Push Jamie's talk.

Author: vgvassilev

_data/meetinglist.yml

@@ -1,3 +1,50 @@
+- name: "CaaS Monthly Meeting"
+  date: 2025-05-15 17:00:00 +0200
+  time_cest: "17:00"
+  connect: "[Link to zoom](https://princeton.zoom.us/j/94431046845?pwd=D5i77Qb0PgfwwIubvbo2viEunne7eQ.1)"
+  label: caas_15May2025
+  agenda:
+    - title: "Accelerating simulation-based inference in RooFit at LHCb with Clad"
+      speaker:
+        image: "https://gitlab.cern.ch/uploads/-/system/user/avatar/23455/avatar.png?width=192"
+        name: "Jamie Gooding"
+        bio: |
+          Jamie Gooding is a 3rd year Doctoral Student at Technische Universitat
+          (TU) Dortmund and SMARTHEP Early Stage Researcher, working on the LHCb
+          experiment and currently completing a secondment with the ROOT
+          team. His research focuses on developing real-time analysis techniques
+          to enable studies of charge-parity and lepton-flavour violation in
+          neutral B meson decays, with an additional interest in computational
+          tools for statistical analyses in HEP. Prior to starting his doctoral
+          studies at TU Dortmund, he completed a Physics MPhys (Hons) at the
+          University of Manchester.        
+      description: |
+        Simulation-based inference (SBI) is a set of statistical inference
+        approaches in which Machine Learning (ML) algorithms are trained to
+        approximate likelihood ratios, e.g., as an alternative to the likelihood
+        fits commonly performed in HEP analyses. A demonstrator has been 
+        developed in which SBI is applied to extract parameters of interest from
+        the kinematic and angular distributions of
+        $B^0 \rightarrow D^{*-} \mu^+ \nu_\mu​$ decays in pseudodata samples
+        generated with RapidSim representative of the datasets used in LHCb
+        analysis. The SBI fit is constructed using the RooFit framework, to
+        which enhanced Python interfaces were recently introduced. Dense Neural
+        Networks (DNNs) were trained to distinguish between the Standard Model
+        and New Physics scenarios for varying parameters of interest. This
+        workflow incorporates automatic differentiation (AD) of the learned
+        likelihoods, using the ROOT SOFIE framework to generate C++ code from
+        the DNNs, from which gradient code is generated by source-code
+        transformation AD with Clad. In this talk, the SBI fit is introduced and
+        compared to the current state-of-the-art. Additionally, this talk
+        discusses the functionality introduced to enable differentiation from
+        likelihood through to ML classifier call and the impact of this
+        functionality upon performance of the SBI fit.
+    - title: Update
+      speaker: Vassil Vassilev
+    - title: Next meeting
+      speaker: Vassil Vassilev
+      link: 05 Jun 1700
+
 - name: "CaaS Monthly Meeting"
   date: 2024-12-12 17:00:00 +0200
   time_cest: "17:00"

_includes/head.html

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