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1. Event Generators for High-Energy Physics Experiments

Author

Campbell, J. M., Diefenthaler, M., Hobbs, T. J., Höche, S., Isaacson, J., Kling, F., Mrenna, S., Reuter, J., Alioli, S., Andersen, J. R., Andreopoulos, C., Ankowski, A. M., Aschenauer, E. C., Ashkenazi, A., Baker, M. D., Barrow, J. L., van Beekveld, M., Bewick, G., Bhattacharya, S., Bierlich, C., Bothmann, E., Bredt, P., Broggio, A., Buckley, A., Butter, A., Butterworth, J. M., Byrne, E. P., Calame, C. M. Carloni, Chakraborty, S., Chen, X., Chiesa, M., Childers, J. T., Cruz-Martinez, J., Currie, J., Darvishi, N., Dasgupta, M., Denner, A., Dreyer, F. A., Dytman, S., El-Menoufi, B. K., Engel, T., Ravasio, S. Ferrario, Figueroa, D., Flower, L., Forshaw, J. R., Frederix, R., Friedland, A., Frixione, S., Gallagher, H., Gallmeister, K., Gardiner, S., Gauld, R., Gaunt, J., Gavardi, A., Gehrmann, T., Ridder, A. Gehrmann-De, Gellersen, L., Giele, W., Gieseke, S., Giuli, F., Glover, E. W. N., Grazzini, M., Grohsjean, A., Gütschow, C., Hamilton, K., Han, T., Hatcher, R., Heinrich, G., Helenius, I., Hen, O., Hirschi, V., Höfer, M., Holguin, J., Huss, A., Ilten, P., Jadach, S., Jentsch, A., Jones, S. P., Ju, W., Kallweit, S., Karlberg, A., Katori, T., Kerner, M., Kilian, W., Kirchgaeßer, M. M., Klein, S., Knobbe, M., Krause, C., Krauss, F., Lang, J., Lang, J. -N., Lee, G., Li, S. W., Lim, M. A., Lindert, J. M., Lombardi, D., Lönnblad, L., Löschner, M., Lurkin, N., Ma, Y., Machado, P., Magerya, V., Maier, A., Majer, I., Maltoni, F., Marcoli, M., Marinelli, G., Masouminia, M. R., Mastrolia, P., Mattelaer, O., Mazzitelli, J., McFayden, J., Medves, R., Meinzinger, P., Mo, J., Monni, P. F., Montagna, G., Morgan, T., Mosel, U., Nachman, B., Nadolsky, P., Nagar, R., Nagy, Z., Napoletano, D., Nason, P., Neumann, T., Nevay, L. J., Nicrosini, O., Niehues, J., Niewczas, K., Ohl, T., Ossola, G., Pandey, V., Papadopoulou, A., Papaefstathiou, A., Paz, G., Pellen, M., Pelliccioli, G., Peraro, T., Piccinini, F., Pickering, L., Pires, J., Płaczek, W., Plätzer, S., Plehn, T., Pozzorini, S., Prestel, S., Preuss, C. T., Price, A. C., Quackenbush, S., Re, E., Reichelt, D., Reina, L., Reuschle, C., Richardson, P., Rocco, M., Rocco, N., Roda, M., Garcia, A. Rodriguez, Roiser, S., Rojo, J., Rottoli, L., Salam, G. P., Schönherr, M., Schuchmann, S., Schumann, S., Schürmann, R., Scyboz, L., Seymour, M. H., Siegert, F., Signer, A., Chahal, G. Singh, Siódmok, A., Sjöstrand, T., Skands, P., Smillie, J. M., Sobczyk, J. T., Soldin, D., Soper, D. E., Soto-Ontoso, A., Soyez, G., Stagnitto, G., Tena-Vidal, J., Tomalak, O., Tramontano, F., Trojanowski, S., Tu, Z., Uccirati, S., Ullrich, T., Ulrich, Y., Utheim, M., Valassi, A., Verbytskyi, A., Verheyen, R., Wagman, M., Walker, D., Webber, B. R., Weinstein, L., White, O., Whitehead, J., Wiesemann, M., Wilkinson, C., Williams, C., Winterhalder, R., Wret, C., Xie, K., Yang, T-Z., Yazgan, E., Zanderighi, G., Zanoli, S., and Zapp, K.

