Recommendations of the LHC Dark Matter Working Group: Comparing LHC searches for heavy mediators of dark matter production in visible and invisible decay channels

Andreas Albert, Mihailo Backovic, Antonio Boveia, Oliver Buchmueller, Giorgio Busoni, Albert De Roeck, Caterina Doglioni, Tristan DuPree, Malcolm Fairbairn, Marie-Helene Genest, Stefania Gori, Giuliano Gustavino, Kristian Hahn, Ulrich Haisch, Philip C. Harris, Dan Hayden, Valerio Ippolito, Isabelle John, Felix Kahlhoefer, Suchita Kulkarni, Greg Landsberg, Steven Lowette, Kentarou Mawatari, Antonio Riotto, William Shepherd, Tim M. P. Tait, Emma Tolley, Patrick Tunney, Bryan Zaldivar, Markus Zinser

Weakly-coupled TeV-scale particles may mediate the interactions between normal matter and dark matter. If so, the LHC would produce dark matter through these mediators, leading to the familiar "mono-X" search signatures, but the mediators would also produce signals without missing momentum via the same vertices involved in their production. This document from the LHC Dark Matter Working Group suggests how to compare searches for these two types of signals in case of vector and axial-vector mediators, based on a workshop that took place on September 19/20, 2016 and subsequent discussions. These suggestions include how to extend the spin-1 mediated simplified models already in widespread use to include lepton couplings. This document also provides analytic calculations of the relic density in the simplified models and reports an issue that arose when ATLAS and CMS first began to use preliminary numerical calculations of the dark matter relic density in these models.

GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders

Ryan Raikman, Eric A. Moreno, Ekaterina Govorkova, Ethan J Marx, Alec Gunny, William Benoit, Deep Chatterjee, Rafia Omer, Muhammed Saleem, Dylan S Rankin, Michael W Coughlin, Philip C Harris, Erik Katsavounidis

Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name Gravitational Wave Anomalous Knowledge (GWAK). While the semi-supervised nature of the problem comes with a cost in terms of accuracy as compared to supervised techniques, there is a qualitative advantage in generalizing experimental sensitivity beyond pre-computed signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.

Snowmass 2021 Dark Matter Complementarity Report

Antonio Boveia, Mohamed Berkat, Thomas Y. Chen, Aman Desai, Caterina Doglioni, Alex Drlica-Wagner, Susan Gardner, Stefania Gori, Joshua Greaves, Patrick Harding, Philip C. Harris, W. Hugh Lippincott, Maria Elena Monzani, Katherine Pachal, Chanda Prescod-Weinstein, Gray Rybka, Bibhushan Shakya, Jessie Shelton, Tracy R. Slatyer, Amanda Steinhebel, Philip Tanedo, Natalia Toro, Yun-Tse Tsai, Mike Williams, Lindley Winslow, Jaehoon Yu, Tien-Tien Yu

The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all Frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the Frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.

Strategy for Understanding the Higgs Physics: The Cool Copper Collider

Sridhara Dasu, Emilio A. Nanni, Michael E. Peskin, Caterina Vernieri, Tim Barklow, Rainer Bartoldus, Pushpalatha C. Bhat, Kevin Black, Jim Brau, Martin Breidenbach, Nathaniel Craig, Dmitri Denisov, Lindsey Gray, Philip C. Harris, Michael Kagan, Zhen Liu, Patrick Meade, Nathan Majernik, Sergei Nagaitsev, Isobel Ojalvo, Christoph Paus, Carl Schroeder, Ariel G. Schwartzman, Jan Strube, Su Dong, Sami Tantawi, LianTao Wang, Andy White, Graham W. Wilson

A program to build a lepton-collider Higgs factory, to precisely measure the couplings of the Higgs boson to other particles, followed by a higher energy run to establish the Higgs self-coupling and expand the new physics reach, is widely recognized as a primary focus of modern particle physics. We propose a strategy that focuses on a new technology and preliminary estimates suggest that can lead to a compact, affordable machine. New technology investigations will provide much needed enthusiasm for our field, resulting in trained workforce. This cost-effective, compact design, with technologies useful for a broad range of other accelerator applications, could be realized as a project in the US. Its technology innovations, both in the accelerator and the detector, will offer unique and exciting opportunities to young scientists. Moreover, cost effective compact designs, broadly applicable to other fields of research, are more likely to obtain financial support from our funding agencies.

