Machine Learning Science and Technology最新文献

{"title":"Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network","authors":"G Aad, E Aakvaag, B Abbott, K Abeling, N J Abicht, S H Abidi, A Aboulhorma, H Abramowicz, H Abreu, Y Abulaiti, B S Acharya, C Adam Bourdarios, L Adamczyk, S V Addepalli, M J Addison, J Adelman, A Adiguzel, T Adye, A A Affolder, Y Afik, M N Agaras, J Agarwala, A Aggarwal, C Agheorghiesei, A Ahmad, F Ahmadov, W S Ahmed, S Ahuja, X Ai, G Aielli, A Aikot, M Ait Tamlihat, B Aitbenchikh, I Aizenberg, M Akbiyik, T P A Åkesson, A V Akimov, D Akiyama, N N Akolkar, S Aktas, K Al Khoury, G L Alberghi, J Albert, P Albicocco, G L Albouy, S Alderweireldt, Z L Alegria, M Aleksa, I N Aleksandrov, C Alexa, T Alexopoulos, F Alfonsi, M Algren, M Alhroob, B Ali, H M J Ali, S Ali, S W Alibocus, M Aliev, G Alimonti, W Alkakhi, C Allaire, B M M Allbrooke, J F Allen, C A Allendes Flores, P P Allport, A Aloisio, F Alonso, C Alpigiani, M Alvarez Estevez, A Alvarez Fernandez, M Alves Cardoso, M G Alviggi, M Aly, Y Amaral Coutinho, A Ambler, C Amelung, M Amerl, C G Ames, D Amidei, S P Amor Dos Santos, K R Amos, V Ananiev, C Anastopoulos, T Andeen, J K Anders, S Y Andrean, A Andreazza, S Angelidakis, A Angerami, A V Anisenkov, A Annovi, C Antel, M T Anthony, E Antipov, M Antonelli, F Anulli, M Aoki, T Aoki, J A Aparisi Pozo, M A Aparo, L Aperio Bella, C Appelt, A Apyan, S J Arbiol Val, C Arcangeletti, A T H Arce, E Arena, J-F Arguin, S Argyropoulos, J-H Arling, O Arnaez, H Arnold, G Artoni, H Asada, K Asai, S Asai, N A Asbah, K Assamagan, R Astalos, S Atashi, R J Atkin, M Atkinson, H Atmani, P A Atmasiddha, K Augsten, S Auricchio, A D Auriol, V A Austrup, G Avolio, K Axiotis, G Azuelos, D Babal, H Bachacou, K Bachas, A Bachiu, F Backman, A Badea, T M Baer, P Bagnaia, M Bahmani, D Bahner, K Bai, A J Bailey, V R Bailey, J T Baines, L Baines, O K Baker, E Bakos, D Bakshi Gupta, V Balakrishnan, R Balasubramanian, E M Baldin, P Balek, E Ballabene, F Balli, L M Baltes, W K Balunas, J Balz, E Banas, M Bandieramonte, A Bandyopadhyay, S Bansal, L Barak, M Barakat, E L Barberio, D Barberis, M Barbero, M Z Barel, K N Barends, T Barillari, M-S Barisits, T Barklow, P Baron, D A Baron Moreno, A Baroncelli, G Barone, A J Barr, J D Barr, F Barreiro, J Barreiro Guimarães da Costa, U Barron, M G Barros Teixeira, S Barsov, F Bartels, R Bartoldus, A E Barton, P Bartos, A Basan, M Baselga, A Bassalat, M J Basso, C R Basson, R L Bates, S Batlamous, B Batool, M Battaglia, D Battulga, M Bauce, M Bauer, P Bauer, L T Bazzano Hurrell, J B Beacham, T Beau, J Y Beaucamp, P H Beauchemin, P Bechtle, H P Beck, K Becker, A J Beddall, V A Bednyakov, C P Bee, L J Beemster, T A Beermann, M Begalli, M Begel, A Behera, J K Behr, J F Beirer, F Beisiegel, M Belfkir, G Bella, L Bellagamba, A Bellerive, P Bellos, K Beloborodov, D Benchekroun, F Bendebba, Y Benhammou, K C Benkendorfer, L Beresford, M Beretta, E Bergeaas Kuutmann, N Berger, B Bergmann, J Beringer, G Bernardi, C Bernius, F U Bernlochner, F Bernon, A Berrocal Guardia, T Berry, P Berta, A Berthold, S Bethke, A Betti, A J Bevan, N K Bhalla, M Bhamjee, S Bhatta, D S Bhattacharya, P Bhattarai, K D Bhide, V S Bhopatkar, R M Bianchi, G Bianco, O Biebel, R Bielski, M Biglietti, C S Billingsley, M Bindi, A Bingul, C Bini, A Biondini, C J Birch-sykes, G A Bird, M Birman, M Biros, S Biryukov, T Bisanz, E Bisceglie, J P Biswal, D Biswas, K Bjørke, I Bloch, A Blue, U Blumenschein, J Blumenthal, V S Bobrovnikov, M Boehler, B Boehm, D Bogavac, A G Bogdanchikov, C Bohm, V Boisvert, P Bokan, T Bold, M Bomben, M Bona, M Boonekamp, C D Booth, A G Borbély, I S Bordulev, H M Borecka-Bielska, G Borissov, D Bortoletto, D Boscherini, M Bosman, J D Bossio Sola, K Bouaouda, N Bouchhar, J Boudreau, E V Bouhova-Thacker, D Boumediene, R Bouquet, A Boveia, J Boyd, D Boye, I R Boyko, J Bracinik, N Brahimi, G Brandt, O Brandt, F Braren, B Brau, J E Brau, R Brener, L Brenner, R Brenner, S Bressler, D Britton, D Britzger, I Brock, G Brooijmans, E Brost, L M Brown, L E Bruce, T L Bruckler, P A Bruckman de Renstrom, B Brüers, A Bruni, G Bruni, M Bruschi, N Bruscino, T Buanes, Q Buat, D Buchin, A G Buckley, O Bulekov, B A Bullard, S Burdin, C D Burgard, A M Burger, B Burghgrave, O Burlayenko, J T P Burr, C D Burton, J C Burzynski, E L Busch, V Büscher, P J Bussey, J M Butler, C M Buttar, J M Butterworth, W Buttinger, C J Buxo Vazquez, A R Buzykaev, S Cabrera Urbán, L Cadamuro, D Caforio, H Cai, Y Cai, Y Cai, V M M Cairo, O Cakir, N Calace, P Calafiura, G Calderini, P Calfayan, G Callea, L P Caloba, D Calvet, S Calvet, M Calvetti, R Camacho Toro, S Camarda, D Camarero Munoz, P Camarri, M T Camerlingo, D Cameron, C Camincher, M Campanelli, A Camplani, V Canale, A C Canbay, J Cantero, Y Cao, F Capocasa, M Capua, A Carbone, R Cardarelli, J C J Cardenas, F Cardillo, G Carducci, T Carli, G Carlino, J I Carlotto, B T Carlson, E M Carlson, L Carminati, A Carnelli, M Carnesale, S Caron, E Carquin, S Carrá, G Carratta, A M Carroll, T M Carter, M P Casado, M Caspar, F L Castillo, L Castillo Garcia, V Castillo Gimenez, N F Castro, A Catinaccio, J R Catmore, T Cavaliere, V Cavaliere, N Cavalli, Y C Cekmecelioglu, E Celebi, F Celli, M S Centonze, V Cepaitis, K Cerny, A S Cerqueira, A Cerri, L Cerrito, F Cerutti, B Cervato, A Cervelli, G Cesarini, S A Cetin, D Chakraborty, J Chan, W Y Chan, J D Chapman, E Chapon, B Chargeishvili, D G Charlton, M Chatterjee, C Chauhan, Y Che, S Chekanov, S V Chekulaev, G A Chelkov, A Chen, B Chen, B Chen, H Chen, H Chen, J Chen, J Chen, M Chen, S Chen, S J Chen, X Chen, X Chen, Y Chen, C L Cheng, H C Cheng, S Cheong, A Cheplakov, E Cheremushkina, E Cherepanova, R Cherkaoui El Moursli, E Cheu, K Cheung, L Chevalier, V Chiarella, G Chiarelli, N Chiedde, G Chiodini, A S Chisholm, A Chitan, M Chitishvili, M V Chizhov, K Choi, Y Chou, E Y S Chow, K L Chu, M C Chu, X Chu, J Chudoba, J J Chwastowski, D Cieri, K M Ciesla, V Cindro, A Ciocio, F Cirotto, Z H Citron, M Citterio, D