A fresh approach to a stubborn quantum problem is reshaping how the world’s biggest particle experiment plans its future. A grad student’s persistence, fused with machine learning that directly targets likelihoods rather than simple classifications, is delivering sharper measurements in the presence of quantum interference. The result is not only a clearer glimpse into the Higgs boson’s behavior but also a rebirth of how ATLAS, one of the two main LHC collaborations, reasons about uncertainty and precision. This shift is already influencing how the collaboration plans its next steps, and it promises to recalibrate expectations for the long-term trajectory of Higgs and beyond.
The challenge of interpreting LHC data in the presence of quantum interference
The Large Hadron Collider is among humanity’s most ambitious enterprises in experimental science, designed to create and scrutinize fundamental particles by smashing protons at near-light speeds. Yet the data it returns are not straightforward images that can be interpreted with the same ease as a photograph. The results are intricate patterns born from a web of possible processes that can lead to the same final state. In practice, this means physicists must rely on careful statistical modeling to extract meaningful physics—especially when quantum interference is at play.
Interference is a central consequence of quantum mechanics: different possible histories for a collision can combine, enhancing or suppressing the probability of particular outcomes. This phenomenon makes the data richer but also more challenging to interpret. When interference is strong, the signal of a desired process may not simply stand out as a clean, isolated peak; instead, parts of the signal can be suppressed or altered in subtle ways. The conventional approach has been to separate events into signal and background categories, train a machine learning model to distinguish those categories, and then extract a parameter of interest by looking at the distribution of events. But when interference causes some events to disappear or morph in unexpected ways, a classifier trained on a straightforward signal-versus-background paradigm can mislead the inference, inflating uncertainties or biasing measurements.
This is not a hypothetical problem in the Higgs sector. In particular, one pathway of interest involves the production of a Higgs boson via a fusion of two W bosons from the colliding protons. The Higgs can then decay into a pair of Z bosons, which subsequently decay into leptons. The same final-state configuration—two Z bosons appearing as a pair of leptons—can also arise without an intermediate Higgs: a background process that mimics the same final state. Crucially, the two formation histories interfere with each other, so simply counting events with a certain final state does not cleanly separate signal from background. This interference can increase or decrease the observed rates depending on the quantum amplitudes involved, and thus the extraction of the Higgs decay properties becomes highly nontrivial.
Statistically, these effects urge physicists to go beyond standard classification—toward methods that can account for how the observed data arise from a combination of competing amplitudes. The problem is compounded by the scale of ATLAS and the sheer volume of data: thousands of physicists and many years of data accumulation, with the need to quantify uncertainties with a precision that matches the experimental prowess. The standard toolset—simulations, templates, and likelihood-based inference—must be carefully adapted to accommodate interference without sacrificing reliability. In short, the interference phenomenon sits at the heart of a delicate balance: it grants deeper access to the underlying physics if we can extract the information without letting the noise of quantum complexity overwhelm the signal.
In this landscape, a young researcher named Aishik Ghosh identified a fundamental mismatch between how most analyses approached the problem and what the data could reliably tell us when interference was significant. The conventional approach, predicated on classifying events into signal and background, tended to underutilize the information encoded in the data when interference was strong. The outcome was that researchers often relied on methods that treated the intermediate physics as if it existed in isolation, a simplification that could misrepresent the true likelihood of different decay scenarios. The challenge, then, was to find a framework that would embrace the interference—rather than circumvent it—while still delivering precise, trustworthy measurements.
Neural Simulation-Based Inference: a new tool for physics
The breakthrough came in the form of a machine learning approach with a different philosophy. Neural Simulation-Based Inference, or NSBI, does not train a classifier to separate signal from background. Instead, it teaches a neural network to infer a likelihood ratio directly from simulated data. The idea is to build a flexible model that can compare competing hypotheses—such as different Higgs decay rates—by examining how often each hypothesis would generate the observed data under a range of simulated scenarios. In practice, researchers run many simulations that reflect various possible parameter values, then teach the neural network to predict the likelihood of each scenario given the actual set of observed events.
This approach has a crucial advantage when interference is present. Rather than forcing a binary distinction between signal and background, NSBI leverages all aspects of the simulated data to estimate how probable a given set of observations is under each hypothesis. The likelihood ratio—the ratio of probabilities under two competing hypotheses—becomes the central quantity guiding inference. If the neural network can approximate this ratio with high fidelity, researchers can infer a parameter such as the Higgs decay time or width with a precision that respects the full quantum mechanical structure of the processes involved.
Importantly, NSBI bypasses a common pitfall of earlier machine-learning strategies. Traditional ML in physics often relies on classification accuracy, which can degrade when the data do not cleanly separate into exclusive categories due to interference. NSBI instead targets the inference problem head-on: it seeks to determine the parameter values that render the observed data most probable, given a realistic model of the detector, the physics, and the interference effects at play. By focusing on likelihoods rather than labels, NSBI provides a more faithful statistical representation of what the data are telling us.
The ATLAS collaboration recognized the conceptual potential of NSBI, and two papers released in the following December spelled out the method and its empirical validation. The first paper described how to implement Neural Simulation-Based Inference to maximize the information content of particle-physics data in the presence of complex quantum interference. The second paper tested the method by reanalyzing a previous ATLAS result with the NSBI framework and demonstrated a dramatic improvement in precision. These papers did not merely propose a theoretical idea; they provided a concrete, computable pathway to extract robust physics in the face of interference. The results signaled a potential paradigm shift: if NSBI could scale to the complexity of real collider data, it would empower measurements that were previously limited by how uncertainty and interference were treated.
The NSBI approach thus represents more than a technical trick. It embodies a philosophical shift in how to handle inference in high-energy physics: move away from trying to separate mutually entangled processes into tidy categories and toward a framework that directly models the joint distribution of all relevant variables under different hypotheses. In doing so, NSBI can exploit the full structure of the data produced by the collision, including correlations among observables that arise from interference patterns. The method’s strength lies in its capacity to use simulations—under a suite of plausible physical scenarios—to teach the network how the data would look if a particular decay rate or quantum amplitude were true. The network then uses that knowledge to judge which scenario the real data most likely came from.
The promise of NSBI, as suggested by the ATLAS papers, lies in its potential to increase precision without sacrificing reliability. In regimes where interference blurs the lines between signal and background, a method that operates on likelihoods rather than labels can preserve sensitivity to the underlying physics that would otherwise be smeared out by conventional classification. The initial demonstrations indicated that NSBI could be calibrated to deliver trustworthy uncertainty assessments, provided that a careful validation program was in place. The emphasis was not solely on achieving a sharper central value; it was on ensuring that the reported uncertainties faithfully reflect what is known and what remains unknown about the data and the models used to generate them.
NSBI’s conceptual shift is complemented by pragmatic engineering: building neural networks that can handle the scale and complexity of collider data, integrating them with existing simulation pipelines, and validating their performance across a range of scenarios. The approach is data-driven but anchored in physical theory and detector response. It requires meticulous attention to calibration, uncertainty quantification, and cross-checks. The ATLAS researchers who championed NSBI had to confront questions about the trustworthiness of neural likelihoods in a high-stakes scientific environment, where miscalibration could propagate into biased measurements. The outcome would hinge on whether NSBI could deliver increased precision while maintaining the rigorous standards demanded by a collaboration of thousands of scientists.
The NSBI framework also opens doors to addressing questions that were previously difficult to quantify. Interference effects can be subtle and highly sensitive to the details of the model—such as the phases and amplitudes associated with different processes, or the precise way in which particles interact and decay. NSBI provides a path to incorporate these subtleties into the inference process in a principled way. By calibrating the likelihoods across a spectrum of plausible physical scenarios, researchers can map out how sensitive their measurements are to various assumptions. In this sense, NSBI is not simply a data-analytic trick; it is a way to encode the physics more faithfully into the statistical machinery that ultimately drives the measurement results.
From concept to collaboration: the path of Aishik Ghosh and the NSBI team
The journey from a promising idea to a working methodology within a leading collider experiment is rarely a straight line. It involves not only technical innovation but also a long, patient process of building trust within a large collaboration whose members span multiple institutions and countries. For Aishik Ghosh, the project began as a task to improve a specific pathway used to glean Higgs properties. The goal was to detect a single path in isolation, a goal that turned out to be at odds with the physics of interference. Ghosh’s early work showed a need to rethink the objective of the analysis.
As a graduate student entering the ATLAS collaboration—a formidable ecosystem of about 3,500 physicists—Ghosh found himself stepping into a complex machine. The collaboration’s infrastructure is designed to optimize the collective effort: rigorous review processes, cross-checks, and extensive internal validation are necessary before any new technique can be deployed on real data. In that environment, a small, independent advance must demonstrate its value across a broad spectrum of checks. The skepticism that accompanies any novel methodology in such a setting is not a flaw but a precaution: it ensures that the method will be robust when applied to the wealth of data and the variety of analyses ATLAS conducts.
