Jahns_2022 Slide

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diff --git a/docs/notebooks/19-FRB20180916B.ipynb b/docs/notebooks/19-FRB20180916B.ipynb
diff --git a/docs/notebooks/FRB20180916B-periodogram-LS.png b/docs/notebooks/FRB20180916B-periodogram-LS.png
diff --git a/docs/notebooks/FRB20180916B-periodogram-phase.png b/docs/notebooks/FRB20180916B-periodogram-phase.png
diff --git a/docs/paper/repeaters.pdf b/docs/paper/repeaters.pdf
diff --git a/docs/paper/repeaters.qmd b/docs/paper/repeaters.qmd
@@ -102,14 +102,24 @@ The idea is that the uncertainty in the periodicity is tied to the fact that som
 # Result
 
 ## FRB20180916B
-- All three methods were able to recover the known periodicity for FRB20180916B of 16 days with considerable standard deviation and false alarm probability of ....
+The methodology were first applied to FRB20180916B as a test.
+The Lomb--Scargle periodogram points to a periodicity of 16.33$\pm$0.01^[The estimation results in uncertainty below 0.01] days with a false alarm probability of ... .
+The Duty Cycle periodogram shows that it has a periodicity of 16.60$\pm$0.21 days with a duty cycle of 42.11% while the Phase Dispersion Minimization periodogram shows a periodicity of 16.39$\pm$ ... days.
+The low uncertainty in Lomb--Scargle periodogram can be attributed to the FRB having 77 detections with a well-defined periodicity so leaving one out does not affect the result that much.
+This is consistent with the accepted value of 16 days (citation needed).
+
 ## FRB20190915D
+The periodicity results for FRB20190915D are 30.06$\pm$10.21 days using the Lomb--Scargle periodogram, 30.00$\pm$0.47 days using the Duty Cycle periodogram and 13.84$\pm$0.01 days using the Phase Dispersion Minimization periodogram.
+Its false alarm probability is 4.87% and the duty cycle is ... .
+It is surprising that phase dispersion minimization shows a different result. Why is that?
+
 - FRB20190915D shows consistent periodicity of 30 days with false alarm probability of 1% using Lomb--Scargle periodogram and the duty cycle periodogram.
 - However, the phase dispersion minimization periodogram show a different periodicity of 13.84 days with no standard deviation.
   The leave one out strategy does not yield a significant standard deviation.
   It may be because ...
+
 ## FRB20191106C
-- In contrast, FRB20191106C shows inconsistent result.
+In contrast, FRB20191106C shows inconsistent result.
 
 # Discussion
 

diff --git a/docs/slides/jahns_2022/_metadata/gantt/milestones.csv b/docs/slides/jahns_2022/_metadata/gantt/milestones.csv
@@ -0,0 +1,5 @@
+date,name
+2023-04-17,"Proposal Defence"
+2023-10-16,"Publication / Candidature Defence"
+2024-04-15,"Chapters 1,2 & 3"
+2024-10-14,"Submission of Dissertation"
diff --git a/docs/slides/jahns_2022/_metadata/gantt/progress-got.csv b/docs/slides/jahns_2022/_metadata/gantt/progress-got.csv
@@ -0,0 +1,11 @@
+start,end,phase,task
+2022-10-17,2023-07-27,Data Exploration,Data Collection
+2022-10-17,2022-12-26,Data Exploration,Literature Review
+2022-10-17,2022-12-26,Data Exploration,Machine Learning
+2022-12-26,2023-03-17,Theoretical Study,Literature Review
+2022-12-26,2023-04-17,Theoretical Study,Theoretical Consideration
+2023-04-17,2023-07-27,Statistical Study,Literature Review
+2023-04-17,2023-10-16,Statistical Study,Statistical Testing
+2023-10-16,2024-04-15,Extrapolation,Signal Processing
+2024-02-01,2024-04-15,Extrapolation,Analysis
+2024-02-01,2024-10-14,Thesis Writing,Thesis Writing
diff --git a/docs/slides/jahns_2022/figures/20_energetic.png b/docs/slides/jahns_2022/figures/20_energetic.png
