Nov update (#229)

* Add new papers * Mark with * what is still TODO * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add some papers * Update and forward to Claudius * Fix some trailing curly braces linked to issue 183 * testing updating references in pulled branch * Update until September 25th * Update until October 2nd * Update until October 18 * change order of papers. show newest first * Finish update until Nov 4 * Update until Nov 08 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Change update_plot back to False * Add CaloChallenge, run plot and recent * Remove duplicates from histogramming --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Claudius Krause <[email protected]>
iml-wg · Nov 11, 2024 · 6277ecd · 6277ecd
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diff --git a/HEPML.bib b/HEPML.bib
diff --git a/HEPML.tex b/HEPML.tex
diff --git a/README.md b/README.md
diff --git a/check_inspire.py b/check_inspire.py
@@ -20,7 +20,7 @@
 categories = ['hep-ph', 'hep-ex', 'hep-lat', 'hep-th', 'physics.ins-det', 'physics.data-an']
 print(f"Looking at arXiv categories: {categories}")
 
-keywords = ['neural network', 'machine learning', 'generative models', 'diffusion models', 'normalizing flow', 'foundation model']
+keywords = ['neural network', 'machine learning', 'generative models', 'diffusion models', 'normalizing flow', 'foundation model', 'ML']
 print(f"scanning papers with {keywords}:")
 keyword_str = f'("{keywords}")'.replace("', '", '"%20OR%20"').replace("['", '').replace("']", "")
 keyword_str = keyword_str.replace(" ", "%20")

diff --git a/cite_reverse_order.py b/cite_reverse_order.py
@@ -0,0 +1,34 @@
+import re
+
+
+def reverse_citations_in_tex(file_path, output_path=None):
+    r"""Reverse the order of citation keys in all \cite{...} blocks in a .tex file."""
+
+    # Read the content of the .tex file
+    with open(file_path) as file:
+        content = file.read()
+
+    # Regular expression to match \cite{...} blocks
+    cite_pattern = re.compile(r'\\cite\{([^}]+)\}')
+
+    # Function to reverse the order of keys within each cite block
+    def reverse_cite_keys(match):
+        keys = match.group(1).split(',')
+        reversed_keys = ','.join(keys[::-1])
+        return f'\\cite{{{reversed_keys}}}'
+
+    # Replace all \cite{...} blocks with reversed citation order
+    updated_content = cite_pattern.sub(reverse_cite_keys, content)
+
+    # Write the updated content back to the same file or a new file
+    output_file = output_path
+    with open(output_file, 'w') as file:
+        file.write(updated_content)
+
+    print(f"Updated citations saved to {output_file}")
+
+if __name__ == "__main__":
+    # Example usage
+    input_file = 'HEPML.tex'
+    output_file = 'HEPML_cite_reverse.tex'
+    reverse_citations_in_tex(input_file, output_file)
diff --git a/docs/assets/dark_per_year.png b/docs/assets/dark_per_year.png
diff --git a/docs/assets/per_year.png b/docs/assets/per_year.png
diff --git a/docs/index.md b/docs/index.md
diff --git a/docs/recent.md b/docs/recent.md
@@ -9,23 +9,96 @@ search:
 
 This is an automatically compiled list of papers which have been added to the living review that were made public within the previous 4 months at the time of updating. This is not an exhaustive list of released papers, and is only able to find those which have both year and month data provided in the bib reference.
 
-## August 2024
-* [Calibrating Bayesian Generative Machine Learning for Bayesiamplification](https://arxiv.org/abs/2408.00838)
-* [Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects](https://arxiv.org/abs/2408.01138) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01256-6)]
+## November 2024
+* [Improving smuon searches with Neural Networks](https://arxiv.org/abs/2411.04526) (2024)
+* [Rejection Sampling with Autodifferentiation -- Case study: Fitting a Hadronization Model](https://arxiv.org/abs/2411.02194) (2024)
+* [The Fundamental Limit of Jet Tagging](https://arxiv.org/abs/2411.02628) (2024)
+* [Generative Unfolding with Distribution Mapping](https://arxiv.org/abs/2411.02495) (2024)
+* [Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics](https://arxiv.org/abs/2411.02177) (2024)
+* [New Physics Through Flavor Tagging at FCC-ee](https://arxiv.org/abs/2411.02485) (2024)
+* [A versatile framework for attitude tuning of beamlines at advanced light sources](https://arxiv.org/abs/2411.01278) (2024)
+* [Profile Likelihoods on ML-Steroids](https://arxiv.org/abs/2411.00942) (2024)
+* [A Lorentz-Equivariant Transformer for All of the LHC](https://arxiv.org/abs/2411.00446) (2024)
+
+## October 2024
+* [Systematic Interpretability and the Likelihood for Boosted Top Quark Identification](https://arxiv.org/abs/2411.00104) (2024)
+* [Machine Learning Electroweakino Production](https://arxiv.org/abs/2411.00093) (2024)
+* [Point cloud-based diffusion models for the Electron-Ion Collider](https://arxiv.org/abs/2410.22421) (2024)
+* [Hybrid quantum-classical approach for combinatorial problems at hadron colliders](https://arxiv.org/abs/2410.22417) (2024)
+* [HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction](https://arxiv.org/abs/2410.23236) (2024)
+* [Variational inference for pile-up removal at hadron colliders with diffusion models](https://arxiv.org/abs/2410.22074) (2024)
+* [CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation](https://arxiv.org/abs/2410.21611) (2024)
+* [SIGMA: Single Interpolated Generative Model for Anomalies](https://arxiv.org/abs/2410.20537) (2024)
+* [On learning higher-order cumulants in diffusion models](https://arxiv.org/abs/2410.21212) (2024)
+* [Diffusion models for lattice gauge field simulations](https://arxiv.org/abs/2410.19602) (2024)
+* [cymyc -- Calabi-Yau Metrics, Yukawas, and Curvature](https://arxiv.org/abs/2410.19728) (2024)
+* [Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoods](https://arxiv.org/abs/2410.18553) (2024)
+* [A novel quantum machine learning classifier to search for new physics](https://arxiv.org/abs/2410.18847) (2024)
+* [Probing Light Scalars and Vector-like Quarks at the High-Luminosity LHC](https://arxiv.org/abs/2410.17854) (2024)
+* [Performance of the CMS high-level trigger during LHC Run 2](https://arxiv.org/abs/2410.17038) (2024)
+* [Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks](https://arxiv.org/abs/2410.14760) (2024)
+* [Fast Perfekt: Regression-based refinement of fast simulation](https://arxiv.org/abs/2410.15992) (2024)
+* [Machine Learning-Powered Data Cleaning for LEGEND](https://arxiv.org/abs/2410.14701) (2024)
+* [Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM and BSM Processes](https://arxiv.org/abs/2410.13904) (2024)
+* [MACK: Mismodeling Addressed with Contrastive Knowledge](https://arxiv.org/abs/2410.13947) (2024)
+* [Exploring jets: substructure and flavour tagging in CMS and ATLAS](https://arxiv.org/abs/2410.14330) (2024)
+* [Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC](https://arxiv.org/abs/2410.13799) (2024)
+* [Learning Efficient Representations of Neutrino Telescope Events](https://arxiv.org/abs/2410.13148) (2024)
+* [Observation of a rare beta decay of the charmed baryon with a Graph Neural Network](https://arxiv.org/abs/2410.13515) (2024)
+* [Continuous normalizing flows for lattice gauge theories](https://arxiv.org/abs/2410.13161) (2024)
+* [Application of Particle Transformer to quark flavor tagging in the ILC project](https://arxiv.org/abs/2410.11322) (2024)
+* [Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm](https://arxiv.org/abs/2410.11284) (2024)
+* [Machine learning tagged boosted dark photon: A signature of fermionic portal matter at the LHC](https://arxiv.org/abs/2410.06925) (2024)
+* [Machine learning opportunities for online and offline tagging of photo-induced and diffractive events in continuous readout experiments](https://arxiv.org/abs/2410.06983) (2024)
+* [Advancing Tools for Simulation-Based Inference](https://arxiv.org/abs/2410.07315) (2024)
+* [Measurements of decay branching fractions of the Higgs boson to hadronic final states at the CEPC](https://arxiv.org/abs/2410.04465) (2024)
+* [Building Hadron Potentials from Lattice QCD with Deep Neural Networks](https://arxiv.org/abs/2410.03082) (2024)
+* [FAIR Universe HiggsML Uncertainty Challenge Competition](https://arxiv.org/abs/2410.02867) (2024)
+* [Real-time Position Reconstruction for the KamLAND-Zen Experiment using Hardware-AI Co-design](https://arxiv.org/abs/2410.02991) (2024)
+* [Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS](https://arxiv.org/abs/2410.02945) (2024)
+* [Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline](https://arxiv.org/abs/2410.00755) (2024)
 