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments., Comment: 164 pages, 10 figures, contribution to Snowmass 2021

Published
2022

2. The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

Author

Aarrestad, T., van Beekveld, M., Bona, M., Boveia, A., Caron, S., Davies, J., De Simone, A., Doglioni, C., Duarte, J. M., Farbin, A., Gupta, H., Hendriks, L., Heinrich, L., Howarth, J., Jawahar, P., Jueid, A., Lastow, J., Leinweber, A., Mamuzic, J., Merényi, E., Morandini, A., Moskvitina, P., Nellist, C., Ngadiuba, J., Ostdiek, B., Pierini, M., Ravina, B., de Austri, R. Ruiz, Sekmen, S., Touranakou, M., Vaškevičiūte, M., Vilalta, R., Vlimant, J. R., Verheyen, R., White, M., Wulff, E., Wallin, E., Wozniak, K. A., and Zhang, Z.

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability, Statistics - Machine Learning
Abstract

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge., Comment: v1: 54 pages, 24 figures. v2: 56 pages, citations added, extend discussion of look-elsewhere-effect, results unchanged; v3. minor typos and updated references

Published
2021
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3. Les Houches 2019 Physics at TeV Colliders: New Physics Working Group Report

Author

Brooijmans, G., Buckley, A., Caron, S., Falkowski, A., Fuks, B., Gilbert, A., Murray, W. J., Nardecchia, M., No, J. M., Torre, R., You, T., Della Porta, G. Zevi, Alguero, G., Araz, J. Y., Banerjee, S., Bélanger, G., Berger-Hryn'ova, T., Bernigaud, J., Bharucha, A., Buttazzo, D., Butterworth, J. M., Cacciapaglia, G., Coccaro, A., Corpe, L., Desai, N., De Simone, A., Dutta, J., Farbin, A., Fichet, S., Flacke, T., Ganguly, S., Gascon-Shotkin, S., Gonzalo, T., Goudelis, A., Gras, P., Gupta, R. S., Habedank, M., Heisig, J., Hendriks, L., Jain, S., Jueid, A., Kar, D., Kraml, S., Kvellestad, A., Lane, K. D., Langford, J., Leinweber, A., Lesauvage, A., Lessa, A., Mamuzic, J., Marzocca, D., Merényi, E., Mjallal, A., Mohan, K. A., Morandini, A., Moreau, G., Mutzel, S., Nellist, C., Otten, S., Pani, P., Panizzi, L., Pierini, M., Polesello, G., Porod, W., Prosper, H. B., Pukhov, A., Quevillon, J., Raklev, A., Reyes-González, H., Ruiz, R., de Austri, R. Ruiz, Schouwenberg, J., Sekmen, S., Sengupta, D., Smith, C., Son, M., Sperka, D., Stienen, B., Tobioka, K., van Beekveld, M., Venturini, E., Vignaroli, N., Vilalta, R., Waltenberger, W., Wardle, N., Watt, G., White, M., Williamson, S. L., Yallup, D., and Zurita, J.

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract

This report presents the activities of the `New Physics' working group for the `Physics at TeV Colliders' workshop (Les Houches, France, 10--28 June, 2019). These activities include studies of direct searches for new physics, approaches to exploit published data to constrain new physics, as well as the development of tools to further facilitate these investigations. Benefits of machine learning for both the search for new physics and the interpretation of these searches are also presented., Comment: Proceedings of the BSM Session of the Les Houches 2019 workshop, 227 pages