diff --git a/docs/slides/jahns_2022/figures/DM-sigma_t.png b/docs/slides/jahns_2022/figures/DM-sigma_t.png
diff --git a/docs/slides/jahns_2022/figures/burst-rate.png b/docs/slides/jahns_2022/figures/burst-rate.png
diff --git a/docs/slides/jahns_2022/figures/dt-sigma_t-linear-vs-power.png b/docs/slides/jahns_2022/figures/dt-sigma_t-linear-vs-power.png
diff --git a/docs/slides/jahns_2022/figures/gauss.png b/docs/slides/jahns_2022/figures/gauss.png
diff --git a/docs/slides/jahns_2022/figures/gauss_waterfall.png b/docs/slides/jahns_2022/figures/gauss_waterfall.png
diff --git a/docs/slides/jahns_2022/figures/subburst-vs-intraburst-drift.png b/docs/slides/jahns_2022/figures/subburst-vs-intraburst-drift.png
diff --git a/docs/slides/jahns_2022/gantt.png b/docs/slides/jahns_2022/gantt.png
diff --git a/docs/slides/jahns_2022/index.qmd b/docs/slides/jahns_2022/index.qmd
@@ -0,0 +1,114 @@
+---
+title: 'The FRB 20121102A November rain in 2018 observed with the Arecibo Telescope'
+subtitle: '<doi:10.1093/mnras/stac3446>'
+author: 'Jahns J. N., et. al. (2022)'
+format: 
+    revealjs:
+        footer: 'Murthadza bin Aznam'
+        # logo: '../../_common/_assets/radio-cosmology-lab-logo.png'
+        chalkboard: true
+        # theme: [dark, ../../_common/_assets/styles.scss]
+        slide-number: true
+        center: true
+bibliography: references.bib
+nocite: |
+  @*
+output-file: 'slides'
+---
+
+## Data
+- 849 bursts detected
+- using the 305m Arecibo Telescope
+- within 1150 to 1730 MHz
+- in the active period of FRB20121102A around Nov 2018
+
+## Observation
+
+![20 most energetic bursts](./figures/20_energetic.png){#fig-obs}
+
+## Burst Rates
+
+![The lower panel shows the burst rate in each observation and the upper panel shows DMs selected for dedispersion](./figures/burst-rate.png){#fig-burst-rate}
+
+## 2D Gaussian Model
+![](./figures/gauss.png)
+
+- The 2D Gaussian is parameterized in such a way that it directly characterizes the center ($t_0$, $\nu_0$) and the drift rate $d_t$.
+(As opposed to the commonly used form of an elliptical 2D Gaussian with rotation).
+
+## 2D Gaussian Fit
+
+::::{.columns}
+:::{.column}
+![The resulting 2D Gaussian fits to an example burst](./figures/gauss_waterfall.png){#fig-eg}
+:::
+:::{.column}
+- There are two forms of drift: temporal drift, $d_t$ and frequency drift $d_\nu$ which can be mapped into and from each other.
+- There are two types of drift: subburst drift (sad-trombone effect) and intraburst drift.
+:::
+::::
+
+## Drift Relationship
+![Relationship between intraburst drift and its center temporal width](./figures/dt-sigma_t-linear-vs-power.png){#fig-drift-width}
+
+## Inter- vs Intraburst Drift
+![Comparison of the temporal drift from the sad-trombone effect (triangles) to the intraburst drift (circles) for the 12 bursts with three or more sub-bursts.](./figures/subburst-vs-intraburst-drift.png){#fig-drift}
+
+## Temporal Drift vs DM
+![The apparent DM difference from the dedispersion DM caused by the intrinsic tilt of the sub-bursts](./figures/DM-sigma_t.png){#fig-DM-sigma_t}
+
+## Periodicity
+- The periodicity is measured using the Lomb--Scargle Periodogram.
+- Since it is only measured within the active period, this only represents the short-term periodicity.
+- No short-term periodicity is found.
+
+
+## Conclusion
+- This paper proposes a new Gaussian fit based on physical parameters
+- There is linear relation between subburst drift and its duration
+- The intraburst drift is the cause of the apparent short-term variations in DM that have been reported.
