Scikit-Learn):

max_depth=3, class_weight="balanced", criterion="gini",
min_impurity_decrease=0.01, min_samples_leaf=2,
splitter="best".

  2. 2.

Random Forest classifier (Scikit-Learn):

n_estimators=200, max_depth=4, class_weight="balanced",
min_impurity_decrease=0.01.

  3. 3.

RuleFit (imodels):

n_estimators=200, tree_size=4, max_rules=30. The dataset was normalized, only
for this model, with a quantile transformer in order to facilitate the
converge of the lasso linear regression.

  4. 4.

The Manual rules are hand-crafted considering the DT, the most important rules
by RuleFit and guided by the classification methodology described in Section
5.2.1:

    1. (a)

Rule for SF: HR10 $\leq$ 0.392 AND diff_sun < 63.49

    2. (b)

Rule for GRB: HR10 > 0.449 AND HR21 $\leq$ 0.375 AND fe_wet > 2.054

    3. (c)

Rule for LP: ((dist_saa_lon <= 9 AND dist_saa_lat <= 3.6) OR
(dist_polo_nord_lon <= 19 AND dist_polo_nord_lat <= 7.6) OR (dist_polo_sud_lon
<= 19 AND dist_polo_sud_lat <= 7.6)) AND (num_det >= 9 OR fe_skw <= 0.345) AND
(diff_sun > 35 OR $max($ra_std, dec_std$)>100$)

    4. (d)

Rule for FP: $\frac{fe\\_bkg\\_step\\_min}{max(\mid fe\\_bkg\\_max\mid,\mid
fe\\_bkg\\_min\mid))}$ $\leq-0.028$ OR
$\frac{fe\\_bkg\\_step\\_min}{max(\mid fe\\_bkg\\_max\mid,\mid
fe\\_bkg\\_min\mid))}>0.03$ OR fe_std $<4.8$.

Table 5.8 presents the performance metrics of four distinct models on the test
dataset. The Random Forest (RF) and RuleFit models outperformed the simple
rule-based method in three out of four classes, with LP being the exception.
This discrepancy can be attributed to the fact that in the Manual Rules are
included rules based on the distance from the SAA and the Earth’s poles,
particularly in regions with higher McIlwain parameter values (see Figure
5.14). Figure 5.15 depicts the Decision Tree (DT) paths that employ distance
from the SAA. However, the model does not account for the higher L McIlwain
value zone (colored in yellow in Figure 5.13), which is a crucial aspect of LP
classification. RuleFit demonstrated superior performance to RF in SF
classification, indicating that manipulation of important rules (in this case,
only 30 rules) can lead to improved generalization.

However, while simplifying the rules and integrating domain knowledge, Manual
rules outperform the greedy DT and are comparable to RF, except in the case of
GRB classification. This finding suggests the existence of a more complex
underlying pattern for GRB events.

Class |  | DT | RF | FuleFit | Manual rule
---|---|---|---|---|---
Metric |  |  |  |
GRB | Precision | 81% |  93% | 87% | 87%
| Recall | 84% |  90% | 87% | 84%
| F1-score | 83% |  92% | 87% | 85%
SF | Precision | 80% | 80% |  87% | 83%
| Recall |  95% |  95% |  95% |  95%
| F1-score | 87% | 87% |  91% | 89%
LP | Precision | 43% | 73% |  100% | 73%
| Recall | 83% | 67% | 58% |  92%
| F1-score | 57% | 70% | 74% |  81%
FP | Precision | 50% | 82% |  89% | 71%
| Recall |  100% | 90% | 80% |  100%
| F1-score | 67% |  86% | 84% | 83%
Table 5.8: Classification performance over four different models, Decision
Tree (DT), Random Forest (RF), RuleFit and Manual rule, one per each of the
four classes defining a binary classification task. In bold are highlighted
the highest metrics among the models. Figure 5.15: Decision Tree model for LP
class classification. While it correctly incorporates a feature related to the
distance from the SAA, this alone is insufficient for building an effective
model. Notably, the model erroneously splits on the right branch over the
feature diff_earth, which lacks physical relevance and introduces bias. This
is because LP should not exhibit a clear localization, and LP classification
should not be influenced by the Earth’s position.

Indeed, relying heavily on HR10 and HR21 in both DT and manual rules for
determining the GRB class can introduce bias by favoring events within energy
range r1. These rules may lead to selection bias by favoring GRB events within
energy range r1. To improve the generalization of the GRB class, it’s
essential to include a more extensive dataset with a greater representation of
GRB events. In addiction, a careful analysis of classification model errors,
including both false positives and false negatives, could help understanding
the logic behind the classification (see Section 5.4.1).

