{"id":19102,"date":"2025-06-30T21:17:00","date_gmt":"2025-06-30T21:17:00","guid":{"rendered":"https:\/\/crosettofoundation.org\/?p=19102"},"modified":"2025-07-01T16:57:34","modified_gmt":"2025-07-01T16:57:34","slug":"trasparenza-e-responsabilita-lunica-via-per-salvare-il-cern-da-sprechi-miliardari-e-sbloccare-innovazioni-cruciali-per-i-pazienti-oncologici","status":"publish","type":"post","link":"https:\/\/crosettofoundation.org\/it\/trasparenza-e-responsabilita-lunica-via-per-salvare-il-cern-da-sprechi-miliardari-e-sbloccare-innovazioni-cruciali-per-i-pazienti-oncologici\/","title":{"rendered":"Trasparenza e Responsabilit\u00e0: L’Unica Via per Salvare il CERN da Sprechi Miliardari e Sbloccare Innovazioni Cruciali per i Pazienti Oncologici"},"content":{"rendered":"

In English<\/strong> in PDF: https:\/\/bit.ly\/3TMnDNI<\/a>\u00a0 In Italiano<\/strong> in PDF:\u00a0 https:\/\/bit.ly\/4nsvk9E<\/a><\/p>\n

As Long as Parliamentarians Continue Funding CERN\u2019s 20-Trillion-Transistor, 650 kW FPGA Systems (66 Operations), CERN Will Avoid a Public Comparison with Crosetto\u2019s 6 kW 3D-Flow System (8,000+ Operations)<\/em><\/p>\n

Dallas, June 30, 2025<\/strong> \/Press Release\/ \u2013 The Crosetto Foundation for the Reduction of Cancer Deaths, dedicated to defending taxpayers and cancer patients, is taking a firm stand against the recent approval, on 20 June 2025, by the CERN Council<\/strong> of the Medium-Term Plan (MTP) to secure resources for the Phase 2 upgrades of the ATLAS and CMS experiments. Irrefutable facts and evidence demonstrate that these plans will<\/strong> waste taxpayers over \u20ac12 billion in the next decade<\/strong>.<\/p>\n

CERN has denied transparency and accountability for decades and continues to squander public funding, consequently, taxpayers\u2014through their<\/strong> Members of the European Parliament (MEPs) and national parliamentarians<\/strong> who manage their money\u2014are compelled to demand a freeze on funding for these projects<\/strong>. Funding should only resume after a public, side-by-side comparison<\/strong> in a public meeting between CERN\u2019s designers of FPGA-Based Level-1 Trigger architecture<\/strong> (consuming 650 kW while limited to 66 programmable operations per dataset) and Crosetto\u2019s 3D-Flow architecture<\/strong> (consuming only 6 kW while enabling over 8,000 programmable operations per dataset, such as add, subtract, multiply, move, etc.).\"\"<\/p>\n

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<\/a><\/a>1. \u00a0\u00a0 The Broader Impact: From Fundamental Physics to Cancer Detection<\/h1>\n

Italian-American scientist Dario Crosetto, founder of the namesake foundation dedicated to significantly reduce cancer deaths, is actively writing letters to MEPs and national parliamentarians (details at: https:\/\/bit.ly\/4era28b<\/a>). He explains that the ongoing waste of public funds in fundamental physics research, combined with CERN\u2019s refusal to allow transparency, has damaged progress in many other fields<\/strong>\u2014particularly healthcare\u2014by preventing funding for innovations such as the 3D-CBS (3D Complete Body Screening). <\/strong>This advanced PET\/CT system can detect cancer at an early curable stage and has the potential to cut premature cancer deaths by half<\/strong>.<\/p>\n

Positron Emission Tomography (PET)<\/strong> technology, fundamental to the 3D-CBS, relies on the accurate extraction<\/strong> and identification<\/strong> of specific photon<\/strong>s (pairs of 511 keV) linked to tumor markers from radiation<\/strong>. This task is remarkably similar<\/strong> to<\/strong> the work performed in multi-billion euro experiments at CERN<\/strong>, which involve extracting and identifying various particles<\/strong> like photons, electrons, hadrons, muons, Higgs bosons, and other particles from radiation.<\/p>\n

Radiation is emitted continuously<\/strong>, regardless of how efficiently we extract valuable information. To enhance efficiency<\/strong>, instruments such as PET devices or CERN’s Level-1 Trigger must be capable of executing complex, programmable algorithms<\/strong>. These algorithms need a sufficient number of operations to efficiently filter valuable data while sustaining ultra-high data streams without loss.<\/p>\n

<\/a><\/a>2. \u00a0\u00a0 The Core of the Problem: Inefficient Data Filtering at CERN<\/h1>\n

In high-energy physics experiments (HEP), the apparatus\u2014the LHC accelerator and its detectors\u2014generated 1.2 billion events per second<\/strong> (equivalent to 2.4 Petabytes per second) until 2025. The upcoming upgrade to the High-Luminosity LHC (HL-LHC), approved by the CERN Council for 2026-2036, is projected to generate an astonishing 8 billion events per second<\/strong> (67 Petabytes per second).<\/p>\n

