arXiv subjects

Timothy Andeen

Publications and source records attributed to Timothy Andeen.

7 recordsLinked to original sources

arXiv2026

Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments

Electromagnetic calorimeters provide essential information for reconstructing and selecting both Standard Model (SM) and potential beyond the SM physics events at high-energy particle colliders. The fine-grained segmentation of modern calorimeters captures rich information about the internal structure of particle showers, much of which is discarded by conventional high-level reconstruction methods. In this work, we leverage calorimeter cell-level information to classify highly collimated diphoton signatures, arising from the decay of light axion-like particles, from isolated single-photon showers. We systematically compare a range of machine learning architectures, spanning high-level, shower shape variable-based approaches and direct cell-level methods. Cell-level machine learning shows significantly superior classification ability, with a Transformer in particular representing the best performance among six different architectures studied, and an MLP Mixer representing a resource-constrained alternative for potential real-time, trigger-level applications. Beyond classification, the Transformer model developed enables direct invariant mass regression from calorimeter cells, improving the characterization of light resonances and providing an additional handle in reducing the $\pi^0$ and $\eta$ fake photon backgrounds. These results demonstrate that cell-level machine learning methods can extend calorimeter-based particle identification and performance well beyond the capabilities of current conventional techniques.

hep-ph↗

arXiv2022

Readout for Calorimetry at Future Colliders: A Snowmass 2021 White Paper

Calorimeters will provide critical measurements at future collider detectors. As the traditional challenge of high dynamic range, high precision, and high readout rates for signal amplitudes is compounded by increasing granularity and precision timing the readout systems will become increasingly complex. This white paper reviews the challenges and opportunities in calorimeter readout at future collider detectors.

physics.ins-det↗

arXiv2022

Non-resonant Diagrams for Single Production of Top and Bottom Partners

The search for Top and Bottom Partners is a major focus of analyses at both the ATLAS and CMS experiments. When singly produced, these vector-like partners of the Standard Model third generation quarks retain a sizeable cross-section that makes them attractive search candidates in their respective topologies. While most efforts have concentrated on the resonant mode for single production of these hypothetical particles, the most dominant mode at narrow widths, recent studies have revealed a wide and rich phenomenology involving the non-resonant diagrams. In this letter we categorically investigate the impact of the non-resonant diagrams on different Top and Bottom partner single production topologies and their impact on the inclusive cross-section estimation. We also propose a parameterization for calculating the the correction factor to the single vector-like quark production cross-section when such diagrams are included.

hep-ph↗

arXiv2021

Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks

Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function. This paper shows that a PINN can be sensitive to errors in training data and overfit itself in dynamically propagating these errors over the domain of the solution of the PDE. It also shows how physical regularizations based on continuity criteria and conservation laws fail to address this issue and rather introduce problems of their own causing the deep network to converge to a physics-obeying local minimum instead of the global minimum. We introduce Gaussian Process (GP) based smoothing that recovers the performance of a PINN and promises a robust architecture against noise/errors in measurements. Additionally, we illustrate an inexpensive method of quantifying the evolution of uncertainty based on the variance estimation of GPs on boundary data. Robust PINN performance is also shown to be achievable by choice of sparse sets of inducing points based on sparsely induced GPs. We demonstrate the performance of our proposed methods and compare the results from existing benchmark models in literature for time-dependent Schr\"odinger and Burgers' equations.

cs.LG↗

arXiv2020

Novel Interpretation Strategy for Searches of Singly Produced Vector-like Quarks at the LHC

Vector-like Quarks (VLQs) are potential signatures of physics beyond the Standard Model at the TeV energy scale and major efforts have been put forward at both ATLAS and CMS experiments in search of these particles. In order to make these search results more relatable in the context of most plausible theories of VLQs, it is deemed important to present the analysis results in a general fashion. We investigate the challenges associated with such interpretations of singly produced VLQ searches and propose a generalized, semi-analytical framework that allows a model-independent casting of the results in terms of unconstrained, free parameters of the VLQ Lagrangian. We also propose a simple parameterization of the correction factor to the single VLQ production cross-section at large decay widths. We illustrate how the proposed framework can be used to conveniently represent statistical limits by numerically reinterpreting results from benchmark ATLAS and CMS analyses.

hep-ph↗

arXiv2017

A radiation-hard dual-channel 12-bit 40 MS/s ADC prototype for the ATLAS liquid argon calorimeter readout electronics upgrade at the CERN LHC

The readout electronics upgrade for the ATLAS Liquid Argon Calorimeters at the CERN Large Hadron Collider requires a radiation-hard ADC. The design of a radiation-hard dual-channel 12-bit 40 MS/s pipeline ADC for this use is presented. The design consists of two pipeline A/D channels each with four Multiplying Digital-to-Analog Converters followed by 8-bit Successive-Approximation-Register analog-to-digital converters. The custom design, fabricated in a commercial 130 nm CMOS process, shows a performance of 67.9 dB SNDR at 10 MHz for a single channel at 40 MS/s, with a latency of 87.5 ns (to first bit read out), while its total power consumption is 50 mW/channel. The chip uses two power supply voltages: 1.2 and 2.5 V. The sensitivity to single event effects during irradiation is measured and determined to meet the system requirements.

physics.ins-det↗

arXiv2013

A Radiation-Hard Dual Channel 4-bit Pipeline for a 12-bit 40 MS/s ADC Prototype with extended Dynamic Range for the ATLAS Liquid Argon Calorimeter Readout Electronics Upgrade at the CERN LHC

The design of a radiation-hard dual channel 12-bit 40 MS/s pipeline ADC with extended dynamic range is presented, for use in the readout electronics upgrade for the ATLAS Liquid Argon Calorimeters at the CERN Large Hadron Collider. The design consists of two pipeline A/D channels with four Multiplying Digital-to-Analog Converters with nominal 12-bit resolution each. The design, fabricated in the IBM 130 nm CMOS process, shows a performance of 68 dB SNDR at 18 MHz for a single channel at 40 MS/s while consuming 55 mW/channel from a 2.5 V supply, and exhibits no performance degradation after irradiation. Various gain selection algorithms to achieve the extended dynamic range are implemented and tested.

physics.ins-det↗