Pushing first set of changes for BsMuMu, adding 3 updated episodes, correcting title, etc.

Author: guptaharshul24

AUTHORS

@@ -1,2 +1,2 @@
- * [Martin Kwok](https://github.com/kakwok)
- * [Christina Wang](https://github.com/Christinaw97/) 
+ * [Chris Cosby](https://github.com/cosbyc)
+ * [Harshul Gupta](https://github.com/guptaharshul24) 

_config.yml

@@ -11,7 +11,7 @@
 carpentry: "swc"
 
 # Overall title for pages.
-title: "CMSDAS: Muon Detector Shower (MDS) Long Exercise"
+title: "CMSDAS: Measurement of B0s→μ+μ− Long Exercise"
 
 # Life cycle stage of the lesson
 # See this page for more details: https://cdh.carpentries.org/the-lesson-life-cycle.html
@@ -44,7 +44,7 @@ kind: "lesson"
 # Magic to make URLs resolve both locally and on GitHub.
 # See https://help.github.com/articles/repository-metadata-on-github-pages/.
 # Please don't change it: <USERNAME>/<PROJECT> is correct.
-repository: kakwok/MDS_CMSDAS
+repository: FNALLPC/LongExerciseBsMuMu
 
 # Email address, no mailto:
 email: "team@carpentries.org"

_episodes/01-introduction.md

@@ -1,20 +1,52 @@
+
 ---
 title: "Introduction"
-teaching: 30
+teaching: 15
 exercises: 0
 questions:
-- Why do we search for long-lived particles?
-- Why do we use muon detectors?
-- What signatures are we looking for?
+- "What is the physics motivation for measuring B0s→μ+μ−?"
+- "What is the overall analysis strategy?"
 objectives:
-- "Understand the motivation to search for LLPs with muon detectors"
-- "Understand the high-multiplicity signature from LLPs due to the unique CMS muon detector design"
-- "Have an understanding of the overall analysis strategy and background estimation"
+- "Understand why B0s→μ+μ− is a sensitive probe of new physics."
+- "Know the key ingredients of the branching fraction measurement."
+- "Understand the role of the normalization channel."
 keypoints:
-- "Searching for LLPs with the CMS muon detectors that is interleaved with steel return yoke give rise to unique high multiplicity signature that allow us to be sensitive to a broad range of LLP decay modes and to LLP masses below GeV"
+- "B0s→μ+μ− is a FCNC decay heavily suppressed in the SM — new physics can enhance it."
+- "The branching fraction is extracted from a simultaneous fit across BDT categories."
+- "B+→J/ψK+ serves as the normalization channel to cancel many systematic uncertainties."
 ---
 
-We will go through the [introductory slides](https://indico.cern.ch/event/1462056/contributions/6313496/attachments/2996184/5278686/CMSDAS_2025.pdf) to give you an overview of the motivation to search for LLP with the CMS muon detectors, the analysis strategy, background estimation methods, and limit setting on LLP cross section.
+## Physics motivation
+
+<!-- TODO: paste/expand from TWiki introduction section -->
+
+The decay B<sup>0</sup><sub>s</sub>→μ<sup>+</sup>μ<sup>−</sup> is a Flavour-Changing Neutral
+Current (FCNC) process. In the Standard Model it is loop- and helicity-suppressed, giving a
+branching fraction of:
+
+**BF(B<sup>0</sup><sub>s</sub>→μ<sup>+</sup>μ<sup>−</sup>) ≈ 3.66 × 10<sup>−9</sup>**
+
+Many beyond-SM scenarios (SUSY, leptoquarks, extra dimensions) predict significant deviations
+from this value, making it one of the most sensitive indirect probes of new physics at the LHC.
+
+## Analysis strategy
+
+<!-- TODO: paste from TWiki -->
+
+The measurement follows the strategy of the CMS Run-2 paper [BPH-21-006](https://cms-results.web.cern.ch/cms-results/public-results/publications/BPH-21-006/index.html):
+
+1. Select B<sub>s</sub>→μμ candidates and classify them into **8 BDT categories** based on signal/background discrimination.
+2. Model the **signal PDF** using a double Gaussian + Crystal Ball shape fitted to MC.
+3. Model **background PDFs**: combinatorial (Bernstein), peaking (KDE from MC), semileptonic (KDE from MC).
+4. Fit the **normalization channel** B<sup>+</sup>→J/ψK<sup>+</sup> in data to extract the observed yield and efficiency.
+5. Perform a **simultaneous unbinned maximum likelihood fit** across all 8 categories to extract BF(B<sub>s</sub>→μμ).
+
+## Branching fraction formula
+
+The branching fraction is extracted via:
 