The most crucial component<\/strong> in this entire multi-billion euro project is an electronic data acquisition and processing unit called the Level-1 Trigger<\/strong>. Its primary task is to analyze and filter incoming data in real-time from a subset of fast subdetectors. This system is designed to keep only the most promising events, discarding over 99% of the rest<\/strong>. This extreme filtering is necessary because saving all raw data would overwhelm every hard drive on the planet in a single day.<\/p>\n

However, data discarded by an inefficient Level-1 Trigger is lost forever<\/strong>. An inefficient system means billions of euros are wasted<\/strong> as thousands of scientists spend years analyzing irrelevant data. To effectively filter promising events and resolve the “pile-up” (multiple interactions per beam crossing) at the HL-LHC, the Level-1 Trigger will need to execute algorithms with thousands programmable operations<\/strong> on each dataset arriving every 25 nanoseconds, without missing any data.<\/p>\n

<\/a><\/a>3. \u00a0\u00a0 The Devastating Cost of Inefficiency: Beyond Euros to Human Lives<\/h1>\n

While it is detrimental to taxpayers that CERN has wasted over<\/strong> \u20ac4 billion in past decades<\/strong> due to the inefficiency of their FPGA-Based Level-1 Triggers, the consequences are far more damaging in medical imaging applications<\/strong>.<\/p>\n

The suppression of Crosetto\u2019s 3D-Flow and 3D-CBS inventions has, for decades, prevented the development of devices capable of accurately extracting and identifying all valuable data from radiation linked to tumor markers.<\/strong> These inventions could enable cost-effective early cancer detection<\/strong>, at a very low examination cost, requiring a very low radiation doses, and identifying tumors with fewer than 100 cancer cells\u2014a breakthrough that saves lives<\/strong>.<\/p>\n

Suppressing the advantages of the 3D-Flow invention in physics experiments results in billions of euros lost by taxpayers. Yet, in medical imaging applications, this suppression has led to the premature loss of over 39 million lives<\/strong> that could have been saved\u2014 a loss that far exceeds any financial measure<\/strong> (see article https:\/\/bit.ly\/3AbUghP<\/a>).<\/p>\n

<\/a><\/a>4. \u00a0\u00a0 Historical Evidence of CERN FPGA-Based Trigger Ineffectiveness<\/h1>\n

For decades, CERN’s FPGA-Based Level-1 Triggers<\/strong> have proven ineffective, leading to multiple redesigns that failed due to a flawed choice of architecture<\/strong>. This architecture allowed for fewer than 40 programmable operations, rendering it unsuitable for Level-1 Triggers. FPGA is also unsuitable for High-Level Triggers (HLT)<\/strong> because commercial computers offer higher computing power at a lower cost with operating systems and application programs easier to use than FPGA\u2019s simulators and development systems\u2019 tools.<\/p>\n

While FPGAs are excellent devices for many other applications, their use for Level-1 Trigger is fundamentally flawed in this context.<\/strong><\/p>\n

This inefficiency is confirmed by experimental results<\/strong>. On July 4, 2012, only 40 Higgs boson-like events<\/strong> were announced. These were found by chance from 1,000 trillion collisions<\/strong> generated over two years, whereas statistical analysis suggests over 100,000 Higgs bosons<\/strong> should have been produced.<\/p>\n

For the future HL-LHC, CERN has approved the Phase 2 upgrades of the ATLAS and CMS experiments. These are currently under construction, and funds have been secured for the entire project, as announced by the CERN Council on June 20, 2025.<\/p>\n

However, funding agencies must be informed<\/strong> about the scientific inconsistencies inherent in CERN\u2019s FPGA-Based Level-1 Triggers. With the same volume of 1,000 trillion collisions<\/strong>, the HL-LHC in 2029 is expected to yield over 750,000 Higgs bosons<\/strong>. Filtering this immense volume of data requires a Level-1 Trigger capable of executing thousands of programmable operations<\/strong> to filter noise more effectively and resolve pile-up events; the current 66 operations are utterly inadequate<\/strong>.<\/p>\n

For example, CERN-CMS\u2019s own technical report<\/strong> (Table 3.2, page 46 of [4]) reveals that the CMS FPGA-Based Level-1 Trigger for 2026-2036 experiments will consist of 20 trillion transistors<\/strong>, consume 650 kW<\/strong>, and cost several million euros, yet it is limited to approximately 66 programmable operations<\/strong>. This limitation, which is inadequate to filter over 99% of the data,<\/strong> can be proven by conceptually analyzing FPGA architecture and by using simulation tools from Xilinx, the FPGA manufacturer.<\/p>\n

<\/a><\/a>5. \u00a0\u00a0 The Stark Contrast: Crosetto\u2019s 3D-Flow Invention<\/h1>\n

By Contrast, Crosetto\u2019s 3D-Flow invention offers a solution that could save taxpayers billions on future CERN projects<\/strong> and save millions of lives from premature cancer deaths<\/strong>.<\/p>\n

Recognized as a breakthrough in 1993<\/strong> by a major scientific review at Fermilab [1] for its ability to accurately recognize objects (particles) traveling at the speed of light (photons). <\/em>Crosetto’s 3D-Flow received initial funding and a subsequent $1 million grant<\/strong> for a successful feasibility study, detailed in a 1999 peer-reviewed publication in Nuclear Instruments and Methods in Physics Research (NIM)<\/strong> [2]. The 3D-Flow architecture has broad applications:<\/p>\n