Published
2020

4. Event generators for high-energy physics experiments

Author

Campbell, J, Diefenthaler, M, Hobbs, T, Hoche, S, Isaacson, J, Kling, F, Mrenna, S, Reuter, J, Alioli, S, Andersen, J, Andreopoulos, C, Ankowski, A, Aschenauer, E, Ashkenazi, A, Baker, M, Barrow, J, van Beekveld, M, Bewick, G, Bhattacharya, S, Bierlich, C, Bothmann, E, Bredt, P, Broggio, A, Buckley, A, Butter, A, Butterworth, J, Byrne, E, Calame, C, Chakraborty, S, Chen, X, Chiesa, M, Childers, J, Cruz-Martinez, J, Currie, J, Darvishi, N, Dasgupta, M, Denner, A, Dreyer, F, Dytman, S, El-Menoufi, B, Engel, T, Ravasio, S, Figueroa, D, Flower, L, Forshaw, J, Frederix, R, Friedland, A, Frixione, S, Gallagher, H, Gallmeister, K, Gardiner, S, Gauld, R, Gaunt, J, Gavardi, A, Gehrmann, T, De Ridder, A, Gellersen, L, Giele, W, Gieseke, S, Giuli, F, Glover, E, Grazzini, M, Grohsjean, A, Gutschow, C, Hamilton, K, Han, T, Hatcher, R, Heinrich, G, Helenius, I, Hen, O, Hirschi, V, Hofer, M, Holguin, J, Huss, A, Ilten, P, Jadach, S, Jentsch, A, Jones, S, Ju, W, Kallweit, S, Karlberg, A, Katori, T, Kerner, M, Kilian, W, Kirchgaesser, M, Klein, S, Knobbe, M, Krause, C, Krauss, F, Lang, J, Lee, G, Li, S, Lim, M, Lindert, J, Lombardi, D, Lonnblad, L, Loschner, M, Lurkin, N, Ma, Y, Machado, P, Magerya, V, Maier, A, Majer, I, Maltoni, F, Marcoli, M, Marinelli, G, Masouminia, M, Mastrolia, P, Mattelaer, O, Mazzitelli, J, Mcfayden, J, Medves, R, Meinzinger, P, Mo, J, Monni, P, Montagna, G, Morgan, T, Mosel, U, Nachman, B, Nadolsky, P, Nagar, R, Nagy, Z, Napoletano, D, Nason, P, Neumann, T, Nevay, L, Nicrosini, O, Niehues, J, Niewczas, K, Ohl, T, Ossola, G, Pandey, V, Papadopoulou, A, Papaefstathiou, A, Paz, G, Pellen, M, Pelliccioli, G, Peraro, T, Piccinini, F, Pickering, L, Pires, J, Placzek, W, Platzer, S, Plehn, T, Pozzorini, S, Prestel, S, Preuss, C, Price, A, Quackenbush, S, Re, E, Reichelt, D, Reina, L, Reuschle, C, Richardson, P, Rocco, M, Rocco, N, Roda, M, Garcia, A, Roiser, S, Rojo, J, Rottoli, L, Salam, G, Schonherr, M, Schuchmann, S, Schumann, S, Schurmann, R, Scyboz, L, Seymour, M, Siegert, F, Signer, A, Chahal, G, Siodmok, A, Sjostrand, T, Skands, P, Smillie, J, Sobczyk, J, Soldin, D, Soper, D, Soto-Ontoso, A, Soyez, G, Stagnitto, G, Tena-Vidal, J, Tomalak, O, Tramontano, F, Trojanowski, S, Tu, Z, Uccirati, S, Ullrich, T, Ulrich, Y, Utheim, M, Valassi, A, Verbytskyi, A, Verheyen, R, Wagman, M, Walker, D, Webber, B, Weinstein, L, White, O, Whitehead, J, Wiesemann, M, Wilkinson, C, Williams, C, Winterhalder, R, Wret, C, Xie, K, Yang, T, Yazgan, E, Zanderighi, G, Zanoli, S, Zapp, K, Campbell