Towards the next generation of simplified Dark Matter models

Andreas Albert, Martin Bauer, Oliver Buchmueller, Jim Brooke, David G. Cerdeno, Matthew Citron, Gavin Davies, Annapaola de Cosa, Albert De Roeck, Andrea De Simone, Tristan Du Pree, John Ellis, Henning Flaecher, Malcolm Fairbairn, Alexander Grohsjean, Kristian Hahn, Ulrich Haisch, Philip C. Harris, Valentin V. Khoze, Greg Landsberg, Christopher McCabe, Bjoern Penning, Veronica Sanz, Christian Schwanenberger, Pat Scott, Nicholas Wardle

This White Paper is an input to the ongoing discussion about the extension and refinement of simplified Dark Matter (DM) models. Based on two concrete examples, we show how existing simplified DM models (SDMM) can be extended to provide a more accurate and comprehensive framework to interpret and characterise collider searches. In the first example we extend the canonical SDMM with a scalar mediator to include mixing with the Higgs boson. We show that this approach not only provides a better description of the underlying kinematic properties that a complete model would possess, but also offers the option of using this more realistic class of scalar mixing models to compare and combine consistently searches based on different experimental signatures. The second example outlines how a new physics signal observed in a visible channel can be connected to DM by extending a simplified model including effective couplings. This discovery scenario uses the recently observed excess in the high-mass diphoton searches of ATLAS and CMS for a case study to show that such a pragmatic approach can aid the experimental search programme to verify/falsify a potential signal and to study its underlying nature. In the next part of the White Paper we outline other interesting options for SDMM that could be studied in more detail in the future. Finally, we discuss important aspects of supersymmetric models for DM and how these could help to develop of more complete SDMM.

Snowmass 2021 Cross Frontier Report: Dark Matter Complementarity (Extended Version)

Antonio Boveia, Mohamed Berkat, Thomas Y. Chen, Aman Desai, Caterina Doglioni, Alex Drlica-Wagner, Susan Gardner, Stefania Gori, Joshua Greaves, Patrick Harding, Philip C. Harris, W. Hugh Lippincott, Maria Elena Monzani, Katherine Pachal, Chanda Prescod-Weinstein, Gray Rybka, Bibhushan Shakya, Jessie Shelton, Tracy R. Slatyer, Amanda Steinhebel, Philip Tanedo, Natalia Toro, Yun-Tse Tsai, Mike Williams, Lindley Winslow, Jaehoon Yu, Tien-Tien Yu

The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.

Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings

Deep Chatterjee, Philip C. Harris, Maanas Goel, Malina Desai, Michael W. Coughlin, Erik Katsavounidis

Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference to make it even faster by marginalizing symmetries in a physical problem. In this approach, physical symmetries, for example, time-translation are learned using joint-embedding via self-supervised learning with symmetry data augmentations. Subsequently, parameter inference is performed using a normalizing flow where the embedding network is used to summarize the data before conditioning the parameters. We present this approach on two simple physical problems and we show faster convergence in a smaller number of parameters compared to a normalizing flow that does not use a pre-trained symmetry-informed representation.

Recommendations on presenting LHC searches for missing transverse energy signals using simplified $s$-channel models of dark matter

Antonio Boveia, Oliver Buchmueller, Giorgio Busoni, Francesco D'Eramo, Albert De Roeck, Andrea De Simone, Caterina Doglioni, Matthew J. Dolan, Marie-Helene Genest, Kristian Hahn, Ulrich Haisch, Philip C. Harris, Jan Heisig, Valerio Ippolito, Felix Kahlhoefer, Valentin V. Khoze, Suchita Kulkarni, Greg Landsberg, Steven Lowette, Sarah Malik, Michelangelo Mangano, Christopher McCabe, Stephen Mrenna, Priscilla Pani, Tristan du Pree, Antonio Riotto, David Salek, Kai Schmidt-Hoberg, William Shepherd, Tim M. P. Tait, Lian-Tao Wang, Steven Worm, Kathryn Zurek

This document summarises the proposal of the LHC Dark Matter Working Group on how to present LHC results on $s$-channel simplified dark matter models and to compare them to direct (indirect) detection experiments.

Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum

Daniel Abercrombie, Nural Akchurin, Ece Akilli, Juan Alcaraz Maestre, Brandon Allen, Barbara Alvarez Gonzalez, Jeremy Andrea, Alexandre Arbey, Georges Azuelos, Patrizia Azzi, Mihailo Backović, Yang Bai, Swagato Banerjee, James Beacham, Alexander Belyaev, Antonio Boveia, Amelia Jean Brennan, Oliver Buchmueller, Matthew R. Buckley, Giorgio Busoni, Michael Buttignol, Giacomo Cacciapaglia, Regina Caputo, Linda Carpenter, Nuno Filipe Castro, Guillelmo Gomez Ceballos, Yangyang Cheng, John Paul Chou, Arely Cortes Gonzalez, Chris Cowden, Francesco D'Eramo, Annapaola De Cosa, Michele De Gruttola, Albert De Roeck, Andrea De Simone, Aldo Deandrea, Zeynep Demiragli, Anthony DiFranzo, Caterina Doglioni, Tristan du Pree, Robin Erbacher, Johannes Erdmann, Cora Fischer, Henning Flaecher, Patrick J. Fox, Benjamin Fuks, Marie-Helene Genest, Bhawna Gomber, Andreas Goudelis, Johanna Gramling, John Gunion, Kristian Hahn, Ulrich Haisch, Roni Harnik, Philip C. Harris, Kerstin Hoepfner, Siew Yan Hoh, Dylan George Hsu, Shih-Chieh Hsu, Yutaro Iiyama, Valerio Ippolito, Thomas Jacques, Xiangyang Ju, Felix Kahlhoefer, Alexis Kalogeropoulos, Laser Seymour Kaplan, Lashkar Kashif, Valentin V. Khoze, Raman Khurana, Khristian Kotov, Dmytro Kovalskyi, Suchita Kulkarni, Shuichi Kunori, Viktor Kutzner, Hyun Min Lee, Sung-Won Lee, Seng Pei Liew, Tongyan Lin, Steven Lowette, Romain Madar, Sarah Malik, Fabio Maltoni, Mario Martinez Perez, Olivier Mattelaer, Kentarou Mawatari, Christopher McCabe, Théo Megy, Enrico Morgante, Stephen Mrenna, Siddharth M. Narayanan, Andy Nelson, Sérgio F. Novaes, Klaas Ole Padeken, Priscilla Pani, Michele Papucci, Manfred Paulini, Christoph Paus, Jacopo Pazzini, Björn Penning, Michael E. Peskin, Deborah Pinna, Massimiliano Procura, Shamona F. Qazi, Davide Racco, Emanuele Re, Antonio Riotto, Thomas G. Rizzo, Rainer Roehrig, David Salek, Arturo Sanchez Pineda, Subir Sarkar, Alexander Schmidt, Steven Randolph Schramm, William Shepherd, Gurpreet Singh, Livia Soffi, Norraphat Srimanobhas, Kevin Sung, Tim M. P. Tait, Timothee Theveneaux-Pelzer, Marc Thomas, Mia Tosi, Daniele Trocino, Sonaina Undleeb, Alessandro Vichi, Fuquan Wang, Lian-Tao Wang, Ren-Jie Wang, Nikola Whallon, Steven Worm, Mengqing Wu, Sau Lan Wu, Hongtao Yang, Yong Yang, Shin-Shan Yu, Bryan Zaldivar, Marco Zanetti, Zhiqing Zhang, Alberto Zucchetta