-## July 2024
-* [AI for Nuclear Physics: the EXCLAIM project](https://arxiv.org/abs/2408.00163)
-* [TASI Lectures on Physics for Machine Learning](https://arxiv.org/abs/2408.00082)
-* [Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees](https://arxiv.org/abs/2407.21239)
-* [Universal New Physics Latent Space](https://arxiv.org/abs/2407.20315)
-* [Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection](https://arxiv.org/abs/2407.21008)
-* [Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring](https://arxiv.org/abs/2407.20278)
-* [Accelerating template generation in resonant anomaly detection searches with optimal transport](https://arxiv.org/abs/2407.19818)
-* [Probing Charm Yukawa through $ch$ Associated Production at the Hadron Collider](https://arxiv.org/abs/2407.19797)
-* [Accuracy versus precision in boosted top tagging with the ATLAS detector](https://arxiv.org/abs/2407.20127) [[DOI](https://doi.org/10.1088/1748-0221/19/08/P08018)]
-* [Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes](https://arxiv.org/abs/2407.19010)
-* [The Observation of a 95 GeV Scalar at Future Electron-Positron Colliders](https://arxiv.org/abs/2407.16806)
-* [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704)
-* [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://arxiv.org/abs/2407.13925)
-* [Exploring Top-Quark Signatures of Heavy Flavor-Violating Scalars at the LHC with Parametrized Neural Networks](https://arxiv.org/abs/2407.12118)
+## September 2024
+* [Novel machine learning applications at the LHC](https://arxiv.org/abs/2409.20413) (2024)
+* [Nanosecond hardware regression trees in FPGA at the LHC](https://arxiv.org/abs/2409.20506) (2024)
+* [Bootstrapping string models with entanglement minimization and Machine-Learning](https://arxiv.org/abs/2409.18259) (2024)
+* [Polarized and unpolarized gluon PDFs: generative machine learning applications for lattice QCD matrix elements at short distance and large momentum](https://arxiv.org/abs/2409.17234) (2024)
+* [A Variational Approach to Quantum Field Theory](https://arxiv.org/abs/2409.17887) (2024)
+* [Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA](https://arxiv.org/abs/2409.16075) (2024)
+* [Machine Learning Toric Duality in Brane Tilings](https://arxiv.org/abs/2409.15251) (2024)
+* [Signal model parameter scan using Normalizing Flow](https://arxiv.org/abs/2409.13201) [[DOI](https://doi.org/10.22323/1.458.0017)] (2024)
+* [Is Tokenization Needed for Masked Particle Modelling?](https://arxiv.org/abs/2409.12589) (2024)
+* [Conformal Fields from Neural Networks](https://arxiv.org/abs/2409.12222) (2024)
+* [Unveiling the Secrets of New Physics Through Top Quark Tagging](https://arxiv.org/abs/2409.12085) (2024)
+* [Search for light long-lived particles decaying to displaced jets in proton-proton collisions at $\sqrt{s}$](https://arxiv.org/abs/2409.10806) (2024)
+* [Evaluating Modifications to Classifiers for Identification of Higgs Bosons](https://arxiv.org/abs/2409.10902) (2024)
+* [Reinforcement learning-based statistical search strategy for an axion model from flavor](https://arxiv.org/abs/2409.10023) (2024)
