Analysis BLLac Aug 2021 Aguasca-Cabot
Contents
Basis
The basis of this algorithm is explained in the following LST-Reco calls:
- Content: Description of the algorithm and proof of concept with Crab data and simulations of different temporal profiles.
- Content: Upgrade of the algorithm and application to simulations of an orbital period of HESS J0632+057.
(Gabriel Emery developed a similar algorithm in his PhD, see https://tel.archives-ouvertes.fr/tel-02983041/document)
Objectives
- Predict what is the observation time that LST-1 would require to achieve a given significance of a transient event to plan future observations of similar transient sources. A spectral index of the source is assumed.
- Study the variable emission of a source using the minimum time interval LST can probe this emission considering a threshold value in its significance. Again, this is done assuming a given spectral index.
- Obtain a light curve specifying a given threshold in the significance of the source, instead of specifying a time interval.
BL Lac light curve
- Use the same DL3 data of BL Lac obtained by the BL Lac team in order to use their light curve as the reference one. Apply the algorithm to BL Lac to obtain the minimum time interval for each flux.
Fast analysis
This fast analysis aims to show preliminary results of the algorithm to BL Lac data. MC gammas are optimized to BL Lac (see data reduction specifications). The results should improve with a refined data reduction.
Parameters values of the algorithm:
- Range of energy (TeV): 0.1 to 1
- Threshold in Li & Ma TS: 9
- Threshold for UL (sigma): 2
- Increase of events in each iteration: 1000
- Minimum threshold to consider two consecutive observations (min): 10
The best spectral model obtained fitting all data:
This model is used to compute the fluxes using forward folding
Data reduction specifications
Light curve
We can see that the flux points produced by the algorithm are well distributed around the run-wise fluxes and around fluxes for a time interval of more or less three minutes. Also, we can see that when the 3 min fluxes increase or decrease, the 3 sigma fluxes also show an increase or decrease. For example, see fluxes at t=5.9435e4+0.05 MJD.
Notice that the light curves in left and right figures are slightly different because we used a different data reduction and assumed spectral index.
TS detection and time interval of the fluxes
In the left figure, some TS values are much higher than the threshold value (9) because I used an increase of 1000 events in each iteration to reduce the computation time.
In the right figure, there are high values of time intervals because events from two different runs are used to compute the flux.
The p-value against the null hypothesis of a constant source is 1.7350606859473474e-101.