It was run a training on the shape of the lightcurve using wavelets highlights
an important aspect. While using all features from Table 5.7 yields comparable
performance to Table 5.8, focusing solely on wavelet-transformed features
results in a drop in the F1-score to around 70%. This result emphasizes the
importance of conducting further experiments with lightcurves with higher
temporal resolution (e.g., TTE files) to assess the significance of these
features. Additionally, ML algorithms designed for time series analysis, such
as RNN or CNN, as demonstrated in the [279], shows promise as the dataset
expands to include more events. However, these models’ interpretability
remains a challenge, and more research is needed to elucidate their decision-
making processes and improve their transparency and explainability.

The confusion matrices for the multiclassification task on the train and test
sets using the Random Forest algorithm are shown in table 5.9. The Balanced
Accuracy for the train set is 97%, while it is 86% for the test set,
indicating possible overfitting. To address this issue and improve
generalization, cross-validation with hyperparameter tuning on the train set
could be used. Before fine-tuning the models, an error analysis should be
performed to determine whether the problems are due to the algorithm or the
dataset.

For the evaluation phase, unknown event labels were assigned to a single
class, with priority given to SF, followed by LP and GRB. This prioritization
helps prevent potential confusion between the major classes, being more
precise in GRB labelling.

| Predicted Classes
---|---
| GRB | LP | SF | FP
Actual Classes | GRB | 118 | 4 | 0 | 2
LP | 0 | 47 | 0 | 1
SF | 1 | 2 | 78 | 0
FP | 0 | 1 | 0 | 41

| Predicted Classes
---|---
| GRB | LP | SF | FP
Actual Classes | GRB | 28 | 1 | 1 | 1
LP | 0 | 9 | 2 | 1
SF | 2 | 0 | 19 | 0
FP | 0 | 0 | 1 | 9

Table 5.9: Confusion Matrices (CM) for the multiclassification task. On the
left the CM on the train set with a Balanced Accuracy of 97%. On the right the
CM table with a Balanced Accuracy of 86%.

####  5.4.1 Error analysis

This section investigates false negative and false positive classifications in
order to identify potential model biases or labeling inaccuracies within the
GRB class. We look specifically at Random Forest (RF) false negatives, such as
events GRB 140113624, GRB 140218427, and 2014_193, as well as two Manual rule
false negatives, GRB 110119931 and GRB 190531840. Furthermore, are
investigated two cases of RF false positives among unknown events, denoted
2011_6 and 2011_7.

###### GRB 140113624 - false negative for RF

Figure 5.16 depicts GRB 140113624, which was initially classified as SF rather
than a GRB. Notably, the localization estimate on the lightcurve peak
corresponds to the Sun’s position. However, it’s important to consider that
this event lasts approximately 300 seconds, making it invisible with 4-second
binning (T90 is 4 seconds). As a result, the event displayed is more likely to
have the correct classification, whereas the initial trigger have missed it.

Even the Random Forest for SF classifies this event as a SF with high
confidence (97%) and provides the following Anchor rule: diff_sun $\leq$ 32.58
AND HR10 $\leq$ 0.27. Conversely, the Random Forest for GRB assigns a low
probability (10%) and generates an Anchor Rule as follows: sigma_r0_ratio
$\leq$ 0.06 AND $\frac{fe\\_kur}{fe\\_med}$ > 1.13 AND fe_bkg_min > 253.10.
The first Anchor rule offers a straightforward criterion for classifying as
SF, while the second rule excludes GRB classification based on certain
statistical properties.

Figure 5.16:  The first two figures (top) depict GRB 140113624 and its
estimated localization coincident with the Sun. The third image (bottom)
displays solar activity around the event’s trigger time (15:04:12), as
observed by the GOES-X ray satellite, showing a peak in solar activity
(15:09:00). Credits for the latter image goes to SpaceWeatherLive.com.

###### GRB 140218427 - false negative for RF

Figure 5.17 showcases GRB 140218427, which the RF misclassifies as a FP class
with a probability of 84.7%. The associated Anchor rule is quite extensive:
qtl_cut_r1 $\leq$ 0.35
AND $\frac{fe\\_bkg\\_step\\_max}{fe\\_std}$ > 0.07 AND fe_max $\leq$ 78.94
AND
$\frac{fe\\_bkg\\_step\\_max}{fe\\_bkg\\_std}$ > 0.07 AND dec_std > 153.00 AND
$\frac{fe\\_bkg\\_med}{fe\\_bkg\\_max}$ $\leq$ 0.98 AND
$\frac{fe\\_bkg\\_step\\_max}{\mid fe\\_bkg\\_max\mid-\mid
fe\\_bkg\\_min\mid}$ > 0.20. Interestingly, the features used in this rule
closely resemble those utilized for FP classification. Specifically, the
background estimation exhibits noticeable steps rather than smooth behaviour.

