Difference between revisions of "Analysis BLLac Aug 2021 Aguasca-Cabot"
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<li style="display: inline-block;"> [[File:Time-resolution-20210808-fast-TRETS.png|thumb|none|800px|Histogram of flux time intervals.]] </li> | <li style="display: inline-block;"> [[File:Time-resolution-20210808-fast-TRETS.png|thumb|none|800px|Histogram of flux time intervals.]] </li> | ||
</ul></div> | </ul></div> | ||
| − | In the left figure, some TS values are much higher than the threshold value because I did not use soft cuts | + | In the left figure, some TS values are much higher than the threshold value because I did not use soft cuts. |
| − | |||
In the right figure, high values of time intervals are due to the fact that events from two different runs are used to compute the flux. | In the right figure, high values of time intervals are due to the fact that events from two different runs are used to compute the flux. | ||
The p-value against the null hypothesis of a constant source is 6.173759998831549e-53. | The p-value against the null hypothesis of a constant source is 6.173759998831549e-53. | ||
Revision as of 23:12, 6 April 2022
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)
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. Neither the cuts nor the MC simulations are optimized to BL Lac (see data reduction specifications). The results should improve with a refined data reduction and proper parameter values for the algorithm, for example, the number of events added in each iteration.
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: 50
- Minimum threshold to consider two consecutive observations (min): 10
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 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 do too.
TS detection and time interval of the fluxes
In the left figure, some TS values are much higher than the threshold value because I did not use soft cuts. In the right figure, high values of time intervals are due to the fact that events from two different runs are used to compute the flux.
The p-value against the null hypothesis of a constant source is 6.173759998831549e-53.