+* [Multidimensional Deconvolution with Profiling](https://arxiv.org/abs/2409.10421) (2024)
+* [Converting sWeights to Probabilities with Density Ratios](https://arxiv.org/abs/2409.08183) (2024)
+* [Detect anomalous quartic gauge couplings at muon colliders with quantum kernel k-means](https://arxiv.org/abs/2409.07010) (2024)
+* [ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks](https://arxiv.org/abs/2409.03065) (2024)
+* [Application of Kolmogorov-Arnold Networks in high energy physics](https://arxiv.org/abs/2409.01724) (2024)
+* [An Extended Closure Relation by LightGBM for Neutrino Radiation Transport in Core-collapse Supernovae](https://arxiv.org/abs/2409.02719) (2024)
+* [A novel machine learning method to detect double-$\Lambda$ hypernuclear events in nuclear emulsions](https://arxiv.org/abs/2409.01657) (2024)
+
+## August 2024
+* [Variational Monte Carlo with Neural Network Quantum States for Yang-Mills Matrix Model](https://arxiv.org/abs/2409.00398) (2024)
+* [Estimating event-by-event multiplicity by a Machine Learning Method for Hadronization Studies](https://arxiv.org/abs/2408.17130) (2024)
+* [Semi-supervised permutation invariant particle-level anomaly detection](https://arxiv.org/abs/2408.17409) (2024)
+* [Vertex Imaging Hadron Calorimetry Using AI/ML Tools](https://arxiv.org/abs/2408.15385) (2024)
+* [Estimation of the pseudoscalar glueball mass based on a modified Transformer](https://arxiv.org/abs/2408.13280) (2024)
+* [Multiple testing for signal-agnostic searches of new physics with machine learning](https://arxiv.org/abs/2408.12296) (2024)
+* [From strange-quark tagging to fragmentation tagging with machine learning](https://arxiv.org/abs/2408.12377) (2024)
+* [RODEM Jet Datasets](https://arxiv.org/abs/2408.11616) (2024)
+* [Full Detector Simulation of a Projective Dual-Readout Segmented Crystal Electromagnetic Calorimeter with Precision Timing](https://arxiv.org/abs/2408.11027) (2024)
+* [Electron-nucleus cross sections from transfer learning](https://arxiv.org/abs/2408.09936) (2024)
+* [Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution](https://arxiv.org/abs/2408.08474) (2024)
+* [Bayesian Inference analysis of jet quenching using inclusive jet and hadron suppression measurements](https://arxiv.org/abs/2408.08247) (2024)
+* [Pay Attention To Mean Fields For Point Cloud Generation](https://arxiv.org/abs/2408.04997) (2024)
+* [Learning the Simplicity of Scattering Amplitudes](https://arxiv.org/abs/2408.04720) (2024)
+* [Modelling parametric uncertainty in PDEs models via Physics-Informed Neural Networks](https://arxiv.org/abs/2408.04690) (2024)
+* [Neural Network Modeling of Heavy-Quark Potential from Holography](https://arxiv.org/abs/2408.03784) (2024)
+* [Calibrating Bayesian Generative Machine Learning for Bayesiamplification](https://arxiv.org/abs/2408.00838) (2024)
+* [Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects](https://arxiv.org/abs/2408.01138) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01256-6)] (2024)
+* [Differentiable MadNIS-Lite](https://arxiv.org/abs/2408.01486) (2024)
 