A Ciubotaru, A Clark, P J Clark, C Clarry, J M Clavijo Columbie, S E Clawson, C Clement, J Clercx, Y Coadou, M Cobal, A Coccaro, R F Coelho Barrue, R Coelho Lopes De Sa, S Coelli, B Cole, J Collot, P Conde Muiño, M P Connell, S H Connell, E I Conroy, F Conventi, H G Cooke, A M Cooper-Sarkar, A Cordeiro Oudot Choi, L D Corpe, M Corradi, F Corriveau, A Cortes-Gonzalez, M J Costa, F Costanza, D Costanzo, B M Cote, G Cowan, K Cranmer, D Cremonini, S Crépé-Renaudin, F Crescioli, M Cristinziani, M Cristoforetti, V Croft, J E Crosby, G Crosetti, A Cueto, T Cuhadar Donszelmann, H Cui, Z Cui, W R Cunningham, F Curcio, P Czodrowski, M M Czurylo, M J Da Cunha Sargedas De Sousa, J V Da Fonseca Pinto, C Da Via, W Dabrowski, T Dado, S Dahbi, T Dai, D Dal Santo, C Dallapiccola, M Dam, G D’amen, V D’Amico, J Damp, J R Dandoy, M Danninger, V Dao, G Darbo, S Darmora, S J Das, S D’Auria, A D’avanzo, C David, T Davidek, B Davis-Purcell, I Dawson, H A Day-hall, K De, R De Asmundis, N De Biase, S De Castro, N De Groot, P de Jong, H De la Torre, A De Maria, A De Salvo, U De Sanctis, F De Santis, A De Santo, J B De Vivie De Regie, D V Dedovich, J Degens, A M Deiana, F Del Corso, J Del Peso, F Del Rio, L Delagrange, F Deliot, C M Delitzsch, M Della Pietra, D Della Volpe, A Dell’Acqua, L Dell’Asta, M Delmastro, P A Delsart, S Demers, M Demichev, S P Denisov, L D’Eramo, D Derendarz, F Derue, P Dervan, K Desch, C Deutsch, F A Di Bello, A Di Ciaccio, L Di Ciaccio, A Di Domenico, C Di Donato, A Di Girolamo, G Di Gregorio, A Di Luca, B Di Micco, R Di Nardo, M Diamantopoulou, F A Dias, T Dias Do Vale, M A Diaz, F G Diaz Capriles, M Didenko, E B Diehl, S Díez Cornell, C Diez Pardos, C Dimitriadi, A Dimitrievska, J Dingfelder, I-M Dinu, S J Dittmeier, F Dittus, F Djama, T Djobava, C Doglioni, A Dohnalova, J Dolejsi, Z Dolezal, K M Dona, M Donadelli, B Dong, J Donini, A D’Onofrio, M D’Onofrio, J Dopke, A Doria, N Dos Santos Fernandes, P Dougan, M T Dova, A T Doyle, M A Draguet, E Dreyer, I Drivas-koulouris, M Drnevich, M Drozdova, D Du, T A du Pree, F Dubinin, M Dubovsky, E Duchovni, G Duckeck, O A Ducu, D Duda, A Dudarev, E R Duden, M D’uffizi, L Duflot, M Dührssen, A E Dumitriu, M Dunford, S Dungs, K Dunne, A Duperrin, H Duran Yildiz, M Düren, A Durglishvili, B L Dwyer, G I Dyckes, M Dyndal, B S Dziedzic, Z O Earnshaw, G H Eberwein, B Eckerova, S Eggebrecht, E Egidio Purcino De Souza, L F Ehrke, G Eigen, K Einsweiler, T Ekelof, P A Ekman, S El Farkh, Y El Ghazali, H El Jarrari, A El Moussaouy, V Ellajosyula, M Ellert, F Ellinghaus, N Ellis, J Elmsheuser, M Elsing, D Emeliyanov, Y Enari, I Ene, S Epari, P A Erland, M Errenst, M Escalier, C Escobar, E Etzion, G Evans, H Evans, L S Evans, A Ezhilov, S Ezzarqtouni, F Fabbri, L Fabbri, G Facini, V Fadeyev, R M Fakhrutdinov, D Fakoudis, S Falciano, L F Falda Ulhoa Coelho, P J Falke, J Faltova, C Fan, Y Fan, Y Fang, M Fanti, M Faraj, Z Farazpay, A Farbin, A Farilla, T Farooque, S M Farrington, F Fassi, D Fassouliotis, M Faucci Giannelli, W J Fawcett, L Fayard, P Federic, P Federicova, O L Fedin, M Feickert, L Feligioni, D E Fellers, C Feng, M Feng, Z Feng, M J Fenton, L Ferencz, R A M Ferguson, S I Fernandez Luengo, P Fernandez Martinez, M J V Fernoux, J Ferrando, A Ferrari, P Ferrari, R Ferrari, D Ferrere, C Ferretti, F Fiedler, P Fiedler, A Filipčič, E K Filmer, F Filthaut, M C N Fiolhais, L Fiorini, W C Fisher, T Fitschen, P M Fitzhugh, I Fleck, P Fleischmann, T Flick, M Flores, L R Flores Castillo, L Flores Sanz De Acedo, F M Follega, N Fomin, J H Foo, A Formica, A C Forti, E Fortin, A W Fortman, M G Foti, L Fountas, D Fournier, H Fox, P Francavilla, S Francescato, S Franchellucci, M Franchini, S Franchino, D Francis, L Franco, V Franco Lima, L Franconi, M Franklin, G Frattari, W S Freund, Y Y Frid, J Friend, N Fritzsche, A Froch, D Froidevaux, J A Frost, Y Fu, S Fuenzalida Garrido, M Fujimoto, K Y Fung, E Furtado De Simas Filho, M Furukawa, J Fuster, A Gabrielli, A Gabrielli, P Gadow, G Gagliardi, L G Gagnon, S Galantzan, E J Gallas, B J Gallop, K K Gan, S Ganguly, Y Gao, F M Garay Walls, B Garcia, C García, A Garcia Alonso, A G Garcia Caffaro, J E García Navarro, M Garcia-Sciveres, G L Gardner, R W Gardner, N Garelli, D Garg, R B Garg, J M Gargan, C A Garner, C M Garvey, P Gaspar, V K Gassmann, G Gaudio, V Gautam, P Gauzzi, I L Gavrilenko, A Gavrilyuk, C Gay, G Gaycken, E N Gazis, A A Geanta, C M Gee, A Gekow, C Gemme, M H Genest, A D Gentry, S George, W F George, T Geralis, P Gessinger-Befurt, M E Geyik, M Ghani, M Ghneimat, K Ghorbanian, A Ghosal, A Ghosh, A Ghosh, B Giacobbe, S Giagu, T Giani, P Giannetti, A Giannini, S M Gibson, M Gignac, D T Gil, A K Gilbert, B J Gilbert, D Gillberg, G Gilles, L Ginabat, D M Gingrich, M P Giordani, P F Giraud, G Giugliarelli, D Giugni, F Giuli, I Gkialas, L K Gladilin, C Glasman, G R Gledhill, G Glemža, M Glisic, I Gnesi, Y Go, M Goblirsch-Kolb, B Gocke, D Godin, B Gokturk, S Goldfarb, T Golling, M G D Gololo, D Golubkov, J P Gombas, A Gomes, G Gomes Da Silva, A J Gomez Delegido, R Gonçalo, L Gonella, A Gongadze, F Gonnella, J L Gonski, R Y González Andana, S González de la Hoz, R Gonzalez Lopez, C Gonzalez Renteria, M V Gonzalez Rodrigues, R Gonzalez Suarez, S Gonzalez-Sevilla, G R Gonzalvo Rodriguez, L Goossens, B Gorini, E Gorini, A Gorišek, T C Gosart, A T Goshaw, M I Gostkin, S Goswami, C A Gottardo, S A Gotz, M Gouighri, V Goumarre, A G Goussiou, N Govender, I Grabowska-Bold, K Graham, E Gramstad, S Grancagnolo, C M Grant, P M Gravila, F G Gravili, H M Gray, M Greco, C Grefe, I M Gregor, P Grenier, S G Grewe, A A Grillo, K Grimm, S Grinstein, J -F Grivaz, E Gross, J Grosse-Knetter, J C Grundy, L Guan, C Gubbels, J G R Guerrero Rojas, G Guerrieri, F Guescini, R Gugel, J A M Guhit, A Guida, E Guilloton, S Guindon, F Guo, J Guo, L Guo, Y Guo, R Gupta, R Gupta, S Gurbuz, S S Gurdasani, G Gustavino, M Guth, P Gutierrez, L F Gutierrez Zagazeta, M Gutsche, C Gutschow, C Gwenlan, C B Gwilliam, E S Haaland, A Haas, M