2561 DPyC SMF false [["minutes", "Minutes"], ["notes", "Notes"], ["paper", "Paper"], ["poster", "Poster"], ["slides", "Slides"], ["summary", "Summary"]] Cinvestav ea481e58ca9c64e14d7982cb66bcd604 3312 The Division of Particles and Fields of the Mexican Physical Society (DPyC-SMF) organizes every year the Annual Meeting (RADPyC). This space is dedicated mainly for the exposure of results of graduate students and young researchers working in high energy physics and includes some review talks on the hot topics of the field. This year the meeting will be held in Auditorio José Ádem, Cinvestav, Mexico City, México. The scientific programme will consist of invited and contributed talks, and a poster session. It will begin in the morning of Wednesday 17 June and end before lunch on Friday 19 June. We will have the following invited talks: - Ana Colmenero, HAWC (UMSNH) - Jens Erler, The EW SM and constraints on NP (Mainz, PRISMA) - Iván Martínez Soler, Neutrino physics (Durham Univ.) - Cristina Oropeza, CMS (Ibero) - Antonio Ortiz, ALICE (ICN-UNAM) - Gabriel Palacios, Accelerator physics (UNAM) - Antonio Pich, Flavor physics (IFIC) - Pedro Podesta, Belle-II (UAS) - Selomit Ramírez, QCD, Loop Tree Duality, Quantum Computing (UAS) - Saúl Ramos, Flavor physics from a top-down perspective (IF-UNAM) - Alfredo Raya, NICA (UMSNH) - Eric Vázquez, DM-neutrinos (exp.) (IF-UNAM) The whole event can be followed by Zoom: https://us02web.zoom.us/j/85815121456?pwd=t1JWdqhgarEQRyewmXIEzNlkIcqU8a.1 (858 1512 1456 & 044752). Due to recent internet security problems at Cinvestav it will NOT be broadcasted via the DPyC facebook account (facebook is currently blocked on our premises). False 2026-06-17T09:00:00 2026-06-19T14:30:00 2026-03-23T09:50:55 2026-06-19T17:51:55 Mexico/General cinvestav f09241183e84638d226a8885656239cd Cinvestav 6be00aeecce15d4b2547496f45352003 CINVESTAV-IPN Mexico 207484e6583dd9c0c1af2f52ce04fdd8 Cinvestav ea481e58ca9c64e14d7982cb66bcd604 Cinvestav 3cd3ab69a079e614c15e4c750e7d3d60 BUAP 5848375cb8b0db28e94350f19dcb744f Benemérita Universidad Autónoma de Puebla 65efd11437264d48aa1f7486255a57bc 88 false [["minutes", "Minutes"], ["notes", "Notes"], ["paper", "Paper"], ["poster", "Poster"], ["slides", "Slides"], ["summary", "Summary"]] Facultad de Ciencias e00a176336cee75ef58f5ee03f2b7e4b Facultad de Ciencias e00a176336cee75ef58f5ee03f2b7e4b ICN-UNAM 22ee5d51bdc81ef0c65de4f8cfb3a642 00:15 Machine learning techniques are widely used in many research areas, especially in high energy physics. Here we use the Random Forests algorithm to reconstruct tracks, identify particles, as well as measure their basic properties, like transverse momentum in the environment of the MPD experiment.The results demonstrate a significant improvement, achieving classification purities exceeding 90% alongside a higher overall efficiency. This methodological optimization provide great tools to mitigate systematic fluctuations arising from particle misidentification, reaching high precision on measurements.