Celestial Objects as Dark Matter Colliders

In the vicinity of the Milky Way Galactic Center, celestial bodies, including neutron stars, reside within a dense dark matter environment. This study explores the accumulation of dark matter by neutron stars through dark matter-nucleon interactions, leading to increased internal dark matter density. Consequently, dark matter annihilation produces long-lived mediators that escape and decay into neutrinos. Leveraging experimental limits from IceCube, ANTARES, and future projections from ARIA, we establish constraints on the dark matter-nucleon cross section within a simplified dark $U(1)_{X}$ mediator model. This approach, applicable to various celestial objects and dark matter models, offers insights into the intricate interplay between dark matter and neutron stars near the Galactic Center.Comment: 3 pages, 2 figures, Proceedings for the "Window on the Universe" conference celebrating the 30th anniversary of "Rencontres de Vietnam", August 2023, Quy Nhon, Vietna

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.Comment: 29 pages, 12 figures, 5 table

Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re-discovering the top quark by identifying the main features of the t-tbar experimental signature at the LHC.Comment: 16 pages, 9 figure

Leptoquark search at the Forward Physics Facility

In this study, we calculate the sensitivity reach on the vector leptoquark (LQ) $U_1$ at the experiments proposed in Forward Physics Facility (FPF), including FASER$\nu$, FASER$\nu2$, FLArE (10 tons), and FLArE (100 tons) using the neutrino-nucleon scattering ($\nu N \rightarrow \nu N'$ and $\nu N \rightarrow l N'$). We cover a wide mass range of $10^{-3}$ GeV $\leq M_{LQ}\leq 10^4$ GeV. The new result shows that the FLArE (100 tons) offers the best sensitivity to the LQ model. The sensitivity curves for all the experiments follow a similar pattern with weakened sensitivities with the increment of the LQ mass. We combine the sensitivities obtained from the neutral- and charged-current interactions of the neutrinos.Comment: 21 pages, 10 figures. Adding two subfigures on the TeV mass LQ mass regim

Indirect Searches for Dark Photon-Photon Tridents in Celestial Objects

We model and constrain the unique indirect detection signature produced by dark matter particles that annihilate through a $U(1)$ gauge symmetry into dark photons that subsequently decay into three-photon final states. We focus on scenarios where the dark photon is long-lived, and show that $\gamma$-ray probes of celestial objects can set strong constraints on the dark matter/baryon scattering cross section that in many cases surpass the power of current direct detection constraints, and in some cases even peer into the neutrino fog.Comment: 12 pages, 7 figures (8 sub-figures), 3 table

Natural Language Commanding via Program Synthesis

We present Semantic Interpreter, a natural language-friendly AI system for productivity software such as Microsoft Office that leverages large language models (LLMs) to execute user intent across application features. While LLMs are excellent at understanding user intent expressed as natural language, they are not sufficient for fulfilling application-specific user intent that requires more than text-to-text transformations. We therefore introduce the Office Domain Specific Language (ODSL), a concise, high-level language specialized for performing actions in and interacting with entities in Office applications. Semantic Interpreter leverages an Analysis-Retrieval prompt construction method with LLMs for program synthesis, translating natural language user utterances to ODSL programs that can be transpiled to application APIs and then executed. We focus our discussion primarily on a research exploration for Microsoft PowerPoint

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.Comment: 15 pages, 12 figure

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn’t make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC