Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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SPECTRA Fermi-LAT Simulated Sky Maps

This repository contains the simulated dataset associated with the manuscript:

“Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection”

The associated manuscript is currently under review at Astronomy and Computing.

Overview

This dataset was generated to support the development and validation of self-supervised, spatio-temporal anomaly-detection methods for gamma-ray transient searches in Fermi-LAT-like observations.

Starting from long-duration gtobssim simulations within the Fermitools ecosystem, the pipeline produces daily full-sky maps of:

counts
exposure

The primary dataset released here is the original energy-resolved tensor with shape:

(3649, 2, 10, 360, 180)

where:

3649 = valid daily frames over approximately 10 years
2 = channels (counts, exposure)
10 = logarithmically spaced energy bins spanning 100 MeV–500 GeV
360 x 180 = all-sky spatial grid in celestial coordinates
spatial resolution = 1 degree/pixel

One daily frame was discarded during preprocessing and quality control; therefore, the public release contains 3649 valid daily observations.

An energy-integrated representation can be derived by summing over the energy axis, yielding:

(3649, 2, 360, 180)

This reduced representation is the one used in the main ConvLSTM experiments discussed in the manuscript, whereas the full tensor released here preserves the complete spectral structure of the simulation.

Scientific context

The dataset is designed as a controlled testbed for:

self-supervised next-frame prediction
residual-based anomaly detection
transient-search benchmarking
future extensions to energy-resolved spatio-temporal modeling

It reproduces the structure of daily Fermi-LAT-like observations while keeping the simulation environment fully controlled.

Data description

Tensor axes

The main tensor follows the convention:

(time, channel, energy, x, y)

with:

time = 3649 daily frames
channel = 2 → counts, exposure
energy = 10 logarithmic bins
x = 360
y = 180

Channels

channel 0: counts map
channel 1: exposure map

Energy range

100 MeV to 500 GeV
10 logarithmically spaced bins

Spatial grid

celestial coordinates
Cartesian (CAR) projection
360 x 180 pixels
1 degree/pixel

Time coverage

3649 valid daily maps
approximately 10 years of simulated observations

Repository contents

The repository structure is:

README.md
metadata.json
data/
- spectra_tensor_part_00.npy
- spectra_tensor_part_01.npy
- spectra_tensor_part_02.npy
- spectra_tensor_part_03.npy
load_example.py

Intended use

This dataset is intended for research purposes, including:

anomaly detection in gamma-ray astronomy
self-supervised spatio-temporal modeling
benchmarking of ConvLSTM and related architectures
methodological studies on synthetic Fermi-LAT-like sky maps

Citation

If you use this dataset, please cite the associated manuscript.

Suggested citation text:

Garinei et al., “Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection”, under review at Astronomy and Computing.

Important notes

This dataset is simulated, not a redistribution of official Fermi-LAT survey data products.
The exposure channel is included because exposure variations are an essential part of the predictive and anomaly-detection problem addressed in the manuscript.
The repository is intended to improve reproducibility and public accessibility of the simulation products described in the paper.

Contact

For questions regarding the dataset, simulation pipeline, or manuscript status, please contact the corresponding author listed in the associated paper.

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