J. M., Diefenthaler M., Hobbs T. J., Hoche S., Isaacson J., Kling F., Mrenna S., Reuter J., Alioli S., Andersen J. R., Andreopoulos C., Ankowski A. M., Aschenauer E. C., Ashkenazi A., Baker M. D., Barrow J. L., van Beekveld M., Bewick G., Bhattacharya S., Bierlich C., Bothmann E., Bredt P., Broggio A., Buckley A., Butter A., Butterworth J. M., Byrne E. P., Calame C. M. C., Chakraborty S., Chen X., Chiesa M., Childers J. T., Cruz-Martinez J., Currie J., Darvishi N., Dasgupta M., Denner A., Dreyer F. A., Dytman S., El-Menoufi B. K., Engel T., Ravasio S. F., Figueroa D., Flower L., Forshaw J. R., Frederix R., Friedland A., Frixione S., Gallagher H., Gallmeister K., Gardiner S., Gauld R., Gaunt J., Gavardi A., Gehrmann T., De Ridder A. G., Gellersen L., Giele W., Gieseke S., Giuli F., Glover E. W. N., Grazzini M., Grohsjean A., Gutschow C., Hamilton K., Han T., Hatcher R., Heinrich G., Helenius I., Hen O., Hirschi V., Hofer M., Holguin J., Huss A., Ilten P., Jadach S., Jentsch A., Jones S. P., Ju W., Kallweit S., Karlberg A., Katori T., Kerner M., Kilian W., Kirchgaesser M. M., Klein S., Knobbe M., Krause C., Krauss F., Lang J., Lang J. -N., Lee G., Li S. W., Lim M. A., Lindert J. M., Lombardi D., Lonnblad L., Loschner M., Lurkin N., Ma Y., Machado P., Magerya V., Maier A., Majer I., Maltoni F., Marcoli M., Marinelli G., Masouminia M. R., Mastrolia P., Mattelaer O., Mazzitelli J., McFayden J., Medves R., Meinzinger P., Mo J., Monni P. F., Montagna G., Morgan T., Mosel U., Nachman B., Nadolsky P., Nagar R., Nagy Z., Napoletano D., Nason P., Neumann T., Nevay L. J., Nicrosini O., Niehues J., Niewczas K., Ohl T., Ossola G., Pandey V., Papadopoulou A., Papaefstathiou A., Paz G., Pellen M., Pelliccioli G., Peraro T., Piccinini F., Pickering L., Pires J., Placzek W., Platzer S., Plehn T., Pozzorini S., Prestel S., Preuss C. T., Price A. C., Quackenbush S., Re E., Reichelt D., Reina L., Reuschle C., Richardson P., Rocco M., Rocco N., Roda M., Garcia A. R., Roiser S., Rojo J., Rottoli L., Salam G. P., Schonherr M., Schuchmann S., Schumann S., Schurmann R., Scyboz L., Seymour M. H., Siegert F., Signer A., Chahal G. S., Siodmok A., Sjostrand T., Skands P., Smillie J. M., Sobczyk J. T., Soldin D., Soper D. E., Soto-Ontoso A., Soyez G., Stagnitto G., Tena-Vidal J., Tomalak O., Tramontano F., Trojanowski S., Tu Z., Uccirati S., Ullrich T., Ulrich Y., Utheim M., Valassi A., Verbytskyi A., Verheyen R., Wagman M., Walker D., Webber B. R., Weinstein L., White O., Whitehead J., Wiesemann M., Wilkinson C., Williams C., Winterhalder R., Wret C., Xie K., Yang T. -Z., Yazgan