Figure 5.17: GRB 140218427 observed across three energy ranges, with the
softer energy range being the most significant. The background estimation has
non-smooth characteristics and visible steps.

###### 2014_193 - false negative for RF

Figure 5.18 depicts an unknown event (2014_193) initially classified as a GRB,
but upon closer examination, it can be classified as a LP event, especially
when considering the location of Fermi and other detector count rate
residuals. The RF for LP assigns a prediction probability of 54% with the
following Anchor rule: ra_sun > 292.93 AND $\frac{fe\\_med}{fe\\_std}$ > 0.43
AND dist_saa_lon $\leq$ 24.27 AND $\frac{fe\\_mea}{fe\\_std}$ > 2.00 AND
dec_sun > -13.01
AND $\frac{fe\\_bkg\\_step\\_med}{fe\\_std}$ > 0.00.

This rule incorporates features related to the distance from the SAA,
statistical feature extracted and Sun declination, although it’s not entirely
clear how the Sun’s position influences the classification. On the other hand,
the RF for GRB predicts a low probability (29%) with the following Anchor
rule: $\frac{fe\\_min}{fe\\_std}$ > -0.06 AND $\frac{fe\\_med}{\mid
fe\\_max\mid-\mid fe\\_min\mid}$ > 0.54.

Figure 5.18: The first two figures (top) display an unknown event initially
labelled as a GRB. The left image shows the triggered detectors, while the
right shows the non-triggered detectors. The bottom image illustrates Fermi’s
location during the trigger phase.

###### GRB 110119931 - false negative for Manual Rules

Figure 5.19 illustrates the event GRB 110119931, which was initially
classified as a non-GRB and as a LP event according to the Manual rules. This
misclassification can be attributed to the condition
$\text{dist\\_saa}>10.225$, which tends to exclude many LP events but
occasionally results in the misclassification of genuine GRBs. Further
analysis involving shape features is required to address this issue.

In contrast, the RF correctly classifies the event with a probability of 72%.
Its Anchor rule is as follows: $\frac{fe\\_bkg\\_skw}{\mid
fe\\_bkg\\_max\mid-\mid fe\\_bkg\\_min\mid}$ > -0.06 AND
$\frac{fe\\_kur}{fe\\_med}$ > 1.13 AND fe_bkg_min > 253.10. Notably, this rule
relies on statistics derived from the lightcurve distribution, rather than the
hardness ratio proxy used in the Manual rules.

Figure 5.19: On top is GRB 110119931, which was initially misclassified as a
non-GRB and as a LP event using Manual rules. The bottom figure illustrates
the equatorial coordinates of the Fermi satellite’s location, highlighting its
proximity to the SAA.

###### GRB 190531840 - false negative for Manual Rules

The manual rule for classifying GRBs has an energy range bias, which results
in performance that is approximately 6% lower than that of the RF. Due to high
count rates in all energy bands, Event 3 is misclassified as a non-GRB,
similar to GRB 190531840, as shown in Figure 5.20. This bias is undesirable,
and it highlights the need for a second analysis that includes spectral,
statistical, and temporal factors to investigate such events further.

Figure 5.20: GRB event exhibiting high energy levels across all energy bands.
This type of event may be underrepresented in the catalog, potentially leading
to bias in the ML algorithm.

###### 2011_6 - false positive for RF

There are only two false positive events identified by the RF in the test set,
meaning they are labeled as non-GRBs but classified as GRBs. For event 2011_6,
the probability of being a GRB is 78%, with an Anchor rule of HR21 $\leq$ 0.27
AND $\frac{fe\\_min}{fe\\_std}$ $\leq$ -0.40 AND $\frac{fe\\_skw}{\mid
fe\\_bkg\\_max\mid-\mid fe\\_bkg\\_min\mid}$ > 0.64. While the RF for SF gives
a probability of 0.05 with Anchor rule: diff_sun > 32.58 AND HR10 > 1.29.
Meanwhile, the RF for SF gives a low probability of 5%, with an Anchor rule of
diff_sun > 32.58 AND HR10 > 1.29.