Ghosh’s initial experiments with NSBI yielded promising results on toy tests and simulated data, but the real test lay in deploying the method on experimental data in a way that could be trusted by the broader collaboration. The question was not merely whether NSBI could, in principle, infer a Higgs-related parameter more precisely; it was whether the method could be integrated into the ATLAS software stack, validated under the collaboration’s uncertainty framework, and demonstrated to pass every necessary cross-check. This required a team, time, and institutional support.
The project’s leadership recognized that to move NSBI from concept to impact, a broad team would be essential. Ghosh assembled a cohort that included Arnaud Maury, who worked on calibrating the machine’s confidence in its answers; Rafael Coelho Lopes de Sa, a professor at the University of Massachusetts, who brought methodological depth; and Jay Sandesara, a student, who played a crucial role in executing the full-scale calculation on a computing cluster. The collaboration also benefited from the guidance of IJCLab emeritus RD Schaffer and University of Liège professor Gilles Loupe, who offered cross-checks and strategic advice. The team’s aim was to deliver a transparent demonstration that their NSBI method could perform on real data and meet the scientific bar required by ATLAS.
An unusual but pivotal step defined the project’s early phase: rather than limiting themselves to synthetic data or simulated scenarios, the team took data that ATLAS had already analyzed and performed a full reanalysis using the NSBI framework. This approach was designed to be a stringent test: if their method could replicate and surpass the quality of a prior ATLAS analysis, and do so with robust internal checks, it would provide a compelling case for adoption within the collaboration. The two papers that ultimately emerged from the effort—one detailing the NSBI method itself and the other presenting the results of the upgraded analysis—served as the dual proof of concept: the theory and the practice, together, in a form that the ATLAS community could scrutinize.
The publishing path was navigated with care. Zach Marshall, who at the time served as the ATLAS computing coordinator, played a critical role in guiding the papers through the collaboration’s vetting process. His involvement highlighted a broader truth about large collaborations: the success of a new approach depends on bridging the gap between technical specialists and physics analysts. The “overlap group”—a small subset of the community with deep familiarity of both the computational and physics analysis layers—proved essential for ensuring that the NSBI papers were clear, intelligible, and genuinely useful to other scientists. This careful, collaborative vetting was not just about correctness; it was about building trust and acceptance inside a vast organization that relies on consensus for implementing new tools in everyday research.
The new method’s reception within ATLAS was not automatic. It required rigorous evaluation of uncertainties, a task that is especially challenging for neural networks applied to high-energy physics. The team recognized early on that quantifying how well the neural likelihoods are estimated—across all corner cases and potential failure modes—was as important as achieving better central values. Any underestimated uncertainty could bias results, undermining the credibility that a measurement must uphold when tested against the full rigor of collider physics. Thus, the project emphasized a disciplined program of calibration, cross-checks, and independent validation to avoid the pitfalls of overconfidence in machine-driven inferences.
The outcome of this concerted effort was a demonstration of a method that produced a substantially improved precision in the measured quantities, compared with the collaboration’s previous analyses. The NSBI approach did not merely shave a fraction off the error bars; in some aspects, it delivered a meaningful rebalancing of how well the data could constrain the physics parameter under study. The evidence from the reanalysis strongly suggested that the method’s benefits extended beyond a single measurement, offering a more powerful framework for tackling quantum interference across a range of Higgs-related analyses and potentially other areas of particle physics where interference complicates the inference landscape.
Crucially, the NSBI story is also about a culture shift within large collaborations. The project embodied a willingness to take intellectual risks, paired with a rigorous, methodical approach to verification. It showed that a single, well-supported idea—when embedded in a collaborative structure that values cross-disciplinary input and thorough validation—could alter the trajectory of a major experiment’s future plans. In this way, the NSBI effort not only introduced a powerful statistical tool but also demonstrated a model for how experimental physics can evolve in the era of data-driven science, where the synergy between advanced computation and fundamental theory can unlock new levels of precision.
The team’s achievements were not wrapped in a single breakthrough moment but in a sustained, multi-year program of development, testing, and validation. The narrative includes the initial conceptual insight, the construction of the NSBI framework, the iterative process of calibrating and validating the network’s likelihood predictions, and the careful execution of a full reanalysis to produce demonstrable results. The project’s success depended on the willingness of a large collaboration to step beyond traditional methods and embrace a new paradigm—one that leverages neural networks to infer probabilities in a way that respects quantum interference and the full complexity of collider data.