+- No short-term periodicity despite large burst rate
+
+## Bibliography
+:::{#refs}
+:::
+
+## Progress Report
+```{python}
+#| echo: false
+
+import pandas as pd
+
+from sarjana.gantt import generate_gantt
+
+milestone   = pd.read_csv('./_metadata/gantt/milestones.csv', parse_dates=['date'])
+progress    = pd.read_csv('./_metadata/gantt/progress-got.csv')
+```
+
+:::{.content-visible when-format="pptx"}
+```{python}
+save = 'gantt.png'
+```
+:::
+
+:::{.content-visible when-format="revealjs"}
+```{python}
+save = None
+```
+:::
+
+```{python}
+generate_gantt(progress, milestones=milestone, show=True, savefile=save)
+```
+
+:::{.content-visible when-format="pptx"}
+![](./gantt.png)
+:::
+
+## Progress Report
+1. Periodicity of Some Repeaters with Limited Samples from CHIME/FRB Catalog 2023
+    - Status: Writing
+1. Fast Radio Burst Morphology Consideration of Unsupervised Machine Learning Result
+    - Status: On Hold
+2. BURSTT Collaboration
+    - Status: Early communication with Taiwan team
diff --git a/docs/slides/jahns_2022/references.bib b/docs/slides/jahns_2022/references.bib
@@ -0,0 +1,16 @@
+@article{jahns_FRB20121102ANovember_2022,
+  title = {The {{FRB 20121102A November}} Rain in 2018 Observed with the {{Arecibo Telescope}}},
+  author = {Jahns, J. N. and Spitler, L. G. and Nimmo, K. and Hewitt, D. M. and Snelders, M. P. and Seymour, A. and Hessels, J. W. T. and Gourdji, K. and Michilli, D. and Hilmarsson, G. H.},
+  year = {2022},
+  month = nov,
+  journal = {Monthly Notices of the Royal Astronomical Society},
+  volume = {519},
+  pages = {666--687},
+  issn = {0035-8711},
+  doi = {10.1093/mnras/stac3446},
+  urldate = {2023-07-13},
+  abstract = {We present 849 new bursts from FRB 20121102A detected with the 305-m Arecibo Telescope. Observations were conducted as part of our regular campaign to monitor activity and evolution of burst properties. The 10 reported observations were carried out between 1150 and \$1730\textbackslash, \{\textbackslash rm MHz\}\$ and fall in the active period around 2018 November. All bursts were dedispersed at the same dispersion measure and are consistent with a single value of \$(562.4 \textbackslash pm 0.1)\textbackslash, \{\textbackslash rm pc\textbackslash, cm\^\{-3\}\}\$. The rate varies between 0 bursts and 218 {$\pm$} 16 bursts per hour, the highest rate observed to date. The times between consecutive bursts show a bimodal distribution. We find that a Poisson process with varying rate best describes arrival times with separations \$\{\}\{0.1\textbackslash, \{\textbackslash rm s\}\}\$. Clustering on time-scales of \$22\textbackslash, \{\textbackslash rm ms\}\$ reflects a characteristic time-scale of the source and possibly the emission mechanism. We analyse the spectro-temporal structure of the bursts by fitting 2D Gaussians with a temporal drift to each sub-burst in the dynamic spectra. We find a linear relationship between the sub-burst's drift and its duration. At the same time, the drifts are consistent with coming from the sad-trombone effect. This has not been predicted by current models. The energy distribution shows an excess of high-energy bursts and is insufficiently modelled by a single power law even within single observations. We find long-term changes in the energy distribution, the average spectrum, and the sad-trombone drift, compared to earlier and later published observations. Despite the large burst rate, we find no strict short-term periodicity.},
+  keywords = {method/modelling,status/hasread,study/Astrophysics/Fast Radio Bursts,supplementary/data},
+  annotation = {ADS Bibcode: 2023MNRAS.519..666J},
+  file = {C:\Users\LENOVO\Zotero\storage\D2J6RAJU\Jahns et al_2023_The FRB 20121102A November rain in 2018 observed with the Arecibo Telescope.pdf}
+}