TabNet ile Jet Türü Etiketleme: Bir Makine Öğrenimi Yaklaşımı

Year 2025, Volume: 12 Issue: 2, 589 - 599, 30.11.2025

Ali Çelik

https://doi.org/10.35193/bseufbd.1728150

Abstract

Yüksek enerjili çarpışmalardan kaynaklanan kolimlenmiş parçacık demetleri olan jetler, Büyük Hadron Çarpıştırıcısı'nda (BHÇ) oldukça yaygındır ve temel etkileşimlerin incelenmesinde kritik bir rol oynar. Bu çalışma, hadronik olarak bozunan üst kuark jetleri ile hafif kuark veya gluon jetlerinin sınıflandırılmasını ele almakta ve bu amaçla jet bileşenlerinin enerji, enine momentum ve bileşenleri gibi düşük seviyeli kinematik özelliklerinden yararlanarak TabNet algoritmasını kullanmaktadır. Verinin yüksek boyutluluğu ile başa çıkmak için boyut indirgeme yöntemi olarak Temel Bileşenler Analizi (TBA) uygulanmakta; TabNet’in dikkat mekanizması ise dinamik olarak en ilgili özellikleri seçmektedir. Model, %99.98 doğruluk, %99.97 duyarlılık (recall) ve 1.0 AUC ile olağanüstü bir sınıflandırma performansı sergilemektedir.

Keywords

Derin Öğrenme , Tabnet , Üst Kuark Etiketleme

TabNet for Jet Flavor Tagging: A Machine Learning Approach

Abstract

Jets, collimated particle sprays from high-energy collisions, are abundant at the Large Hadron Collider and critical for studying fundamental interactions. This study addresses the classification of hadronically decaying top quark jets and light quark or gluon jets using TabNet algorithm with leveraging low-level kinematic features of the jet constituents such as energy, transverse momentum, and components. To handle the high dimensionality of the data, Principal Component Analysis is applied for dimensionality reduction, while TabNet’s attention-based mechanism dynamically selects relevant features. The model achieves exceptional performance, with an accuracy of 99.98%, a recall of 99.97%, and an AUC of 1.0, demonstrating an outstanding classification performance.

Keywords

Deep Learning , Tabnet , Top Tagging

References

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