E., Zanderighi G., Zanoli S., Zapp K., Campbell, J, Diefenthaler, M, Hobbs, T, Hoche, S, Isaacson, J, Kling, F, Mrenna, S, Reuter, J, Alioli, S, Andersen, J, Andreopoulos, C, Ankowski, A, Aschenauer, E, Ashkenazi, A, Baker, M, Barrow, J, van Beekveld, M, Bewick, G, Bhattacharya, S, Bierlich, C, Bothmann, E, Bredt, P, Broggio, A, Buckley, A, Butter, A, Butterworth, J, Byrne, E, Calame, C, Chakraborty, S, Chen, X, Chiesa, M, Childers, J, Cruz-Martinez, J, Currie, J, Darvishi, N, Dasgupta, M, Denner, A, Dreyer, F, Dytman, S, El-Menoufi, B, Engel, T, Ravasio, S, Figueroa, D, Flower, L, Forshaw, J, Frederix, R, Friedland, A, Frixione, S, Gallagher, H, Gallmeister, K, Gardiner, S, Gauld, R, Gaunt, J, Gavardi, A, Gehrmann, T, De Ridder, A, Gellersen, L, Giele, W, Gieseke, S, Giuli, F, Glover, E, Grazzini, M, Grohsjean, A, Gutschow, C, Hamilton, K, Han, T, Hatcher, R, Heinrich, G, Helenius, I, Hen, O, Hirschi, V, Hofer, M, Holguin, J, Huss, A, Ilten, P, Jadach, S, Jentsch, A, Jones, S, Ju, W, Kallweit, S, Karlberg, A, Katori, T, Kerner, M, Kilian, W, Kirchgaesser, M, Klein, S, Knobbe, M, Krause, C, Krauss, F, Lang, J, Lee, G, Li, S, Lim, M, Lindert, J, Lombardi, D, Lonnblad, L, Loschner, M, Lurkin, N, Ma, Y, Machado, P, Magerya, V, Maier, A, Majer, I, Maltoni, F, Marcoli, M, Marinelli, G, Masouminia, M, Mastrolia, P, Mattelaer, O, Mazzitelli, J, Mcfayden, J, Medves, R, Meinzinger, P, Mo, J, Monni, P, Montagna, G, Morgan, T, Mosel, U, Nachman, B, Nadolsky, P, Nagar, R, Nagy, Z, Napoletano, D, Nason, P, Neumann, T, Nevay, L, Nicrosini, O, Niehues, J, Niewczas, K, Ohl, T, Ossola, G, Pandey, V, Papadopoulou, A, Papaefstathiou, A, Paz, G, Pellen, M, Pelliccioli, G, Peraro, T, Piccinini, F, Pickering, L, Pires, J, Placzek, W, Platzer, S, Plehn, T, Pozzorini, S, Prestel, S, Preuss, C, Price, A, Quackenbush, S, Re, E, Reichelt, D, Reina, L, Reuschle, C, Richardson, P, Rocco, M, Rocco, N, Roda, M, Garcia, A, Roiser, S, Rojo, J, Rottoli, L, Salam, G, Schonherr, M, Schuchmann, S, Schumann, S, Schurmann, R, Scyboz, L, Seymour, M, Siegert, F, Signer, A, Chahal, G, Siodmok, A, Sjostrand, T, Skands, P, Smillie, J, Sobczyk, J, Soldin, D, Soper, D, Soto-Ontoso, A, Soyez, G, Stagnitto, G, Tena-Vidal, J, Tomalak, O, Tramontano, F, Trojanowski, S, Tu, Z, Uccirati, S, Ullrich, T, Ulrich, Y, Utheim, M, Valassi, A, Verbytskyi, A, Verheyen, R, Wagman, M, Walker, D, Webber, B, Weinstein, L, White, O, Whitehead, J, Wiesemann, M, Wilkinson, C, Williams, C, Winterhalder, R, Wret, C, Xie, K, Yang, T, Yazgan, E, Zanderighi, G, Zanoli, S, Zapp, K, Campbell J. M., Diefenthaler M., Hobbs T. J., Hoche