Difference between revisions of "Analysis BLLac Aug 2021 Aguasca-Cabot"
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* 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. | * 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. | * Obtain a light curve specifying a given threshold in the significance of the source, instead of specifying a time interval. | ||
| − | |||
= BL Lac light curve = | = BL Lac light curve = | ||
| Line 21: | Line 20: | ||
= Fast analysis = | = Fast analysis = | ||
| − | This fast analysis aims to show preliminary results of the algorithm | + | This fast analysis aims to show preliminary results of the algorithm using BL Lac data. MC gammas are optimized to BL Lac (see [[#Data reduction specifications|data reduction specifications]]). The results should improve with a refined data reduction. Note: '''the reconstructed energy axis specified in the data reduction specifications section is not the same reco. energy axis used in the algorithm''' |
| − | + | To execute the algorithm, several parameters must be specified. Parameter values of the algorithm used in this analysis: | |
| − | * Range of energy (TeV): 0.1 to 1 | + | * Range of energy (TeV): 0.1 to 1. Reco. energy axis: [0.1,1,3], log. interp. |
* Threshold in Li & Ma TS: 9 | * Threshold in Li & Ma TS: 9 | ||
* Threshold for UL (sigma): 2 | * Threshold for UL (sigma): 2 | ||
* Increase of events in each iteration: 1000 | * Increase of events in each iteration: 1000 | ||
* Minimum threshold to consider two consecutive observations (min): 10 | * Minimum threshold to consider two consecutive observations (min): 10 | ||
| + | * True energy axis: Same as high-level data reduction specifications (see [[#Data reduction specifications|Data reduction specifications]]). | ||
| + | * ON and OFF regions: Same as high-level data reduction specifications (see [[#Data reduction specifications|Data reduction specifications]]). | ||
| − | The best spectral model obtained fitting all data: | + | '''No fitting is performed within the algorithm''' |
| + | *The best spectral model obtained fitting all data (using parameters in [[#Data reduction specifications|data reduction specifications]]): | ||
| + | [[File:Spec-model-20210808-fast.png|600px]] | ||
| − | + | This model is used to compute the fluxes using forward folding. | |
| − | |||
| − | This model is used to compute the fluxes using forward folding | ||
== Data reduction specifications == | == Data reduction specifications == | ||
| + | Data reduction specifications to perform the data reduction up to DL4. The high-level specifications are used to fit the spectral model of BL Lac. | ||
<div><ul> | <div><ul> | ||
<li style="display: inline-block;"> [[File:BLLac-data-reduction-specifications.png|thumb|none|600px|DL1 to DL3 specifications]] </li> | <li style="display: inline-block;"> [[File:BLLac-data-reduction-specifications.png|thumb|none|600px|DL1 to DL3 specifications]] </li> | ||
| Line 50: | Line 52: | ||
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. | 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 the left and right figures are slightly different because we used | + | Notice that the light curves in the left and right figures are slightly different because we used different data reductions specifications and assumed spectral index. |
== TS detection and time interval of the fluxes == | == TS detection and time interval of the fluxes == | ||
| Line 62: | Line 64: | ||
The p-value against the null hypothesis of a constant source is 1.7350606859473474e-101. | The p-value against the null hypothesis of a constant source is 1.7350606859473474e-101. | ||
| + | |||
| + | == Checks == | ||
| + | |||
| + | * Most flux time intervals in the [[:Media:Time-resolution-20210808-fast-TRETS.png|Histogram of flux time intervals]] are lower than 1 min. We plot the fluxes for 1 min time interval and check how the 1-min fluxes are distributed compared to the algorithm and run-wise fluxes. | ||
| + | <div><ul> | ||
| + | <li style="display: inline-block;"> [[File:Light-curve-BLLac-fast-TRETS-loosecuts-1minSubruns.png|thumb|none|600px|Fast analysis light curve with 1-min subrun flux]] </li> | ||
| + | </ul></div> | ||
| + | We can see that the flux points produced by the algorithm are well distributed around the 1-min fluxes. | ||
| + | |||
| + | |||
| + | *Check if the weighted average flux is compatible with the 3-min (left) and run-wise fluxes (center). Also, it is checked if the weighted average 3-min fluxes are compatible with the run-wise fluxes (right). | ||
| + | <div><ul> | ||
| + | <li style="display: inline-block;"> [[File:Weighted av TRETS to subruns-3min.png|thumb|none|600px|Weighted average of the fluxes obtained with the algorithm compared with the 3-min flux.]] </li> | ||
| + | <li style="display: inline-block;"> [[File:Weighted av TRETS to runs.png|thumb|none|600px|Weighted average of the fluxes obtained with the algorithm compared with the run-wise flux.]] </li> | ||
| + | <li style="display: inline-block;"> [[File:Weighted av subruns-3min to runs.png|thumb|none|600px|Weighted average of the 3-min fluxes compared with the run-wise flux.]] </li> | ||
| + | </ul></div> | ||
| + | |||
| + | Some of the weighted average fluxes are miss-aligned in the x-axis to the reference ones because we consider only flux points within the time interval. For instance, fluxes using events from two different observations are not considered, or between two different 3-min time bins. | ||
Latest revision as of 22:16, 10 April 2022
Contents
Basis[edit]
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[edit]
- 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[edit]
- 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[edit]
This fast analysis aims to show preliminary results of the algorithm using BL Lac data. MC gammas are optimized to BL Lac (see data reduction specifications). The results should improve with a refined data reduction. Note: the reconstructed energy axis specified in the data reduction specifications section is not the same reco. energy axis used in the algorithm
To execute the algorithm, several parameters must be specified. Parameter values of the algorithm used in this analysis:
- Range of energy (TeV): 0.1 to 1. Reco. energy axis: [0.1,1,3], log. interp.
- 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
- True energy axis: Same as high-level data reduction specifications (see Data reduction specifications).
- ON and OFF regions: Same as high-level data reduction specifications (see Data reduction specifications).
No fitting is performed within the algorithm
- The best spectral model obtained fitting all data (using parameters in data reduction specifications):
This model is used to compute the fluxes using forward folding.
Data reduction specifications[edit]
Data reduction specifications to perform the data reduction up to DL4. The high-level specifications are used to fit the spectral model of BL Lac.
Light curve[edit]
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 the left and right figures are slightly different because we used different data reductions specifications and assumed spectral index.
TS detection and time interval of the fluxes[edit]
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.
Checks[edit]
- Most flux time intervals in the Histogram of flux time intervals are lower than 1 min. We plot the fluxes for 1 min time interval and check how the 1-min fluxes are distributed compared to the algorithm and run-wise fluxes.
We can see that the flux points produced by the algorithm are well distributed around the 1-min fluxes.
- Check if the weighted average flux is compatible with the 3-min (left) and run-wise fluxes (center). Also, it is checked if the weighted average 3-min fluxes are compatible with the run-wise fluxes (right).
Some of the weighted average fluxes are miss-aligned in the x-axis to the reference ones because we consider only flux points within the time interval. For instance, fluxes using events from two different observations are not considered, or between two different 3-min time bins.