Habedank, C Haber, H K Hadavand, A Hadef, S Hadzic, A I Hagan, J J Hahn, E H Haines, M Haleem, J Haley, J J Hall, G D Hallewell, L Halser, K Hamano, M Hamer, G N Hamity, E J Hampshire, J Han, K Han, L Han, L Han, S Han, Y F Han, K Hanagaki, M Hance, D A Hangal, H Hanif, M D Hank, J B Hansen, P H Hansen, K Hara, D Harada, T Harenberg, S Harkusha, M L Harris, Y T Harris, J Harrison, N M Harrison, P F Harrison, N M Hartman, N M Hartmann, Y Hasegawa, R Hauser, C M Hawkes, R J Hawkings, Y Hayashi, S Hayashida, D Hayden, C Hayes, R L Hayes, C P Hays, J M Hays, H S Hayward, F He, M He, Y He, Y He, Y He, N B Heatley, V Hedberg, A L Heggelund, N D Hehir, C Heidegger, K K Heidegger, W D Heidorn, J Heilman, S Heim, T Heim, J G Heinlein, J J Heinrich, L Heinrich, J Hejbal, A Held, S Hellesund, C M Helling, S Hellman, R C W Henderson, L Henkelmann, A M Henriques Correia, H Herde, Y Hernández Jiménez, L M Herrmann, T Herrmann, G Herten, R Hertenberger, L Hervas, M E Hesping, N P Hessey, E Hill, S J Hillier, J R Hinds, F Hinterkeuser, M Hirose, S Hirose, D Hirschbuehl, T G Hitchings, B Hiti, J Hobbs, R Hobincu, N Hod, M C Hodgkinson, B H Hodkinson, A Hoecker, D D Hofer, J Hofer, T Holm, M Holzbock, L B A H Hommels, B P Honan, J Hong, T M Hong, B H Hooberman, W H Hopkins, Y Horii, S Hou, A S Howard, J Howarth, J Hoya, M Hrabovsky, A Hrynevich, T Hryn’ova, P J Hsu, S -C Hsu, Q Hu, S Huang, X Huang, X Huang, Y Huang, Y Huang, Z Huang, Z Hubacek, M Huebner, F Huegging, T B Huffman, C A Hugli, M Huhtinen, S K Huiberts, R Hulsken, N Huseynov, J Huston, J Huth, R Hyneman, G Iacobucci, G Iakovidis, I Ibragimov, L Iconomidou-Fayard, J P Iddon, P Iengo, R Iguchi, T Iizawa, Y Ikegami, N Ilic, H Imam, M Ince Lezki, T Ingebretsen Carlson, G Introzzi, M Iodice, V Ippolito, R K Irwin, M Ishino, W Islam, C Issever, S Istin, H Ito, R Iuppa, A Ivina, J M Izen, V Izzo, P Jacka, P Jackson, B P Jaeger, C S Jagfeld, G Jain, P Jain, K Jakobs, T Jakoubek, J Jamieson, K W Janas, M Javurkova, L Jeanty, J Jejelava, P Jenni, C E Jessiman, C Jia, J Jia, X Jia, X Jia, Z Jia, S Jiggins, J Jimenez Pena, S Jin, A Jinaru, O Jinnouchi, P Johansson, K A Johns, J W Johnson, D M Jones, E Jones, P Jones, R W L Jones, T J Jones, H L Joos, R Joshi, J Jovicevic, X Ju, J J Junggeburth, T Junkermann, A Juste Rozas, M K Juzek, S Kabana, A Kaczmarska, M Kado, H Kagan, M Kagan, A Kahn, A Kahn, C Kahra, T Kaji, E Kajomovitz, N Kakati, I Kalaitzidou, C W Kalderon, N J Kang, D Kar, K Karava, M J Kareem, E Karentzos, I Karkanias, O Karkout, S N Karpov, Z M Karpova, V Kartvelishvili, A N Karyukhin, E Kasimi, J Katzy, S Kaur, K Kawade, M P Kawale, C Kawamoto, T Kawamoto, E F Kay, F I Kaya, S Kazakos, V F Kazanin, Y Ke, J M Keaveney, R Keeler, G V Kehris, J S Keller, A S Kelly, J J Kempster, P D Kennedy, O Kepka, B P Kerridge, S Kersten, B P Kerševan, S Keshri, L Keszeghova, S Ketabchi Haghighat, R A Khan, A Khanov, A G Kharlamov, T Kharlamova, E E Khoda, M Kholodenko, T J Khoo, G Khoriauli, J Khubua, Y A R Khwaira, B Kibirige, A Kilgallon, D W Kim, Y K Kim, N Kimura, M K Kingston, A Kirchhoff, C Kirfel, F Kirfel, J Kirk, A E Kiryunin, C Kitsaki, O Kivernyk, M Klassen, C Klein, L Klein, M H Klein, S B Klein, U Klein, P Klimek, A Klimentov, T Klioutchnikova, P Kluit, S Kluth, E Kneringer, T M Knight, A Knue, R Kobayashi, D Kobylianskii, S F Koch, M Kocian, P Kodyš, D M Koeck, P T Koenig, T Koffas, O Kolay, I Koletsou, T Komarek, K Köneke, A X Y Kong, T Kono, N Konstantinidis, P Kontaxakis, B Konya, R Kopeliansky, S Koperny, K Korcyl, K Kordas, A Korn, S Korn, I Korolkov, N Korotkova, B Kortman, O Kortner, S Kortner, W H Kostecka, V V Kostyukhin, A Kotsokechagia, A Kotwal, A Koulouris, A Kourkoumeli-Charalampidi, C Kourkoumelis, E Kourlitis, O Kovanda, R Kowalewski, W Kozanecki, A S Kozhin, V A Kramarenko, G Kramberger, P Kramer, M W Krasny, A Krasznahorkay, J W Kraus, J A Kremer, T Kresse, J Kretzschmar, K Kreul, P Krieger, S Krishnamurthy, M Krivos, K Krizka, K Kroeninger, H Kroha, J Kroll, J Kroll, K S Krowpman, U Kruchonak, H Krüger, N Krumnack, M C Kruse, O Kuchinskaia, S Kuday, S Kuehn, R Kuesters, T Kuhl, V Kukhtin, Y Kulchitsky, S Kuleshov, M Kumar, N Kumari, P Kumari, A Kupco, T Kupfer, A Kupich, O Kuprash, H Kurashige, L L Kurchaninov, O Kurdysh, Y A Kurochkin, A Kurova, M Kuze, A K Kvam, J Kvita, T Kwan, N G Kyriacou, L A O Laatu, C Lacasta, F Lacava, H Lacker, D Lacour, N N Lad, E Ladygin, B Laforge, T Lagouri, F Z Lahbabi, S Lai, I K Lakomiec, N Lalloue, J E Lambert, S Lammers, W Lampl, C Lampoudis, G Lamprinoudis, A N Lancaster, E Lançon, U Landgraf, M P J Landon, V S Lang, O K B Langrekken, A J Lankford, F Lanni, K Lantzsch, A Lanza, A Lapertosa, J F Laporte, T Lari, F Lasagni Manghi, M Lassnig, V Latonova, A Laudrain, A Laurier, S D Lawlor, Z Lawrence, R Lazaridou, M Lazzaroni, B Le, E M Le Boulicaut, B Leban, A Lebedev, M LeBlanc, F Ledroit-Guillon, A C A Lee, S C Lee, S Lee, T F Lee, L L Leeuw, H P Lefebvre, M Lefebvre, C Leggett, G Lehmann Miotto, M Leigh, W A Leight, W Leinonen, A Leisos, M A L Leite, C E Leitgeb, R Leitner, K J C Leney, T Lenz, S Leone, C Leonidopoulos, A Leopold, C Leroy, R Les, C G Lester, M Levchenko, J Levêque, L J Levinson, G Levrini, M P Lewicki, D J Lewis, A Li, B Li, C Li, C-Q Li, H Li, H Li, H Li, H Li, H Li, J Li, K Li, L Li, M Li, Q Y Li, S Li, S Li, T Li, X Li, Z Li, Z Li, Z Li, S Liang, Z Liang, M Liberatore, B Liberti, K Lie, J Lieber Marin, H Lien, K Lin, R E Lindley, J H Lindon, E Lipeles, A Lipniacka, A Lister, J D Little, B Liu, B X Liu, D Liu, J B Liu, J K K Liu, K Liu, M Liu, M Y Liu, P Liu, Q Liu, X Liu, X Liu, Y Liu, Y L Liu, Y W Liu, J Llorente Merino, S L Lloyd, E M Lobodzinska, P Loch, T Lohse, K Lohwasser, E Loiacono, M Lokajicek, J D Lomas, J D Long, I Longarini, L Longo, R Longo, I Lopez Paz, A Lopez Solis, N Lorenzo Martinez, A M Lory, G Löschcke Centeno, O Loseva, X Lou, X Lou, A Lounis, P A Love, G Lu, M Lu, S Lu, Y J Lu, H J Lubatti, C Luci, F L Lucio Alves, F Luehring, I