S., Isaacson J., Kling F., Mrenna S., Reuter J., Alioli S., Andersen J. R., Andreopoulos C., Ankowski A. M., Aschenauer E. C., Ashkenazi A., Baker M. D., Barrow J. L., van Beekveld M., Bewick G., Bhattacharya S., Bierlich C., Bothmann E., Bredt P., Broggio A., Buckley A., Butter A., Butterworth J. M., Byrne E. P., Calame C. M. C., Chakraborty S., Chen X., Chiesa M., Childers J. T., Cruz-Martinez J., Currie J., Darvishi N., Dasgupta M., Denner A., Dreyer F. A., Dytman S., El-Menoufi B. K., Engel T., Ravasio S. F., Figueroa D., Flower L., Forshaw J. R., Frederix R., Friedland A., Frixione S., Gallagher H., Gallmeister K., Gardiner S., Gauld R., Gaunt J., Gavardi A., Gehrmann T., De Ridder A. G., Gellersen L., Giele W., Gieseke S., Giuli F., Glover E. W. N., Grazzini M., Grohsjean A., Gutschow C., Hamilton K., Han T., Hatcher R., Heinrich G., Helenius I., Hen O., Hirschi V., Hofer M., Holguin J., Huss A., Ilten P., Jadach S., Jentsch A., Jones S. P., Ju W., Kallweit S., Karlberg A., Katori T., Kerner M., Kilian W., Kirchgaesser M. M., Klein S., Knobbe M., Krause C., Krauss F., Lang J., Lang J. -N., Lee G., Li S. W., Lim M. A., Lindert J. M., Lombardi D., Lonnblad L., Loschner M., Lurkin N., Ma Y., Machado P., Magerya V., Maier A., Majer I., Maltoni F., Marcoli M., Marinelli G., Masouminia M. R., Mastrolia P., Mattelaer O., Mazzitelli J., McFayden J., Medves R., Meinzinger P., Mo J., Monni P. F., Montagna G., Morgan T., Mosel U., Nachman B., Nadolsky P., Nagar R., Nagy Z., Napoletano D., Nason P., Neumann T., Nevay L. J., Nicrosini O., Niehues J., Niewczas K., Ohl T., Ossola G., Pandey V., Papadopoulou A., Papaefstathiou A., Paz G., Pellen M., Pelliccioli G., Peraro T., Piccinini F., Pickering L., Pires J., Placzek W., Platzer S., Plehn T., Pozzorini S., Prestel S., Preuss C. T., Price A. C., Quackenbush S., Re E., Reichelt D., Reina L., Reuschle C., Richardson P., Rocco M., Rocco N., Roda M., Garcia A. R., Roiser S., Rojo J., Rottoli L., Salam G. P., Schonherr M., Schuchmann S., Schumann S., Schurmann R., Scyboz L., Seymour M. H., Siegert F., Signer A., Chahal G. S., Siodmok A., Sjostrand T., Skands P., Smillie J. M., Sobczyk J. T., Soldin D., Soper D. E., Soto-Ontoso A., Soyez G., Stagnitto G., Tena-Vidal J., Tomalak O., Tramontano F., Trojanowski S., Tu Z., Uccirati S., Ullrich T., Ulrich Y., Utheim M., Valassi A., Verbytskyi A., Verheyen R., Wagman M., Walker D., Webber B. R., Weinstein L., White O., Whitehead J., Wiesemann M., Wilkinson C., Williams C., Winterhalder R., Wret C., Xie K., Yang T. -Z., Yazgan E., Zanderighi G., Zanoli S., and Zapp K.