Figure 5.21 shows that the event’s localization is not directly over the sun,
indicating that could not be a SF event. Instead, because the event is so
close to the galactic center, it was manually labeled as GF. To improve the
classification algorithm, it may be beneficial to extend the classification to
GF, incorporating more of those in the training dataset.

###### 2011_7 - false positive for RF

Event 2011_7 (Figure 5.21) was classified as SF primarily due to a peak in
activity observed by the GOES-X ray satellite. The RF for GRB assigned a
probability of 63%, driven by the Anchor rule: fe_wstd3 > 0.21 AND fe_wet >
2.12 AND $\frac{fe\\_min}{fe\\_std}$ $\leq$ -0.06.

Conversely, the RF for SF, while slightly lower with a probability of 61%,
aligned closely with the Manual rule, reflected in the Anchor rule: diff_sun
$\leq$ 32.58 AND HR10 $\leq$ 0.27. These probabilities are quite close,
explaining the classification error.

Figure 5.21: Lightcurves of events 2011_6 (top) and 2011_7 (bottom) in the
triggered detectors and their corresponding localization estimates. Event
2011_6 is notably close to the galactic center, potentially be classified as
GF. However, the GF class is not included in the classification task.

###### Summary

The output results aim to provide possible event classifications, but it’s
important to note that further comprehensive analysis will be necessary to
validate these classifications. This could involve using a larger dataset of
events as input to the ML algorithms to obtain more precise outputs.
Additionally, subsequent analysis could involve tasks such as spectral
examinations or cross-referencing with additional data sources related to
pulsars, galactic objects, and solar activity, potentially with higher
temporal resolution using data like TTE files.

In summary, the Random Forest (RF) classification on the test dataset shows
promise, and the error analysis often provides insights into understandable
misclassifications, which, in some instances, actually confirm the logic
behind the initial classifications. For instance, in the case of event 2011_7,
the close probabilities of GRB and SF classifications highlight the challenge
of distinguishing between them. The misclassification of event 2011_6
underscores the importance of including GF in the classification task, given
the event’s proximity to the galactic center. Furthermore, GRB 140113624’s
misclassification likely arises because it is more likely an SF, as suggested
by the Manual rules. Similarly, event 2014_193 could potentially be
reclassified as LP in the dataset. Additionally, the misclassification of GRB
140218427 can be attributed to the non-smooth background estimation and the
event’s soft characteristics, indicating areas for background NN estimator
refinement.

While the Manual rules offer a straightforward and transparent classification
approach, they may introduce bias related to energy range (being either too
soft or too hard) or misclassify GRB events as LP or SF, especially when these
events are detected in a zone where Fermi is close proximity to the SAA (e.g.,
GRB 110119931) or the location estimated is near the Sun.

## Conclusion and future perspective

### Overview

In this thesis it is reported an overview of GRBs, their observational
properties, and possible progenitors, as well as the instruments on board
satellites that detect them.

The Chapter 3 serves has an overview of the applications and opportunities
that AI has in the GRB context. To the best of my knowledge, this was the
first comprehensive review of its kind in the literature. Clustering of GRBs
was investigated in order to categorize them and gain insight into their
possible progenitors. The step of the detection and classification of
transients is critical because it is responsible for creating a catalog that
should represent the larger population of events. The quality and quantity of
this catalog are essential factors that influence the outcomes of subsequent
research and analyses. Further research focus on using GRBs as standard
candles in cosmological distance, and finally, GRB reconstruction and/or
simulation may be used to complete some analyses that are difficult to
complete because of missing data, or to test detection algorithms with
selected GRB or background conditions.

The new generation of satellites (see Section 1.2.9), particularly those
operating with limited resources such as CubeSats, necessitates innovative
algorithms to maximize data utility. My contribution is in this vein; I
developed a novel framework for detecting high energy transients using a data-
driven approach, which is described in Chapters 4 and 5. This approach may be
included into an AI application for the detection of long and faint GRBs, that
could have been missed by other techniques.

This novel method for high-energy transient detection [67] integrate the
precise estimation from a NN [64] with an efficient trigger algorithm [264,
82]. 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. In this work I have presented an application using Fermi/GBM data,
which served as the training dataset for the framework.

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 can be seen in the 2014 Fermi/GBM data observation. It
has also been integrated explainability methods into the framework. SHAP
allows users to understand and interpret specific predictions made by the NN
by revealing the key contributing factors and features. This method, as a
debugging process, allow me to identify a problem in the feature creation of
the input dataset and biases within the NN, particularly in the 2014 period.