Luise, O Lukianchuk, O Lundberg, B Lund-Jensen, N A Luongo, M S Lutz, A B Lux, D Lynn, R Lysak, E Lytken, V Lyubushkin, T Lyubushkina, M M Lyukova, H Ma, K Ma, L L Ma, W Ma, Y Ma, D M Mac Donell, G Maccarrone, J C MacDonald, P C Machado De Abreu Farias, R Madar, W F Mader, T Madula, J Maeda, T Maeno, H Maguire, V Maiboroda, A Maio, K Maj, O Majersky, S Majewski, N Makovec, V Maksimovic, B Malaescu, Pa Malecki, V P Maleev, F Malek, M Mali, D Malito, U Mallik, S Maltezos, S Malyukov, J Mamuzic, G Mancini, M N Mancini, G Manco, J P Mandalia, I Mandić, L Manhaes de Andrade Filho, I M Maniatis, J Manjarres Ramos, D C Mankad, A Mann, S Manzoni, L Mao, X Mapekula, A Marantis, G Marchiori, M Marcisovsky, C Marcon, M Marinescu, S Marium, M Marjanovic, E J Marshall, Z Marshall, S Marti-Garcia, T A Martin, V J Martin, B Martin dit Latour, L Martinelli, M Martinez, P Martinez Agullo, V I Martinez Outschoorn, P Martinez Suarez, S Martin-Haugh, V S Martoiu, A C Martyniuk, A Marzin, D Mascione, L Masetti, T Mashimo, J Masik, A L Maslennikov, P Massarotti, P Mastrandrea, A Mastroberardino, T Masubuchi, T Mathisen, J Matousek, N Matsuzawa, J Maurer, B Maček, D A Maximov, R Mazini, I Maznas, M Mazza, S M Mazza, E Mazzeo, C Mc Ginn, J P Mc Gowan, S P Mc Kee, C C McCracken, E F McDonald, A E McDougall, J A Mcfayden, R P McGovern, G Mchedlidze, R P Mckenzie, T C Mclachlan, D J Mclaughlin, S J McMahon, C M Mcpartland, R A McPherson, S Mehlhase, A Mehta, D Melini, B R Mellado Garcia, A H Melo, F Meloni, A M Mendes Jacques Da Costa, H Y Meng, L Meng, S Menke, M Mentink, E Meoni, G Mercado, C Merlassino, L Merola, C Meroni, J Metcalfe, A S Mete, C Meyer, J-P Meyer, R P Middleton, L Mijović, G Mikenberg, M Mikestikova, M Mikuž, H Mildner, A Milic, D W Miller, E H Miller, L S Miller, A Milov, D A Milstead, T Min, A A Minaenko, I A Minashvili, L Mince, A I Mincer, B Mindur, M Mineev, Y Mino, L M Mir, M Miralles Lopez, M Mironova, A Mishima, M C Missio, A Mitra, V A Mitsou, Y Mitsumori, O Miu, P S Miyagawa, T Mkrtchyan, M Mlinarevic, T Mlinarevic, M Mlynarikova, S Mobius, P Mogg, M H Mohamed Farook, A F Mohammed, S Mohapatra, G Mokgatitswane, L Moleri, B Mondal, S Mondal, K Mönig, E Monnier, L Monsonis Romero, J Montejo Berlingen, M Montella, F Montereali, F Monticelli, S Monzani, N Morange, A L Moreira De Carvalho, M Moreno Llácer, C Moreno Martinez, P Morettini, S Morgenstern, M Morii, M Morinaga, F Morodei, L Morvaj, P Moschovakos, B Moser, M Mosidze, T Moskalets, P Moskvitina, J Moss, A Moussa, E J W Moyse, O Mtintsilana, S Muanza, J Mueller, D Muenstermann, R Müller, G A Mullier, A J Mullin, J J Mullin, D P Mungo, D Munoz Perez, F J Munoz Sanchez, M Murin, W J Murray, M Muškinja, C Mwewa, A G Myagkov, A J Myers, G Myers, M Myska, B P Nachman, O Nackenhorst, K Nagai, K Nagano, J L Nagle, E Nagy, A M Nairz, Y Nakahama, K Nakamura, K Nakkalil, H Nanjo, R Narayan, E A Narayanan, I Naryshkin, M Naseri, S Nasri, C Nass, G Navarro, J Navarro-Gonzalez, R Nayak, A Nayaz, P Y Nechaeva, F Nechansky, L Nedic, T J Neep, A Negri, M Negrini, C Nellist, C Nelson, K Nelson, S Nemecek, M Nessi, M S Neubauer, F Neuhaus, J Neundorf, R Newhouse, P R Newman, C W Ng, Y W Y Ng, B Ngair, H D N Nguyen, R B Nickerson, R Nicolaidou, J Nielsen, M Niemeyer, J Niermann, N Nikiforou, V Nikolaenko, I Nikolic-Audit, K Nikolopoulos, P Nilsson, I Ninca, H R Nindhito, G Ninio, A Nisati, N Nishu, R Nisius, J-E Nitschke, E K Nkadimeng, T Nobe, D L Noel, T Nommensen, M B Norfolk, R R B Norisam, B J Norman, M Noury, J Novak, T Novak, L Novotny, R Novotny, L Nozka, K Ntekas, N M J Nunes De Moura Junior, J Ocariz, A Ochi, I Ochoa, S Oerdek, J T Offermann, A Ogrodnik, A Oh, C C Ohm, H Oide, R Oishi, M L Ojeda, Y Okumura, L F Oleiro Seabra, S A Olivares Pino, D Oliveira Damazio, D Oliveira Goncalves, J L Oliver, Ö O Öncel, A P O’Neill, A Onofre, P U E Onyisi, M J Oreglia, G E Orellana, D Orestano, N Orlando, R S Orr, V O’Shea, L M Osojnak, R Ospanov, G Otero y Garzon, H Otono, P S Ott, G J Ottino, M Ouchrif, F Ould-Saada, M Owen, R E Owen, K Y Oyulmaz, V E Ozcan, F Ozturk, N Ozturk, S Ozturk, H A Pacey, A Pacheco Pages, C Padilla Aranda, G Padovano, S Pagan Griso, G Palacino, A Palazzo, J Pampel, J Pan, T Pan, D K Panchal, C E Pandini, J G Panduro Vazquez, H D Pandya, H Pang, P Pani, G Panizzo, L Panwar, L Paolozzi, S Parajuli, A Paramonov, C Paraskevopoulos, D Paredes Hernandez, A Pareti, K R Park, T H Park, M A Parker, F Parodi, E W Parrish, V A Parrish, J A Parsons, U Parzefall, B Pascual Dias, L Pascual Dominguez, E Pasqualucci, S Passaggio, F Pastore, P Patel, U M Patel, J R Pater, T Pauly, C I Pazos, J Pearkes, M Pedersen, R Pedro, S V Peleganchuk, O Penc, E A Pender, G D Penn, K E Penski, M Penzin, B S Peralva, A P Pereira Peixoto, L Pereira Sanchez, D V Perepelitsa, E Perez Codina, M Perganti, H Pernegger, O Perrin, K Peters, R F Y Peters, B A Petersen, T C Petersen, E Petit, V Petousis, C Petridou, T Petru, A Petrukhin, M Pettee, N E Pettersson, A Petukhov, K Petukhova, R Pezoa, L Pezzotti, G Pezzullo, T M Pham, T Pham, P W Phillips, G Piacquadio, E Pianori, F Piazza, R Piegaia, D Pietreanu, A D Pilkington, M Pinamonti, J L Pinfold, B C Pinheiro Pereira, A E Pinto Pinoargote, L Pintucci, K M Piper, A Pirttikoski, D A Pizzi, L Pizzimento, A Pizzini, M -A Pleier, V Plesanovs, V Pleskot, E Plotnikova, G Poddar, R Poettgen, L Poggioli, I Pokharel, S Polacek, G Polesello, A Poley, A Polini, C S Pollard, Z B Pollock, E Pompa Pacchi, D Ponomarenko, L Pontecorvo, S Popa, G A Popeneciu, A Poreba, D M Portillo Quintero, S Pospisil, M A Postill, P Postolache, K Potamianos, P A Potepa, I N Potrap, C J Potter, H Potti, T Poulsen, J Poveda, M E Pozo Astigarraga, A Prades Ibanez, J Pretel, D Price, M Primavera, M A Principe Martin, R Privara, T Procter, M L Proffitt, N Proklova, K Prokofiev, G Proto, J Proudfoot, M Przybycien, W W Przygoda, A Psallidas, J E Puddefoot, D Pudzha, D Pyatiizbyantseva, J Qian, D Qichen, Y Qin, T Qiu, A Quadt, M