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**Purpose:** Track complex concepts, design decisions, and review feedback
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---
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## 2. Example Data - itteration 1 (27.10,2025 11:00)
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Your version is too long and includes parts that do not reflect the reality of the project. The main purpose of the document is to motivate the development of a generic interface.
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I am not sure how to proceed. I suggest asking GPT and Gemini to review the conceptual part of section 2. Please provide a question based on my considerations below. Before proceeding, we need to resolve the issues with the scope, purpose, and length of this section.
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Additionally, in this particular case, it may be simpler if I edit it directly. Should I do that?
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Section Dataset A: TPC Spatial Distortion Maps (Test Data) was based on my example, so it closely matches our actual situation.
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2.3 Dataset B: TPC Temporal Evolution (Production Scale) was not described by me, so it does not reflect reality. I can prepare a shortened version. In this section, I want to highlight one important aspect from real practice: I use modified variables of interest – for example, instead of pt, I use q/pt, as many QA variables are more linear in q/pt.
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## Motivation - Iteration 1 (2025-10-27 07:00)
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Before answering the questions, I would like to describe in more detail what is being done and why.
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* 0.) We are trying not only to describe a multidimensional function but also to estimate statistical
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properties of the probability density function (PDF) itself (e.g. using quantiles).
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* 1.) LHC/my specific: We are working with both unbinned and binned data, as well as machine learning
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algorithms, depending on data availability. In the case of ALICE, we usually have a huge amount of data.
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For example, for tracks we have 500 kHz × 10 → 5 × 10^6 tracks per second, measuring for O(10–15 hours) per
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day. This data is either histogrammed in multidimensional histograms or, by default, we sample it using
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"balanced semi-stratified" sampling, populating the variables of interest homogeneously (e.g. flat pt, flat PID).
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This is very important as PDF of Pt and PID is highly unbalanced (exponential, power-law, etc).
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With this approach, we reduce the input data volume by an order of magnitude and enable iterative refinement
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of the PDF estimation.
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* 2.) Extracting PDF properties in multidimensional space has the advantage of enabling post-fitting of
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analytical models for normalised data. Quite often, we do not have analytical models for the full distortion
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in (3D+time), but we can have an analytical model for the delta distortion time evolution.
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In my current studies, for example, we are fitting a two- exponential phi-symmetric model of distortion
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due to common electric field modification.
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### Initial Questions (Iteration 1)
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**Q1:** Does this capture your motivation accurately?
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**A:** Several factors must be considered. Often we have large data but are limited by memory/CPU. Using >4GB in memory is problematic. Pre-sampling helps as original data is statistically highly unbalanced. The problem is not only sparsity - data is "random" and we need substantial statistics per bin.
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**Q2:** Should I emphasize more?
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**A:** Rewrite to emphasize statistical/mathematical considerations - PDF estimation and functional decomposition using partial models and factorization. Show ALICE examples. Software must be reusable.
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**Q3:** Tone - mathematical vs practical?
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**A:** Will ask GPT/Gemini. Some mathematics would be good but need balance.
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**Q4:** Missing key points?
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**A:** Emphasize statistical estimation problem. Motivation should be grounded in defined problems with ALICE examples. Highlight reusability and API design. Note: presented at forums but difficult to explain - people didn't understand statistical estimation, factorization, and usage in analytical model fitting with data renormalization.
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**Q5:** Add diagram?
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**A:** Yes, sparse 3D bins with ±1 neighborhood would help.
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---
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## Motivation - Iteration 2 (2025-10-27 09:00)
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### Additional Use Cases Added
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* Distortion maps (already in use)
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* Performance parameterization (e.g. track pT resolution as function of pT, eta, occupancy, time)
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* Track matching resolution and biases
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* V0 resolution and biases
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* PID resolution and biases
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* Efficiency maps
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* QA variables (chi2, number of clusters, etc.)