Interference at the heart of the Higgs hunted: a quantum histories view
To truly appreciate the significance of NSBI, it helps to ground the discussion in a concrete picture of how interference arises in the processes ATLAS studies. In the simplified but informative language of quantum field theory, multiple histories can lead to the same final state. The double-slit analogy often used in quantum physics provides a helpful bridge to intuition: when an electron travels from a source to a detector, there are multiple possible paths it could take. Each path contributes a complex amplitude, and the superposition of these amplitudes yields a probability distribution that cannot be understood by considering a single path alone. The classic two-slit interference pattern—bright and dim fringes on a screen—emerges because of the phase relationships between the two paths. If one path is blocked or never considered, the interference pattern changes dramatically.
In the proton-proton collisions at the LHC, the histories are less tangible than a particle passing through two slits, but the conceptual structure is analogous. For a given final state, such as two Z bosons that subsequently decay to leptons, there are multiple production mechanisms that can lead to that end state. Imagine two W bosons from the initial protons fusing into a Higgs boson, which then decays into two Z bosons. In another history, the same final state could arise if the two W bosons fuse and directly yield two Z bosons without ever forming a Higgs intermediate state. Both histories share the same initial conditions and the same final observed outcomes, but they differ in their intermediate steps and amplitudes. In quantum theory, these histories do not simply add their probabilities; they add the amplitudes and then square the sum. The interference between these histories can either boost or suppress the observed rate of events, depending on the relative phases and magnitudes of the contributing amplitudes.
This interference has a profound consequence for measurement: the observable signal attributed to the Higgs boson decays is not a clean, isolated peak but a pattern shaped by the interplay of amplitudes. The attempt to identify a Higgs-mediated path using a static classifier faces a fundamental problem. If interference causes certain event configurations to disappear or become less likely, a model trained to recognize a Higgs-decay pattern in isolation may misread the data—either by overestimating the signal or by mischaracterizing the background. In practice, this means the classification approach might misrepresent the likelihood of a given decay scenario, leading to biased inferences about the Higgs properties, such as its lifetime or interaction strengths with other particles.
The neural simulation-based approach reframes the problem by asking: given the observed data, which hypotheses about the underlying physics are most consistent with the entire distribution of events produced by the collision? The method builds a bridge between simulation and inference by using neural networks to approximate a likelihood ratio that compares competing hypotheses. Rather than predicting whether a single event is signal or background, NSBI aims to quantify the relative plausibility of different underlying physical mechanisms that could have produced the observed data. This framework is especially well suited to interference, because it leverages how the joint distribution of all measured observables depends on the combination of amplitudes that contribute to the process, including their interference terms.
A key element in this philosophy is the concept of a likelihood ratio, a fundamental tool in statistical inference. The likelihood ratio compares how likely the observed data are under two different hypotheses—for example, a Higgs-mediated production pathway versus a background-only scenario or a Higgs with a different decay rate. NSBI uses simulations that generate data under a spectrum of parameter values and then trains a neural network to predict the ratio of probabilities under these competing hypotheses. If the neural network can accurately predict this ratio across the relevant parameter space, it becomes a powerful instrument for estimating the most probable decay rate or other quantities of interest given the measured data. The approach thus centralizes a core statistical object—the likelihood ratio—while embedding it in a flexible, data-driven learning framework.
The scientific payoff is not only a sharper central estimate but also a more honest accounting of uncertainties. Because the method uses a principled likelihood framework and is validated against a rigorous set of tests, the estimation of uncertainties can reflect the real sensitivity of the data to the parameters of interest, including the contributions from interference. The risk of bias, a perennial concern when inserting complex machine learning into high-stakes measurements, is mitigated by a comprehensive program of calibration, cross-checks, and independent validation. By confronting the uncertainties head-on, NSBI provides a more complete and credible statistical interpretation of the measurement, strengthening the overall reliability of the ATLAS results.
From a physics perspective, NSBI also holds the promise of expanding the reach of Higgs measurements into regimes where traditional approaches struggle. The precision with which the Higgs boson can be studied—its couplings, width, and potential connections to new, heavy particles—depends in part on our ability to disentangle signal from background in the presence of interference. The NSBI framework provides a way to extract maximum information from the data by leveraging how different hypotheses shape the entire distribution of observables, not just a subset of events that appear to be cleanly signal-like. In principle, this could translate into sharper tests of the Standard Model and more sensitive probes for new physics that influence the Higgs sector or its interactions with other fields.
Moreover, the NSBI approach is not limited to a single measurement. Its generality means it could be adapted to other processes where interference plays a crucial role, and where the observable final states do not provide a straightforward partition into signal and background. The broader scientific value lies in introducing a robust, likelihood-based paradigm to collider data analysis that remains faithful to the physics of the processes involved. In a field where the interplay between theory, simulation, and experiment is already intricate, NSBI adds a principled, scalable tool to the physicist’s analytical arsenal.