Queitsch-Maitland, G Quetant, R P Quinn, G Rabanal Bolanos, D Rafanoharana, F Ragusa, J L Rainbolt, J A Raine, S Rajagopalan, E Ramakoti, I A Ramirez-Berend, K Ran, N P Rapheeha, H Rasheed, V Raskina, D F Rassloff, A Rastogi, S Rave, B Ravina, I Ravinovich, M Raymond, A L Read, N P Readioff, D M Rebuzzi, G Redlinger, A S Reed, K Reeves, J A Reidelsturz, D Reikher, A Rej, C Rembser, M Renda, M B Rendel, F Renner, A G Rennie, A L Rescia, S Resconi, M Ressegotti, S Rettie, J G Reyes Rivera, E Reynolds, O L Rezanova, P Reznicek, H Riani, N Ribaric, E Ricci, R Richter, S Richter, E Richter-Was, M Ridel, S Ridouani, P Rieck, P Riedler, E M Riefel, J O Rieger, M Rijssenbeek, M Rimoldi, L Rinaldi, T T Rinn, M P Rinnagel, G Ripellino, I Riu, J C Rivera Vergara, F Rizatdinova, E Rizvi, B R Roberts, S H Robertson, D Robinson, C M Robles Gajardo, M Robles Manzano, A Robson, A Rocchi, C Roda, S Rodriguez Bosca, Y Rodriguez Garcia, A Rodriguez Rodriguez, A M Rodríguez Vera, S Roe, J T Roemer, A R Roepe-Gier, J Roggel, O Røhne, R A Rojas, C P A Roland, J Roloff, A Romaniouk, E Romano, M Romano, A C Romero Hernandez, N Rompotis, L Roos, S Rosati, B J Rosser, E Rossi, E Rossi, L P Rossi, L Rossini, R Rosten, M Rotaru, B Rottler, C Rougier, D Rousseau, D Rousso, A Roy, S Roy-Garand, A Rozanov, Z M A Rozario, Y Rozen, A Rubio Jimenez, A J Ruby, V H Ruelas Rivera, T A Ruggeri, A Ruggiero, A Ruiz-Martinez, A Rummler, Z Rurikova, N A Rusakovich, H L Russell, G Russo, J P Rutherfoord, S Rutherford Colmenares, K Rybacki, M Rybar, E B Rye, A Ryzhov, J A Sabater Iglesias, P Sabatini, H F-W Sadrozinski, F Safai Tehrani, B Safarzadeh Samani, M Safdari, S Saha, M Sahinsoy, A Saibel, M Saimpert, M Saito, T Saito, D Salamani, A Salnikov, J Salt, A Salvador Salas, D Salvatore, F Salvatore, A Salzburger, D Sammel, D Sampsonidis, D Sampsonidou, J Sánchez, V Sanchez Sebastian, H Sandaker, C O Sander, J A Sandesara, M Sandhoff, C Sandoval, D P C Sankey, T Sano, A Sansoni, L Santi, C Santoni, H Santos, A Santra, K A Saoucha, J G Saraiva, J Sardain, O Sasaki, K Sato, C Sauer, F Sauerburger, E Sauvan, P Savard, R Sawada, C Sawyer, L Sawyer, I Sayago Galvan, C Sbarra, A Sbrizzi, T Scanlon, J Schaarschmidt, U Schäfer, A C Schaffer, D Schaile, R D Schamberger, C Scharf, M M Schefer, V A Schegelsky, D Scheirich, F Schenck, M Schernau, C Scheulen, C Schiavi, M Schioppa, B Schlag, K E Schleicher, S Schlenker, J Schmeing, M A Schmidt, K Schmieden, C Schmitt, N Schmitt, S Schmitt, L Schoeffel, A Schoening, P G Scholer, E Schopf, M Schott, J Schovancova, S Schramm, T Schroer, H-C Schultz-Coulon, M Schumacher, B A Schumm, Ph Schune, A J Schuy, H R Schwartz, A Schwartzman, T A Schwarz, Ph Schwemling, R Schwienhorst, A Sciandra, G Sciolla, F Scuri, C D Sebastiani, K Sedlaczek, P Seema, S C Seidel, A Seiden, B D Seidlitz, C Seitz, J M Seixas, G Sekhniaidze, L Selem, N Semprini-Cesari, D Sengupta, V Senthilkumar, L Serin, L Serkin, M Sessa, H Severini, F Sforza, A Sfyrla, Q Sha, E Shabalina, R Shaheen, J D Shahinian, D Shaked Renous, L Y Shan, M Shapiro, A Sharma, A S Sharma, P Sharma, P B Shatalov, K Shaw, S M Shaw, A Shcherbakova, Q Shen, D J Sheppard, P Sherwood, L Shi, X Shi, C O Shimmin, J D Shinner, I P J Shipsey, S Shirabe, M Shiyakova, J Shlomi, M J Shochet, J Shojaii, D R Shope, B Shrestha, S Shrestha, E M Shrif, M J Shroff, P Sicho, A M Sickles, E Sideras Haddad, A Sidoti, F Siegert, Dj Sijacki, F Sili, J M Silva, M V Silva Oliveira, S B Silverstein, S Simion, R Simoniello, E L Simpson, H Simpson, L R Simpson, N D Simpson, S Simsek, S Sindhu, P Sinervo, S Singh, S Sinha, S Sinha, M Sioli, I Siral, E Sitnikova, J Sjölin, A Skaf, E Skorda, P Skubic, M Slawinska, V Smakhtin, B H Smart, S Yu Smirnov, Y Smirnov, L N Smirnova, O Smirnova, A C Smith, E A Smith, H A Smith, J L Smith, R Smith, M Smizanska, K Smolek, A A Snesarev, S R Snider, H L Snoek, S Snyder, R Sobie, A Soffer, C A Solans Sanchez, E Yu Soldatov, U Soldevila, A A Solodkov, S Solomon, A Soloshenko, K Solovieva, O V Solovyanov, V Solovyev, P Sommer, A Sonay, W Y Song, A Sopczak, A L Sopio, F Sopkova, J D Sorenson, I R Sotarriva Alvarez, V Sothilingam, O J Soto Sandoval, S Sottocornola, R Soualah, Z Soumaimi, D South, N Soybelman, S Spagnolo, M Spalla, D Sperlich, G Spigo, S Spinali, D P Spiteri, M Spousta, E J Staats, R Stamen, A Stampekis, M Standke, E Stanecka, M V Stange, B Stanislaus, M M Stanitzki, B Stapf, E A Starchenko, G H Stark, J Stark, P Staroba, P Starovoitov, S Stärz, R Staszewski, G Stavropoulos, J Steentoft, P Steinberg, B Stelzer, H J Stelzer, O Stelzer-Chilton, H Stenzel, T J Stevenson, G A Stewart, J R Stewart, M C Stockton, G Stoicea, M Stolarski, S Stonjek, A Straessner, J Strandberg, S Strandberg, M Stratmann, M Strauss, T Strebler, P Strizenec, R Ströhmer, D M Strom, R Stroynowski, A Strubig, S A Stucci, B Stugu, J Stupak, N A Styles, D Su, S Su, W Su, X Su, D Suchy, K Sugizaki, V V Sulin, M J Sullivan, D M S Sultan, L Sultanaliyeva, S Sultansoy, T Sumida, S Sun, S Sun, O Sunneborn Gudnadottir, N Sur, M R Sutton, H Suzuki, M Svatos, M Swiatlowski, T Swirski, I Sykora, M Sykora, T Sykora, D Ta, K Tackmann, A Taffard, R Tafirout, J S Tafoya Vargas, Y Takubo, M Talby, A A Talyshev, K C Tam, N M Tamir, A Tanaka, J Tanaka, R Tanaka, M Tanasini, Z Tao, S Tapia Araya, S Tapprogge, A Tarek Abouelfadl Mohamed, S Tarem, K Tariq, G Tarna, G F Tartarelli, P Tas, M Tasevsky, E Tassi, A C Tate, G Tateno, Y Tayalati, G N Taylor, W Taylor, A S Tee, R Teixeira De Lima, P Teixeira-Dias, J J Teoh, K Terashi, J Terron, S Terzo, M Testa, R J Teuscher, A Thaler, O Theiner, N Themistokleous, T Theveneaux-Pelzer, O Thielmann, D W Thomas, J P Thomas, E A Thompson, P D Thompson, E Thomson, Y Tian, V Tikhomirov, Yu A Tikhonov, S Timoshenko, D Timoshyn, E X L Ting, P Tipton, S H Tlou, A Tnourji, K Todome, S Todorova-Nova, S Todt, M Togawa, J Tojo, S Tokár, K Tokushuku, O Toldaiev, R Tombs, M Tomoto, L Tompkins, K W Topolnicki, E Torrence, H Torres, E Torró