Study of blazar variability through their flux distribution for CTAO long-term monitoring

@misc{mikhno2026study,
  title = {{Study of blazar variability through their flux distribution for CTAO long-term monitoring}},
  author = {A. Mikhno and J. Biteau and J. -P. Lenain on behalf of the CTAO collaboration},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.28276},
  eprint = {2609.28276},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.28276},
  note = {CC BY-NC-SA 4.0},
}

TY  - UNPB
TI  - Study of blazar variability through their flux distribution for CTAO long-term monitoring
AU  - A. Mikhno
AU  - J. Biteau
AU  - J. -P. Lenain on behalf of the CTAO collaboration
PY  - 2026
DA  - 2026/09/23
PB  - arXiv
DO  - 10.48550/arxiv.2609.28276
UR  - https://arxiv.org/abs/2609.28276
AB  - The CTAO Key Science Project on Active Galactic Nuclei (AGN) includes a long-term monitoring (LTM) program of blazars. The probability distribution function (PDF) of the flux extracted from unbiased blazar lightcurves can provide important insight into the physical processes taking place in relativistic jets and their connection to observed variability. The aim of this work is to define an optimal observational strategy for the CTAO LTM program using the flux distributions as a quantitative metric. To achieve this goal, we develop a simulation-based analysis framework that enables a systematic comparison of different observational strategies. Light curves were simulated as would be observed with the future CTAO for a representative set of AGN sources, following four different observational strategies within a fixed time budget. For each simulated light curve, different PDF models were fitted and their goodness of fit was evaluated. Based on the best-fit parameters, Monte Carlo simulations were generated to assess the ability of the method to recover the underlying PDF model. Since the true model used to generate the simulated light curves is known, this procedure allows us to quantify the discriminatory power of the algorithm and to identify which observational strategy maximizes model discrimination. Additionally, the algorithm has the potential of discrimination against different models for the flux distributions, such as Gaussian and lognormal. Once validated on simulations, the method is applied to archival lightcurves from existing gamma-ray instruments to extract flux PDFs and study the physical processes driving blazar variability. The analysis framework has been developed and tested on simulated CTAO-like data, showing good stability and robustness. A study to optimize the observing strategy for the CTAO LTM is presented.
ER  -