Astrophysical and astroparticles data are the output of experiments done with a huge variety of instruments ranging from those installed on satellites to those in terrestrial observatories to those installed under the sea surface.
Typycal examples include:
CHANDRA X-ray center – https://cda.harvard.edu/chaser/
XMM-NEWTON repository – https://www.cosmos.esa.int/web/xmm-newton/sas
Pierre Auger Observatory – https://opendata.auger.org
Neutrino data – https://www.km3net.org
The main data repositories used in TECLA are the CHANDRA and XMM repository for data of astrophysical sources.
TECLA developed software for the analysis of astrophysical data. Specifically, TECLA provides tools for astrophysical data analysis in four different areas:
- Tools for the analyses of supernovae remnants images. Starting from X-emission spectra of different SNRs we can cosntruct dendrograms whose leaves represent the different elements of continuum parts of each SNRs. According to the leaves sequence in the dendrogram one can assess what are the SNRs with highest similarity.
- Tools for the detection of changepoints in the lightcurves. Our tool allows to distinguish the different regions of the lightcurve characterized by homogeneous statistical properties.
- Tools for the lightcurve cleaning with Deep learning methods. The analysis of cosmic background emission signals is often flared by the contamination effects due to solar flares. Our tool allows for the discrimitation between genuone astrophysical signal from noise due to solar flares. This is obtained with deep learning based tools.
- Tools for the lightcurve cleaning with statistical methods. The analysis of cosmic background emission signals is often flared by the contamination effects due to solar flares. Our tool allows for the discrimination between genuine astrophysical signal from noise due to solar flares. This is obtained with tools leverage on the statistical properties of the different lightcurve regions.
In this section our tools are available in the form of web apps that allow any user to upload its own data files.
- SNRsClust
- Fast Changepoints
- Stat Photon Cleaner (SPC)
- Deep Photon Cleaner (DPC)
SNRsClust
SNRsClust is a tool for the joint analysis of spatial and spectral information across different energy bands, which is essential for the study of supernova remnants (SNRs). This tool performs clustering of SNR image data using agglomerative techniques. Three hierarchical approaches are supported: SVHC, CosHC, and GraphHC, each leveraging different similarity measures or graph-based relationships to group regions with similar features.
Fast Changepoints
A fast Python library for changepoint detection in time series data.
Implements an efficient PELT with Numba acceleration and a custom robust PELT implementation for automatic penalty selection, which selects the penalty for which there are no changepoints in the shuffled time series.
StatPhotonCleaner (SPC)
StatPhotonCleaner (SPC) is an interactive web-based application that allows users to upload and clean astronomical photon event data stored in FITS files. It automatically identifies and filters out noisy photon bins based on temporal and energy statistics, and provides a side-by-side comparison between the original and cleaned photon counts.
DeepPhotonCleaner (DPC)
DeepPhotonCleaner (DPC) is a deep learning method that removes solar flare contamination from cosmic background signals. It uses an Autoencoder to detect clean segments of the signal, which then guide the cleaning process, preserving genuine astrophysical information while eliminating flare-contaminated parts.