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* Usage in MC-to-Data remapping
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* Note: RootInteractive is only a small subproject for interactive visualisation of extracted data
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### Review Questions (Iteration 2)
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**Q1: Does Section 1 now accurately capture the key concepts?**
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*PDF estimation focus?*
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- More or less OK ✓
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*Balanced sampling strategy?*
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- Mentioned but need more details
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- In some use cases we sample down by factor of 10³–10⁴ to obtain manageable data size
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-**Action:** Added range 10×-10⁴× with typical 10²-10³× in Section 1.3.1 ✓
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*Factorization approach?*
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- Explained with TPC example
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-**Action:** Added note about temporal resolution (5-10 min maps vs O(s) for fluctuations) ✓
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*Connection to RootInteractive?*
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- RootInteractive is just one subproject for interactive visualization
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-**Action:** Added clarification that sliding window is server-side preprocessing ✓
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**Q2: Tone and depth**
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*Is mathematical level appropriate?*
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- Will ask GPT/Gemini for feedback → **See REVIEW_REQUEST_SECTION1.md**
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*Should I add equations?*
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- Yes, would enhance clarity
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- But ask GPT/Gemini first → **See REVIEW_REQUEST_SECTION1.md**
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*Is ALICE example clear?*
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- Need distortion map AND performance parameterization examples
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-**Action:** Added performance parameterization example in Section 1.1 ✓
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-**Action:** Expanded use cases in Section 1.5 ✓
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**Q3: Missing elements**
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*Key concepts still missed?*
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- Performance parameterization case added at beginning
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- Can mention in motivation categories and later in example sections
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-**Action:** Added to Section 1.1 and 1.5 ✓
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**Q4: Structure**
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*Are subsections (1.1-1.5) logical?*
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- Structure OK for now
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- Will ask GPT/Gemini → **See REVIEW_REQUEST_SECTION1.md**
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**Q5: Next steps**
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*Send to GPT/Gemini or continue to Section 2?*
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-**Decision:** Need GPT/Gemini review BEFORE proceeding to Section 2
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-**Action:** Created REVIEW_REQUEST_SECTION1.md with detailed questions ✓
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---
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## Status Summary
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**Section 1 - Motivation:**
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- Iteration 2 draft complete
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- Incorporates all user feedback from 2025-10-27 09:00
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- Ready for external review
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**Next Steps:**
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1. Send to GPT-4 for review
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2. Send to Gemini for review
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3. Address critical issues from both reviewers
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4. Finalize Section 1
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5. Proceed to Section 2 (Example Data)
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**Files:**
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-`SLIDING_WINDOW_SPEC_DRAFT.md` - Main specification document
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-`REVIEW_REQUEST_SECTION1.md` - Review questions for GPT/Gemini
**Overview:** The original sliding window implementation was developed in C++ within the ALICE AliRoot/O2 framework, using N-dimensional histograms as input structures.
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**Overview:** The original sliding window implementation was developed in C++ within the ALICE AliRoot framework,
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using N-dimensional histograms as input structures. The code has not yet been ported to the Run 3 O2 framework,
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and until recently it was used for Run 3 data with AliRoot as a side package.
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It was used for performance and dE/dx parameterisation, as well as the initial implementation of the TPC distortion
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maps in 2015. Q/q, track delta, and efficiency variables were grouped into histograms with the same binning.
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Several versions of binning with different granularity and focus were used, in order to bypass the ROOT internal
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limitation of 1 GB.
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Detector-based summary binning versions:
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* Kinematical variables (q/pt, tgl)
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* ~ occupancy
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* Phi/sector modulation (90 or 180 bins in the full phi range, or 10–20 bins per sector assuming sector symmetry)
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**Key features:**
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- Multi-dimensional histogram-based approach using ROOT's THnSparse
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- Efficient kernel lookups via histogram bin navigation
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- Support for various boundary conditions (mirror, truncate, periodic)
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- Multi-dimensional histogram-based approach using ROOT's THnSparse binned (1GBy limit)
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- O(10) varaiblae types x 5 biining types used (see comment above)
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- aggregation using smapled data on server (bash parallel comand), or farm if larger production
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- Sliding window implmentation as a proposprocessing step together with groupby regression
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- Kernel-based neighbor aggregation using histogram bin indexing
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- In addition to calluating sldiing window statistcs (mean,median, std,mad LTM) of variables of interest
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(dEdx,efficency,track deltai) aslo mean of varaibles used for binning (q/pt,eta,phi,occupancy)
/// * default cut is always applied, weight is applied on top
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/// * ranges syntax:
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/// * nbins,max,min where max and min are double or format strings
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/// * in case format string % specified using (Fraction, mean,meanFraction, rms, rmsFraction)
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/// * %fraction.sigma
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/// * #cumulant
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/// * range for bin content can be specified in the same format (by default is not set)
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/*!
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##### CPU time to process one histogram or set of histograms (in particular case of esdTrack queries) is the same - and it is determined (90 %) by tree->GetEntry
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