Pastor, M Toscani, C Tosciri, M Tost, D R Tovey, A Traeet, I S Trandafir, T Trefzger, A Tricoli, I M Trigger, S Trincaz-Duvoid, D A Trischuk, B Trocmé, L Truong, M Trzebinski, A Trzupek, F Tsai, M Tsai, A Tsiamis, P V Tsiareshka, S Tsigaridas, A Tsirigotis, V Tsiskaridze, E G Tskhadadze, M Tsopoulou, Y Tsujikawa, I I Tsukerman, V Tsulaia, S Tsuno, K Tsuri, D Tsybychev, Y Tu, A Tudorache, V Tudorache, A N Tuna, S Turchikhin, I Turk Cakir, R Turra, T Turtuvshin, P M Tuts, S Tzamarias, P Tzanis, E Tzovara, F Ukegawa, P A Ulloa Poblete, E N Umaka, G Unal, M Unal, A Undrus, G Unel, J Urban, P Urquijo, P Urrejola, G Usai, R Ushioda, M Usman, Z Uysal, V Vacek, B Vachon, K O H Vadla, T Vafeiadis, A Vaitkus, C Valderanis, E Valdes Santurio, M Valente, S Valentinetti, A Valero, E Valiente Moreno, A Vallier, J A Valls Ferrer, D R Van Arneman, T R Van Daalen, A Van Der Graaf, P Van Gemmeren, M Van Rijnbach, S Van Stroud, I Van Vulpen, M Vanadia, W Vandelli, E R Vandewall, D Vannicola, L Vannoli, R Vari, E W Varnes, C Varni, T Varol, D Varouchas, L Varriale, K E Varvell, M E Vasile, L Vaslin, G A Vasquez, A Vasyukov, R Vavricka, F Vazeille, T Vazquez Schroeder, J Veatch, V Vecchio, M J Veen, I Veliscek, L M Veloce, F Veloso, S Veneziano, A Ventura, S Ventura Gonzalez, A Verbytskyi, M Verducci, C Vergis, M Verissimo De Araujo, W Verkerke, J C Vermeulen, C Vernieri, M Vessella, M C Vetterli, A Vgenopoulos, N Viaux Maira, T Vickey, O E Vickey Boeriu, G H A Viehhauser, L Vigani, M Villa, M Villaplana Perez, E M Villhauer, E Vilucchi, M G Vincter, G S Virdee, A Vishwakarma, A Visibile, C Vittori, I Vivarelli, E Voevodina, F Vogel, J C Voigt, P Vokac, Yu Volkotrub, J Von Ahnen, E Von Toerne, B Vormwald, V Vorobel, K Vorobev, M Vos, K Voss, M Vozak, L Vozdecky, N Vranjes, M Vranjes Milosavljevic, M Vreeswijk, N K Vu, R Vuillermet, O Vujinovic, I Vukotic, S Wada, C Wagner, J M Wagner, W Wagner, S Wahdan, H Wahlberg, M Wakida, J Walder, R Walker, W Walkowiak, A Wall, E J Wallin, T Wamorkar, A Z Wang, C Wang, C Wang, H Wang, J Wang, R -J Wang, R Wang, R Wang, S M Wang, S Wang, T Wang, W T Wang, W Wang, X Wang, X Wang, X Wang, Y Wang, Y Wang, Z Wang, Z Wang, Z Wang, A Warburton, R J Ward, N Warrack, S Waterhouse, A T Watson, H Watson, M F Watson, E Watton, G Watts, B M Waugh, C Weber, H A Weber, M S Weber, S M Weber, C Wei, Y Wei, A R Weidberg, E J Weik, J Weingarten, M Weirich, C Weiser, C J Wells, T Wenaus, B Wendland, T Wengler, N S Wenke, N Wermes, M Wessels, A M Wharton, A S White, A White, M J White, D Whiteson, L Wickremasinghe, W Wiedenmann, M Wielers, C Wiglesworth, D J Wilbern, H G Wilkens, D M Williams, H H Williams, S Williams, S Willocq, B J Wilson, P J Windischhofer, F I Winkel, F Winklmeier, B T Winter, J K Winter, M Wittgen, M Wobisch, Z Wolffs, J Wollrath, M W Wolter, H Wolters, E L Woodward, S D Worm, B K Wosiek, K W Woźniak, S Wozniewski, K Wraight, C Wu, M Wu, M Wu, S L Wu, X Wu, Y Wu, Z Wu, J Wuerzinger, T R Wyatt, B M Wynne, S Xella, L Xia, M Xia, J Xiang, M Xie, X Xie, S Xin, A Xiong, J Xiong, D Xu, H Xu, L Xu, R Xu, T Xu, Y Xu, Z Xu, Z Xu, B Yabsley, S Yacoob, Y Yamaguchi, E Yamashita, H Yamauchi, T Yamazaki, Y Yamazaki, J Yan, S Yan, Z Yan, H J Yang, H T Yang, S Yang, T Yang, X Yang, X Yang, Y Yang, Y Yang, Z Yang, W-M Yao, H Ye, H Ye, J Ye, S Ye, X Ye, Y Yeh, I Yeletskikh, B K Yeo, M R Yexley, P Yin, K Yorita, S Younas, C J S Young, C Young, C Yu, Y Yu, M Yuan, R Yuan, L Yue, M Zaazoua, B Zabinski, E Zaid, Z K Zak, T Zakareishvili, N Zakharchuk, S Zambito, J A Zamora Saa, J Zang, D Zanzi, O Zaplatilek, C Zeitnitz, H Zeng, J C Zeng, D T Zenger Jr, O Zenin, T Ženiš, S Zenz, S Zerradi, D Zerwas, M Zhai, D F Zhang, J Zhang, J Zhang, K Zhang, L Zhang, P Zhang, R Zhang, S Zhang, S Zhang, T Zhang, X Zhang, X Zhang, Y Zhang, Y Zhang, Y Zhang, Z Zhang, Z Zhang, H Zhao, T Zhao, Y Zhao, Z Zhao, A Zhemchugov, J Zheng, K Zheng, X Zheng, Z Zheng, D Zhong, B Zhou, H Zhou, N Zhou, Y Zhou, Y Zhou, C G Zhu, J Zhu, Y Zhu, Y Zhu, X Zhuang, K Zhukov, N I Zimine, J Zinsser, M Ziolkowski, L Živković, A Zoccoli, K Zoch, T G Zorbas, O Zormpa, W Zou, L Zwalinski, The ATLAS Collaborationgabriela.navarro@uan.edu.co","doi":"10.1088/2632-2153/ad611e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad611e","url":null,"abstract":"The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta <inline-formula>\u0000<tex-math><?CDATA $p_{text{T}}gt 500$?></tex-math><mml:math overflow=\"scroll\"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mtext>T</mml:mtext></mml:mrow></mml:msub><mml:mo>&gt;</mml:mo><mml:mn>500</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"mlstad611eieqn1.gif\"></inline-graphic></inline-formula> GeV.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"1 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SemiH: DFT Hamiltonian neural network training with semi-supervised learning","authors":"Yucheol Cho, Guenseok Choi, Gyeongdo Ham, Mincheol Shin, Daeshik Kim","doi":"10.1088/2632-2153/ad7227","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7227","url":null,"abstract":"Over the past decades, density functional theory (DFT) calculations have been utilized in various fields such as materials science and semiconductor devices. However, due to the inherent nature of DFT calculations, which rigorously consider interactions between atoms, they require significant computational cost. To address this, extensive research has recently focused on training neural networks to replace DFT calculations. However, previous methods for training neural networks necessitated an extensive number of DFT simulations to acquire the ground truth (Hamiltonians). Conversely, when dealing with a limited amount of training data, deep learning models often display increased errors in predicting Hamiltonians and band structures for testing data. This phenomenon poses the potential risk of generating inaccurate physical interpretations, including the emergence of unphysical branches within band