The background is then used by Poisson-FOCuS, an evolution iteration of the
CUSUM algorithm, to detect transient events more efficiently. This method is
tested on three distinct periods of Fermi/GBM data, which are temporally
binned into 4-second intervals. The method successfully extract the majority
of known GRBs lasting more than 4 seconds. It also identifies transients that
are not present in the Fermi/GBM catalog, which are detailed in the appendix.
A thorough examination of seven such previously unknown events is provided,
along with the information needed to classify them.

In the final phase of this work, I describe the implementation of a supervised
ML algorithm for automatic classification. Furthermore, using transparent ML
algorithm allows for the examination of the prediction logic, making it easier
to identify potential biases within the model.

### Future perspectives

There were no ultra-long GRBs detected in the 9 months of analyzed data.
However, candidate long GRBs were identified, some of which had softer spectra
than typical GRBs. Based on these promising results, the natural progression
could be to extend the work the entire 15-year Fermi dataset.

The focus of future work will be on improving the neural network’s prediction
capabilities. It is worth to investigate the use of Recurrent Neural Networks
and expand the training dataset in order to achieve a more precise estimation,
particularly in areas near the South Atlantic Anomaly. I hope to improve
detection of shorter events with higher precision by reducing time binning,
for example, from 4 s to 1 s. To accomplish this, `CTIME` or `TTE` files would
be required. Actually, the Neural Network described in Chapter 4 must be
applied in the same time period as the training data and does not generalize
perfectly outside of that time period.

Accordingly to the explainability analysis the longitude feature has to be
fixed in the dataset, whereas a the detector FoV occultation features should
have been treated differently (e.g., removed) and retrain the NN, in order to
reduced the biased induced in the NN, which are causes of unexpected steps in
the background estimation. It should be noted that solar activity alters the
background significantly, so an information of time as a feature may be
required to extend the period of training.

In terms of the trigger algorithm, hyperparameter tuning may be required to
optimize the detection of cataloged transients (e.g., Fermi/GBM) while keeping
false positives to a minimum.

Regarding other AI applications discussed in Chapter 3, beyond of the scope of
this thesis but worth to mention, it is suggested to leverage on the most
comprehensive GRB information by representing a GRB as an image (Figure 3.9),
taking into account both temporal and spectral aspects. The handling of
various detector instruments should also be carefully considered. I believe
that it is valuable to investigate novel AI architectures such as Transformers
and the Diffusion model for a variety of tasks. One particularly appealing
direction is the generation of GRBs while taking into account specific
physical properties (e.g., T90, HR, power-law index of Band function, etc).

### Contributions

In conclusion, there are many exciting opportunities for future work in this
field. A well-prepared dataset is essential for inspiring computer scientists
to apply AI techniques to GRBs. My major contribution, DeepGRB
github.com/rcrupi/DeepGRB, provides an end-to-end solution for detecting and
classifying transients, with modularity for future enhancements. The research
contributions related to the background estimator and the application of the
trigger algorithm can be found in the following references: [64, 67, 82].
Furthermore, I contributed to SynthBurst github.com/peppedilillo/synthburst
provides functionality to leverage background estimation for simulating GRBs
in various contexts for testing new trigger algorithms. Finally, ImageGRB
github.com/rcrupi/ImageGRB describes a method for representing Fermi/ GBM GRBs
as images, allowing them to be used in clustering, image generation or in
semi-supervised regression tasks such as redshift estimation for GRBs, which
could leverage both those with known and unknown redshift values.

##  Appendix A Catalog tables

Table A.1:  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 5.3. # | 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: SF/GF | 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: UNC | 0 | 9.18 | 5.31 | P
14 | 2010_14 |  2010-11-22 13:23:34 | 8.19 |  n5 nb |  UNKNOWN: UNC(LP) | 0 | 7.45 | 5.41 | R
33 | 2010_34 |  2010-12-28 18:22:27 | 106.50 |  na |  UNKNOWN: UNC(LP) | 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: UNC(LP) | 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/GRB | $>10$ | $>10$ | 0 | R
117 | 2014_61 |  2014-01-13 20:37:08 | 45.06 |  n3 n4 na |  UNKNOWN: UNC(LP) | 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: UNC(LP) | 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: UNC(LP) | 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: UNC(LP) | 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: UNC(LP) | 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: UNC(LP) | 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: UNC(LP) | 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: UNC(LP) | 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: UNC(LP) | 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 A.2:  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

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