-{% include links.md %}
+$$
+\text{BF}(B_s \to \mu\mu) = \frac{N_{B_s}}{N_{B^+}} \cdot \frac{\varepsilon_{B^+}}{\varepsilon_{B_s}} \cdot \frac{f_u}{f_s} \cdot \text{BF}(B^+ \to J/\psi K^+)
+$$
 
+where f<sub>u</sub>/f<sub>s</sub> is the ratio of B<sup>+</sup> to B<sup>0</sup><sub>s</sub> production fractions.

_episodes/02-LLP_intro.md

@@ -0,0 +1,84 @@
+---
+title: "Signal MC Fit"
+teaching: 10
+exercises: 40
+questions:
+- "How do we model the B+→J/ψK+ signal peak?"
+- "How do we apply MC-derived shape parameters to data?"
+objectives:
+- "Fit a double Gaussian model to B+→J/ψK+ MC."
+- "Understand mean shift and resolution scale corrections."
+- "Fit the full signal+background model to data."
+keypoints:
+- "The signal shape is fixed from MC, with a floating mean shift and resolution scale fitted in data."
+- "The combinatorial background uses an exponential; the J/ψ+X tail uses an error function."
+---
+
+## Task 2.1 — Double Gaussian fit to MC (category 0)
+
+Fit a double Gaussian model to the B<sup>+</sup>→J/ψK<sup>+</sup> MC in category 0.
+The invariant mass range is 5.0–5.8 GeV.
+
+<!-- TODO: add figure from task_2_1 output -->
+
+> ## Task 2.1
+>
+> Open `task_2_1.py` (or `task_2_1.C`) and run the double Gaussian fit to MC.
+> Record the fitted parameters — you will use them as starting values in later tasks.
+>
+> ```python
+> python task_2_1.py
+> ```
+{: .challenge}
+
+## Task 2.2 — Fit data with fixed signal shape
+
+Use the MC-derived signal shape (fixed parameters) and fit the B<sup>+</sup>→J/ψK<sup>+</sup>
+data with a signal + combinatorial + J/ψ+X background model.
+
+<!-- TODO: add figure from task_2_2 output -->
+
+> ## Task 2.2
+>
+> Run `task_2_2.py`. The signal PDF parameters are hard-coded from the Task 2.1 MC fit result.
+> Observe the fit quality and check the yield.
+{: .challenge}
+
+## Task 2.3 — Fit data with mean shift and resolution scale corrections
+
+Introduce two free parameters:
+- `sig_shift`: a common mean shift applied to both Gaussians
+- `sig_scale`: a common resolution scale factor applied to both sigmas
+
+These correct for known data/MC differences.
+
+<!-- TODO: add figure from task_2_3 output -->
+
+> ## Task 2.3
+>
+> Run `task_2_3.py`. Compare the fitted `sig_shift` and `sig_scale` to unity/zero.
+> Are the data/MC corrections significant?
+{: .challenge}
+
+## Task 2.4 — Repeat for category 1
+
+Repeat Tasks 2.1–2.3 for BDT category 1: fit the MC first, then fit the data with corrections.
+
+<!-- TODO: add figure from task_2_4 output -->
+
+> ## Task 2.4
+>
+> Run `task_2_4.py`. Note that the MC parameters are different for category 1.
+{: .challenge}
+
+## Task 2.5 — Bs→J/ψφ signal fit
+
+Repeat the MC+data fit for the B<sub>s</sub>→J/ψφ channel (mass peak near 5.37 GeV).
+
+<!-- TODO: add figure from task_2_5 output -->
+
+> ## Task 2.5
+>
+> Run `task_2_5.py`. Note the different mass peak position and the simpler background
+> (no J/ψ+X tail needed for the B<sub>s</sub>→J/ψφ channel).
+{: .challenge}