structures. To tackle this challenge, we propose a novel deep learning-based method for calculating DFT Hamiltonians, specifically tailored to produce accurate results with limited training data. Our framework not only employs supervised learning with the calculated Hamiltonian but also generates pseudo Hamiltonians (targets for unlabeled data) and trains the neural networks on unlabeled data. Particularly, our approach, which leverages unlabeled data, is noteworthy as it marks the first attempt in the field of neural network Hamiltonians. Our framework showcases the superior performance of our framework compared to the state-of-the-art approach across various datasets, such as MoS₂, Bi₂Te₃, HfO₂, and InGaAs. Moreover, our framework demonstrates enhanced generalization performance by effectively utilizing unlabeled data, achieving noteworthy results when evaluated on data more complex than the training set, such as configurations with more atoms and temperature ranges outside the training data.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"71 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating cell growth and hypoxic regions of 3D spheroids via a machine learning approach","authors":"Jaekak Yoo, Jae Won Choi, Eunha Kim, Eun-Jung Park, Ahruem Baek, Jaeseok Kim, Mun Seok Jeong, Youngwoo Cho, Tae Geol Lee, Min Beom Heo","doi":"10.1088/2632-2153/ad718e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad718e","url":null,"abstract":"This study investigated the applicability of the area of spheroids and hypoxic regions for efficient evaluation of drug efficacy using machine learning (ML). We initially developed a high-throughput detection method to obtain the area of spheroids and hypoxic regions that can handle over 10 000 images per hour with an error rate of 2%–3%. The ML models were trained using cell growth of six cell lines (<italic toggle=\"yes\">i.e.</italic> HepG2, A549, Hep3B, BEAS-2B, HT-29, and HCT116) and hypoxic region variations of two cell lines (<italic toggle=\"yes\">i.e.</italic> HepG2 and BEAS-2B); our model can predict the area of spheroids and hypoxic region of certain growth date with high precision. To demonstrate the applicability, HepG2 spheroids were treated with sorafenib, and the efficacy of the drug was evaluated through a comparison of differences in areas of cell size and hypoxic regions with the predicted results. Furthermore, our ML approach has been shown to be applicable to provide the model-driven evaluative criterion for toxicity and drug efficacy using spheroids.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"71 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging normalizing flows for orbital-free density functional theory","authors":"Alexandre de Camargo, Ricky T Q Chen, Rodrigo A Vargas-Hernández","doi":"10.1088/2632-2153/ad7226","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7226","url":null,"abstract":"Orbital-free density functional theory (OF-DFT) for real-space systems has historically depended on Lagrange optimization techniques, primarily due to the inability of previously proposed electron density approaches to ensure the normalization constraint. This study illustrates how leveraging contemporary generative models, notably normalizing flows (NFs), can surmount this challenge. We develop a Lagrangian-free optimization framework by employing these machine learning models for the electron density. This diverse approach also integrates cutting-edge variational inference techniques and equivariant deep learning models, offering an innovative reformulation to the OF-DFT problem. We demonstrate the versatility of our framework by simulating a one-dimensional diatomic system, LiH, and comprehensive simulations of hydrogen, lithium hydride, water, and four hydrocarbon molecules. The inherent flexibility of NFs facilitates initialization with promolecular densities, markedly enhancing the efficiency of the optimization process.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"27 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural force functional for non-equilibrium many-body colloidal systems","authors":"Toni Zimmermann, Florian Sammüller, Sophie Hermann, Matthias Schmidt, Daniel de las Heras","doi":"10.1088/2632-2153/ad7191","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7191","url":null,"abstract":"We combine power functional theory and machine learning to study non-equilibrium overdamped many-body systems of colloidal particles at the level of one-body fields. We first sample in steady state the one-body fields relevant for the dynamics from computer simulations of Brownian particles under the influence of randomly generated external fields. A neural network is then trained with this data to represent locally in space the formally exact functional mapping from the one-body density and velocity profiles to the one-body internal force field. The trained network is used to analyse the non-equilibrium superadiabatic force field and the transport coefficients such as shear and bulk viscosities. Due to the local learning approach, the network can be applied to systems much larger than the original simulation box in which the one-body fields are sampled. Complemented with the exact non-equilibrium one-body force balance equation and a continuity equation, the network yields viable predictions of the dynamics in time-dependent situations. Even though training is based on steady states only, the predicted dynamics is in good agreement with simulation results. A neural dynamical density functional theory can be straightforwardly implemented as a limiting case in which the internal force field is that of an equilibrium system. The framework is general and directly applicable to other many-body systems of interacting particles following Brownian dynamics.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"44 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triggering dark showers with conditional dual auto-encoders","authors":"Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier, Nadezda Chernyavskaya, Maurizio Pierini","doi":"10.1088/2632-2153/ad652b","DOIUrl":"https://doi.org/10.1088/2632-2153/ad652b","url":null,"abstract":"We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g. in the real-time event triggering systems of large hadron collider experiments such as ATLAS and CMS.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"43 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying chaotic dynamics in noisy time series through multimodal deep neural networks","authors":"Alessandro Giuseppi, Danilo Menegatti, Antonio Pietrabissa","doi":"10.1088/2632-2153/ad7190","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7190","url":null,"abstract":"Chaos detection is the problem of identifying whether a series of measurements is being sampled from an underlying set of chaotic dynamics. The unavoidable presence of measurement noise significantly affects the performance of chaos detectors, as discerning chaotic dynamics from stochastic signals becomes more challenging. This paper presents a computationally efficient multimodal deep neural network tailored for chaos detection by combining information coming from the analysis of time series, recurrence plots and spectrograms. The proposed approach is the first one suitable for multi-class classification of chaotic systems while being robust with respect to measurement noise, and is validated on a dataset of 15 different chaotic and non-chaotic dynamics subject to white, pink or brown colored noise.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"2 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chaotic attractor reconstruction using small reservoirs—the influence of topology","authors":"Lina Jaurigue","doi":"10.1088/2632-2153/ad6ee8","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6ee8","url":null,"abstract":"Forecasting timeseries based upon measured data is needed in a wide range of applications and has been the subject of extensive research. A particularly challenging task is the forecasting of timeseries generated by chaotic dynamics. In recent years reservoir computing has been shown to be an effective method of forecasting chaotic dynamics and reconstructing chaotic attractors from data. In this work strides are made toward smaller and lower complexity reservoirs with the goal of improved hardware implementability and more reliable production of adequate surrogate models. We show that a reservoir of uncoupled nodes more reliably produces long term timeseries predictions than more complex reservoir topologies. We then link the improved attractor reconstruction of the uncoupled reservoir with smaller spectral radii of the resulting surrogate systems. These results indicate that, the node degree plays an important role in determining whether the desired dynamics will be stable in the autonomous surrogate system which is attained via closed-loop operation of the trained reservoir. In terms of hardware implementability, uncoupled nodes would allow for greater freedom in the hardware architecture because no complex coupling setups are needed and because, for uncoupled nodes, the system response is equivalent for space and time multiplexing.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"JefiAtten: an attention-based neural network model for solving Maxwell’s equations with charge and current sources","authors":"Ming-Yan Sun, Peng Xu, Jun-Jie Zhang, Tai-Jiao Du, Jian-Guo Wang","doi":"10.1088/2632-2153/ad6ee9","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6ee9","url":null,"abstract":"We present JefiAtten, a novel neural network model employing the attention mechanism to solve Maxwell’s equations efficiently. JefiAtten uses self-attention and cross-attention modules to understand the interplay between charge density, current density, and electromagnetic fields. Our results indicate that JefiAtten can generalize well to a range of scenarios, maintaining accuracy across various spatial distribution and handling amplitude variations. The model showcases an improvement in computation speed after training, compared to traditional integral methods. The adaptability of the model suggests potential for broader applications in computational physics, with further refinements to enhance its predictive capabilities and computational efficiency. Our work is a testament to the efficacy of integrating attention mechanisms with numerical simulations, marking a step forward in the quest for data-driven solutions to physical phenomena.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"30 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven sparse modeling of oscillations in plasma space propulsion","authors":"Borja Bayón-Buján, Mario Merino","doi":"10.1088/2632-2153/ad6d29","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6d29","url":null,"abstract":"An algorithm to obtain data-driven models of oscillatory phenomena in plasma space propulsion systems is presented, based on sparse regression (SINDy) and Pareto front analysis. The algorithm can incorporate physical constraints, use data bootstrapping for additional robustness, and fine-tuning to different metrics. Standard, weak and integral SINDy formulations are discussed and compared. The scheme is benchmarked for the case of breathing-mode oscillations in Hall effect thrusters, using particle-in-cell/fluid simulation data. Models of varying complexity are obtained for the average plasma properties, and shown to have a clear physical interpretability and agreement with existing 0D models in the literature. Lastly, the algorithm applied is also shown to enable the identification of physical subdomains with qualitatively different plasma dynamics, providing valuable information for more advanced modeling approaches.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"11 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}