diff --git a/dump_bibtex_from_arxiv.py b/dump_bibtex_from_arxiv.py
@@ -34,7 +34,10 @@ def replace_collaboration_author(bib_entry):
         'ALICE': 'ALICE Collaboration',
         'IceCube': 'IceCube Collaboration',
         'NNPDF': 'NNPDF Collaboration',
-        'Belle-II': "Belle-II Collaboration"
+        'Belle-II': "Belle-II Collaboration",
+        'BESIII': "BESIII Collaboration",
+        'DARWIN': "DARWIN Collaboration",
+        'JETSCAPE': "JETSCAPE Collaboration"
     }
 
     # Find the collaboration field and select matching name above

diff --git a/make_md.py b/make_md.py
@@ -27,6 +27,9 @@
     file.write("*Modern machine learning techniques, including deep learning, is rapidly being applied, adapted, and developed for high energy physics.  The goal of this document is to provide a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses.  As a living document, it will be updated as often as possible to incorporate the latest developments.  A list of proper (unchanging) reviews can be found within.  Papers are grouped into a small set of topics to be as useful as possible.  Suggestions are most welcome.*\n\n")
     file.write("[![download](https://img.shields.io/badge/download-review-blue.svg)](https://iml-wg.github.io/HEPML-LivingReview/assets/hepml_review.pdf)\n[![github](https://badges.aleen42.com/src/github.svg)](https://github.com/iml-wg/HEPML-LivingReview)\n\n")
 
+# Define some empty lists
+YEARS_FOR_PLOT = []
+CITE_KEY_LIST = []
 
 # neccessary in testing since readme and out file are at different locations.
 # once merged to master, link can point to file on master branch for both
@@ -128,7 +131,7 @@ def convert_from_bib(myline):
             myentry_dict["year"] = entry_cleaned.split("year")[1].split("=")[1].split("\n")[0].replace("\"","").replace("{","").replace("}","").replace(",","")
             pass
         elif "doi" in first_entry:
-            myentry_dict["doi"] = entry_cleaned.split("doi")[1].split("=")[1].split("\n")[0].replace("\"","").replace(",","").replace("\'","").replace(" ","")
+            myentry_dict["doi"] = entry_cleaned.split("doi")[1].split("=")[1].split("\n")[0].replace("\"","").replace(",","").replace("\'","").replace(" ","").replace("{","")
         elif "url" in first_entry:
             if "@" in first_entry:
                 continue
@@ -178,27 +181,34 @@ def convert_from_bib(myline):
             paper=f" [[DOI](https://doi.org/{myentry_dict['doi']})]"
         elif "url" in myentry_dict:
             paper=f" [[url]({myentry_dict['url']})]"
-        YEARS_FOR_PLOT.append(year)
+        if myline not in CITE_KEY_LIST:
+            YEARS_FOR_PLOT.append(year)
+            CITE_KEY_LIST.append(myline)
         return "["+myentry_dict["title"]+"](https://arxiv.org/abs/"+myentry_dict["eprint"]+")"+paper+year
     elif "doi" in myentry_dict:
         year=""
         if "year" in myentry_dict:
             year = f" ({myentry_dict['year']})"
-            YEARS_FOR_PLOT.append(year)
+            if myline not in CITE_KEY_LIST:
+                YEARS_FOR_PLOT.append(year)
+                CITE_KEY_LIST.append(myline)
         return "["+myentry_dict["title"]+"](https://doi.org/"+myentry_dict["doi"]+")"+year
     elif "url" in myentry_dict:
         year=""
         if "year" in myentry_dict:
             year = f" ({myentry_dict['year']})"
-            YEARS_FOR_PLOT.append(year)
+            if myline not in CITE_KEY_LIST:
+                YEARS_FOR_PLOT.append(year)
+                CITE_KEY_LIST.append(myline)
         return "["+myentry_dict["title"]+"]("+myentry_dict["url"]+")"+year
     else:
         year=""
         if "year" in myentry_dict:
             year = f" ({myentry_dict['year']})"
-            YEARS_FOR_PLOT.append(year)
+            if myline not in CITE_KEY_LIST:
+                YEARS_FOR_PLOT.append(year)
+                CITE_KEY_LIST.append(myline)
         return myentry_dict["title"]+year
-    return myline
 
 def write_to_files(*args,readme=myfile_readme,webpage=myfile_out,add_header=False):
     for line in args:
@@ -220,7 +230,6 @@ def write_to_files(*args,readme=myfile_readme,webpage=myfile_out,add_header=Fals
             webpage.write("\n??? example "+"\""+split[-1].strip()+"\"\n\n")
 
 itemize_counter = 0
-YEARS_FOR_PLOT = []
 for line in myfile:
 
     if "author" in line: