Learning to Identify Semi-Visible Jets (2208.10062v1)

Abstract: We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-$p_\textrm{T}$ jet constituents.

• The paper introduces a deep learning approach that classifies jets containing both Standard Model and dark sector particles.
• It leverages low-pT jet constituents to outperform traditional high-level observables in distinguishing semi-visible signals.
• The research paves the way for developing interpretable ML observables that enhance dark matter interaction studies.

Learning to Identify Semi-Visible Jets

The paper "Learning to Identify Semi-Visible Jets" addresses the challenge of identifying jets in particle physics that have components both from the visible Standard Model (SM) and from an invisible dark matter sector. In collider experiments, jets are complex and common events, making the specific identification of semi-visible jets particularly difficult because they occur when dark matter particles within the jet decay into both visible and dark sector particles. This research employs ML techniques to discern these semi-visible jet patterns, a notable step forward in high energy physics.

Key Points of the Paper

1. Semi-Visible Jets and Challenges: Semi-visible jets emerge from complex dark sector dynamics at colliders, such as those described in Hidden Valley models. Due to the concurrent presence of SM and dark sector particles, the jets create a unique pattern of detectable signal and missing transverse momentum. Existing jet substructure observables are not specifically tailored for these jets, creating a need for new approaches in their identification.
2. Machine Learning Approach: The paper utilizes a deep neural network to learn jet features from low-level constituents, without relying on high-level observables. This represents an opportunity to capture subtle features linked to the presence of dark sector particles in jets. The paper particularly focuses on exploiting low-$p_\textrm{T}$ jet constituents to gain classification power.
3. Experimentation and Results: Simulation data of jets from both dark sector-influenced processes and typical QCD background processes were generated, with different invisible fractions noted in various scenarios. Neural networks trained on low-level data outperformed those using high-level engineered features, suggesting untapped potential in low-$p_\textrm{T}$ constituents.
4. New Observables Identification: The guided search approach highlights the possibility of defining new high-level observables that can mimic the decisions made by neural networks. Although significant features were extracted, a persistent gap in performance remains, indicating the complexity of fully capturing the necessary discriminative information through a limited set of observables.
5. Insights on Low-$p_\textrm{T}$ Contribution: A deeper look into the role of low-$p_\textrm{T}$ constituents points to their relevance in identifying semi-visible jets, although they introduce challenges concerning modeling uncertainties due to their sensitivity to theoretical and hadronization parameters.

Implications and Future Directions

The findings in this paper highlight essential directions for future work. Primarily, the research inspires further efforts in understanding and interpreting ML models in particle physics, emphasizing the balance between leveraging low-level data and crafting robust high-level observables. The implication is clear: there is a wealth of information in low-level data that, if properly interpreted, could significantly advance our understanding of dark matter interactions.

The paper also suggests that improving the interpretability and calibration of ML models could mitigate the drawbacks of utilizing high-dimensional inputs directly, such as those prevalent in detector data. This research offers a blueprint for baseline performance comparison in future jet studies and presents a strong case for deeper investigation into semi-visible and dark matter phenomena at colliders. Future developments may focus on exploring novel ML architectures or a different basis for observable construction that can encapsulate the necessary physics within compact and interpretable forms.

Conclusion

This paper demonstrates the utility of machine learning in identifying semi-visible jets by exploiting detailed patterns in jet constituents. Its approach — leveraging information from low-$p_\textrm{T}$ components — serves as an innovative stride toward deciphering elusive dark sector signals. Continued exploration of ML interpretability in such contexts could unveil further insights into potential new physics, aiding the broader objective of dark matter detection and understanding.