The Relativistic Heavy Ion Collider collides heavy nuclei at ultrarelativistic energies, creating a strongly interacting, partonic medium that is opaque to the passage of high energy quarks and gluons. Direct jet reconstruction applied to these collision systems provides a crucial constraint on the mechanism for in-medium parton energy loss and jet-medium interactions. However, traditional jet reconstruction algorithm operating in the large soft background at RHIC give rise to fake jets well above the intrinsic production rate of high-pT partons, impeding the detection of the low cross section jet signal at RHIC energies. We developed a new jet reconstruction algorithm that uses a Gaussian filter to locate and reconstruct the jet energy. This algorithm is combined with a fake jet rejection scheme that provides efficient jet reconstruction with acceptable fake rate in a background environment up to the central Au + Au collision at sqrt(s_NN) = 200 GeV. We present results of its application in p + p and Cu + Cu collisions using data from the PHENIX detector, namely p + p cross section, Cu + Cu jet yields, the Cu + Cu nuclear modification factor, and Cu + Cu jet-jet azimuthal correlation.

A new algorithm for jet finding in hadronic collisions is presented. The algorithm, based on a Gaussian filter in $(\eta,\phi)$, is specifically intended for use in heavy ion collisions and/or for detectors with limited acceptance. The performance of the algorithm is compared to two conventional algorithms, a seedless cone algorithm and a $k_\perp$ algorithm, for Pythia simulated di-jet events in $\sqrt{s} = 200 \mathrm{GeV}$ $p + p$ collisions with $4 \mathrm{GeV}/c \le \sqrt{Q^2} \le 16 \mathrm{GeV}/c$. The Gaussian filter is found to perform as well as, and in some instances better than, the conventional algorithms.

We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in the energy-momentum four-vectors of the final state particles. We demonstrate the proof-of-concept of our White Box AI approach using a Generative Adversarial Network (GAN) which learns from a DGLAP-based parton shower Monte Carlo event generator. We show, for the first time, that our approach leads to a network that is able to learn not only the final distribution of particles, but also the underlying parton branching mechanism, i.e. the Altarelli-Parisi splitting function, the ordering variable of the shower, and the scaling behavior. While the current work is focused on perturbative physics of the parton shower, we foresee a broad range of applications of our framework to areas that are currently difficult to address from first principles in QCD. Examples include nonperturbative and collective effects, factorization breaking and the modification of the parton shower in heavy-ion, and electron-nucleus collisions.

Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning techniques to identify important features that distinguish jets produced in heavy-ion collisions from jets produced in proton-proton collisions. We formulate the problem using binary classification and focus on leveraging machine learning in ways that inform theoretical calculations of jet modification: (i) we quantify the information content in terms of Infrared Collinear (IRC)-safety and in terms of hard vs. soft emissions, (ii) we identify optimally discriminating observables that are in principle calculable in perturbative QCD, and (iii) we assess the information loss due to the heavy-ion underlying event and background subtraction algorithms. We illustrate our methodology using Monte Carlo event generators, where we find that important information about jet quenching is contained not only in hard splittings but also in soft emissions and IRC-unsafe physics inside the jet. This information appears to be significantly reduced by the presence of the underlying event. We discuss the implications of this for the prospect of using jet quenching to extract properties of the QGP. Since the training labels are exactly known, this methodology can be used directly on experimental data without reliance on modeling. We outline a proposal for how such an experimental analysis can be carried out, and how it can guide future measurements.

We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in the energy-momentum four-vectors of the final state particles. We demonstrate the proof-of-concept of our White Box AI approach using a Generative Adversarial Network (GAN) which learns from a DGLAP-based parton shower Monte Carlo event generator. The constrained generator network architecture mimics the structure of a parton shower exhibiting similarities with Recurrent Neural Networks (RNNs). We show, for the first time, that our approach leads to a network that is able to learn not only the final distribution of particles, but also the underlying parton branching mechanism, i.e. the Altarelli-Parisi splitting function, the ordering variable of the shower, and the scaling behavior. While the current work is focused on perturbative physics of the parton shower, we foresee a broad range of applications of our framework to areas that are currently difficult to address from first principles in QCD. Examples include nonperturbative and collective effects, factorization breaking and the modification of the parton shower in heavy-ion, and electron-nucleus collisions.

The proposed electron-ion collider has a rich physics program to study the internal structure of protons and heavy nuclei. This program will impose strict requirements on detector design. This paper explores how these requirements can be satisfied using an all-silicon tracking detector, by consideration of three representative probes: heavy flavor hadrons, jets, and exclusive vector mesons.