Abstract

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

Published
2024

5. Next-to-leading-logarithmic PanScales showers for deep inelastic scattering and vector boson fusion

Author

van Beekveld, M, Ferrario Ravasio, S, van Beekveld, M, and Ferrario Ravasio, S

Abstract

We introduce the first family of parton showers that achieve next-to-leading logarithmic (NLL) accuracy for processes involving a t-channel exchange of a colour-singlet, and embed them in the PanScales framework. These showers are applicable to processes such as deep inelastic scattering (DIS), vector boson fusion (VBF), and vector boson scattering (VBS). We extensively test and verify the NLL accuracy of the new showers at both fixed order and all orders across a wide range of observables. We also introduce a generalisation of the Cambridge-Aachen jet algorithm and formulate new DIS observables that exhibit a simple resummation structure. The NLL showers are compared to a standard transverse-momentum ordered dipole shower, serving as a proxy for the current state-of-the-art leading-logarithmic showers available in public codes. Depending on the observable, we find discrepancies at NLL of the order of 15%. We also present some exploratory phenomenological results for Higgs production in VBF. This work enables, for the first time, to resum simultaneously global and non-global observables for the VBF process at NLL accuracy.

Published
2024

6. PanScales parton showers for hadron collisions: formulation and fixed-order studies

Author

van Beekveld, M, Ferrario Ravasio, S, Salam, G, Soto-Ontoso, A, Soyez, G, Verheyen, R, Salam, GP, van Beekveld, M, Ferrario Ravasio, S, Salam, G, Soto-Ontoso, A, Soyez, G, Verheyen, R, and Salam, GP

Abstract

We formulate PanScales parton showers for hadron collisions so as to achieve next-to-leading logarithmic (NLL) accuracy across a broad set of observables. We do so specifically for colour singlet production. Relative to the existing PanScales final-state showers, the main new question is that of how to redistribute momentum imbalances from initial-state branching across the remainder of the event. We present tests of the showers at fixed order, including the treatment of full colour for soft-collinear emissions and of spin correlations in both the soft and collinear domains. We also include comparisons to a formulation of a standard dipole shower, the current leading-logarithmic state of the art. A forthcoming companion paper [1] will explore all-order tests of the new showers.

Published
2022

7. Benchmark data and model independent event classification for the large hadron collider

Author

Aarrestad, T. van Beekveld, M. Bona, M. Boveia, A. Caron, S. Davies, J. De Simone, A. Doglioni, C. Duarte, J.M. Farbin, A. Gupta, H. Hendriks, L. Heinrich, L. Howarth, J. Jawahar, P. Jueid, A. Lastow, J. Leinweber, A. Mamuzic, J. Merényi, E. Morandini, A. Moskvitina, P. Nellist, C. Ngadiuba, J. Ostdiek, B. Pierini, M. Ravina, B. de Austri, R.R. Sekmen, S. Touranakou, M. Vaškevičiūte, M. Vilalta, R. Vlimant, J.-R. Verheyen, R. White, M. Wulff, E. Wallin, E. Wozniak, K.A. Zhang, Z.

Abstract

We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge. Copyright © T. Aarrestad et al.

Published
2022

8. A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications

Author

Balázs, C., van Beekveld, M., Caron, S., Dillon, B. M., Farmer, B., Fowlie, A., Garrido-Merchán, E. C., Handley, W., Hendriks, L., Jóhannesson, Gudlaugur, Leinweber, A., Mamužić, J., Martinez, G. D., Otten, S., de Austri, R. R., Scott, P., Searle, Z., Stienen, B., Vanschoren, J., White, M., Group, The DarkMachines High Dimensional Sampling, Balázs, C., van Beekveld, M., Caron, S., Dillon, B. M., Farmer, B., Fowlie, A., Garrido-Merchán, E. C., Handley, W., Hendriks, L., Jóhannesson, Gudlaugur, Leinweber, A., Mamužić, J., Martinez, G. D., Otten, S., de Austri, R. R., Scott, P., Searle, Z., Stienen, B., Vanschoren, J., White, M., and Group, The DarkMachines High Dimensional Sampling

Abstract

Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms., QC 20220323

Published
2021
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9. The current status of fine-tuning in supersymmetry

Author

European Commission, Ministerio de Economía y Competitividad (España), Ministerio de Ciencia e Innovación (España), van Beekveld, M., Caron, S., de Ruiz de Austri, Roberto, European Commission, Ministerio de Economía y Competitividad (España), Ministerio de Ciencia e Innovación (España), van Beekveld, M., Caron, S., and de Ruiz de Austri, Roberto

Abstract

In this paper, we minimize and compare two different fine-tuning measures in four high-scale supersymmetric models that are embedded in the MSSM. In addition, we determine the impact of current and future dark matter direct detection and collider experiments on the fine-tuning. We then compare the low-scale electroweak measure with the high-scale Barbieri-Giudice measure. We find that they reduce to the same value when the higgsino parameter drives the degree of fine-tuning. We also find spectra where the high-scale measure turns out to be lower than the low-scale measure. Depending on the high-scale model and fine-tuning definition, we find a minimal fine-tuning of 3–38 (corresponding to O(10–1)%) for the low-scale measure, and 63–571 (corresponding to O(1–0.1)%) for the high-scale measure. We stress that it is too early to conclude on the fate of supersymmetry, based only on the fine-tuning paradigm.

Published
2020

10. Les Houches 2019 Physics at TeV Colliders: New Physics Working Group Report

Author

Brooijmans, G., Buckley, A., Caron, S., Falkowski, A., Fuks, B., Gilbert, A., Murray, W. J., Nardecchia, M., No, J. M., Torre, R., You, T., Zevi Della Porta, G., Alguero, G., Araz, J. Y., Banerjee, S., Bélanger, G., Berger-Hryn'ova, T., Bernigaud, J., Bharucha, A., Buttazzo, D., Butterworth, J. M., Cacciapaglia, G., Coccaro, A., Corpe, L., Desai, N., De Simone, A., Dutta, J., Farbin, A., Fichet, S., Flacke, T., Ganguly, S., Gascon-Shotkin, S., Gonzalo, T., Goudelis, A., Gras, P., Gupta, R. S., Habedank, M., Heisig, J., Hendriks, L., Jain, S., Jueid, A., Kar, D., Kraml, S., Kvellestad, A., Lane, K. D., Langford, J., Leinweber, A., Lesauvage, A., Lessa, A., Mamuzic, J., Marzocca, D., Merényi, E., Mjallal, A., Mohan, K. A., Morandini, A., Moreau, G., Mutzel, S., Nellist, C., Otten, S., Pani, P., Panizzi, L., Pierini, M., Polesello, G., Porod, W., Prosper, H. B., Pukhov, A., Quevillon, J., Raklev, A., Reyes-González, H., Ruiz, R., Ruiz de Austri, R., Schouwenberg, J., Sekmen, S., Sengupta, D., Smith, C., Son, M., Sperka, D., Stienen, B., Tobioka, K., van Beekveld, M., Venturini, E., Vignaroli, N., Vilalta, R., Waltenberger, W., Wardle, N., Watt, G., White, M., Williamson, S. L., Yallup, D., Zurita, J., Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Laboratoire de Physique Théorique et Hautes Energies (LPTHE), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire d'Annecy-le-Vieux de Physique Théorique (LAPTH), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique Nucléaire de Lyon (IPNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Laboratoire d'Annecy de Physique des Particules (LAPP/Laboratoire d'Annecy-le-Vieux de Physique des Particules), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Clermont Auvergne (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)

Subjects
data analysis method, hep-ex, new physics: search for, FOS: Physical sciences, hep-ph, talk: Les Houches 2019/06/10, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), High Energy Physics - Phenomenology, High Energy Physics - Phenomenology (hep-ph), statistical analysis, [PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph], [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], Particle Physics - Experiment, Particle Physics - Phenomenology
Abstract

This report presents the activities of the `New Physics' working group for the `Physics at TeV Colliders' workshop (Les Houches, France, 10--28 June, 2019). These activities include studies of direct searches for new physics, approaches to exploit published data to constrain new physics, as well as the development of tools to further facilitate these investigations. Benefits of machine learning for both the search for new physics and the interpretation of these searches are also presented., Proceedings of the BSM Session of the Les Houches 2019 workshop, 227 pages

11. Threshold Resummation for the Production of Four Top Quarks at the LHC.

Author

van Beekveld M, Kulesza A, and Moreno Valero L

Abstract

We compute the total cross section for tt[over ¯]tt[over ¯] production at next-to-leading logarithmic (NLL^{'}) accuracy. This is the first time resummation is performed for a hadron-collider process with four colored particles in the final state. The calculation is matched to the next-to-leading order strong and electroweak corrections. The NLL^{'} corrections enhance the total production rate by 15%. The size of the theoretical error due to scale variation is reduced by more than a factor of 2, bringing the theoretical error significantly below the current experimental uncertainty of the measurement.

Published
2023
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12. Event generation and statistical sampling for physics with deep generative models and a density information buffer.

Author

Otten S, Caron S, de Swart W, van Beekveld M, Hendriks L, van Leeuwen C, Podareanu D, Ruiz de Austri R, and Verheyen R

Abstract

Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e + e -  → Z → l + l - and [Formula: see text] including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories.

Published
2021
Full Text
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