The practical demonstration: a full reanalysis and improved precision
The ATLAS collaboration’s two NSBI papers did not exist in a vacuum. They emerged from a deliberate effort to translate a compelling idea into a tangible, rigorous demonstration that the physics community within ATLAS could scrutinize and ultimately adopt. The first paper laid out the methodological blueprint: how NSBI operates, how the neural networks are trained using simulated data, how the likelihood ratio is inferred, and how uncertainties are quantified. The second paper then took the method to the test by reanalyzing a prior result with the NSBI framework and comparing the outcomes to the standard analysis. The comparison was not merely about getting a slightly different number; it was about verifying that the NSBI approach reduces uncertainties in a controlled, well-understood manner and that it yields consistent conclusions across a cross-section of checks.
A central challenge in validating a new analysis technique of this kind is uncertainty quantification. In high-energy physics, the credibility of a measurement hinges on accurately stating what you know and what you do not know. Neural networks, with their notorious reputation for “black box” behavior, must be calibrated and audited in ways that meet the scientific community’s standards. The team anticipated this by constructing an explicit program to test the performance of the NSBI network under a variety of hypothetical scenarios, including edge cases where the interference might push the data into regions of parameter space with lower prior probabilities. They also needed to ensure that the network’s predictions were not unduly biased by aspects of the simulation, detector modeling, or the particular training regimen. The validation plan was designed to be comprehensive and transparent, with multiple cross-checks conducted by team members who were not directly involved in the initial development, and with external reviewers within ATLAS providing independent assessments.
The results of the NSBI reanalysis were compelling: the method delivered significantly improved precision compared with the collaboration’s previous approach. The gains were not just modest; they manifested in a more sharply constrained measurement of the physics quantity in question, with an uncertainty that better reflected the information content of the data when interference was present. The improvements translated into a stronger capacity to test the properties of the Higgs boson and to search for signatures of new, heavy particles that might couple to the Higgs sector. The quantitative improvements were described with the necessary care to ensure that the new method’s uncertainties remained credible under scrutiny. The demonstration was not a one-off flourish; it served as a convincing proof that NSBI could enhance the fidelity and reach of ATLAS analyses going forward.
Because the demonstration hinged on reanalyzing existing ATLAS data, the team faced the crucial task of maintaining a track record of reproducibility and reliability. The approach bridged an empirical check against real data with a forward-looking preparation for broader deployment. Demonstrating compatibility with the collaboration’s data quality standards, computing environments, and analysis workflows was essential. The successful acceptance of NSBI for broader use within ATLAS rested not only on the demonstrated precision gains but also on the method’s transparency, its resilience to potential biases, and its amenability to cross-checks by other researchers in the collaboration. The process showcased a model for how experimental techniques can evolve—through careful validation, open collaboration, and strategic demonstrations that tackle both the physics and the governance of large scientific projects.
The broader implication of this practical demonstration was clear: NSBI offered ATLAS a path to unlock more information from the same datasets, enabling more stringent tests of the Higgs mechanism and its potential connections to beyond-Standard-Model physics. The method’s capacity to handle interference without collapsing under its own complexity suggested a future in which precision measurements can be pursued with a higher degree of confidence. This potential did not go unnoticed by those responsible for planning ATLAS’s research program. If NSBI could be scaled and validated across a wider set of processes, it could redefine the baseline for how uncertainties are estimated and how analyses are designed to maximize information extraction. The demonstration thus acted as a catalyst, encouraging a more expansive, long-term exploration of NSBI-adjacent approaches and the integration of advanced statistical methods within the experimental workflow.
Validation, uncertainties, and building trust within ATLAS
A central theme of the NSBI effort was establishing trust in a method that relies on neural networks to perform inference on complex, real-world data. Trust, in this context, meant more than just a single successful reanalysis. It required a thorough, multi-faceted validation program designed to demonstrate that the method would behave robustly across different data samples, physics channels, and detector conditions. The ATLAS collaboration’s governance and review mechanisms demand that any new technique that could influence published measurements be capable of withstanding an array of scrutiny, including sensitivity to methodological choices and potential biases that could arise in corner cases.
One practical concern with neural networks, particularly in physics analyses that hinge on precise uncertainty estimates, is calibration. A neural model that predicts a likelihood or a ratio of probabilities must be calibrated correctly to ensure that the reported intervals reflect the true statistical coverage. If not, the confidence intervals could be too optimistic or too conservative, leading to biased conclusions about the physics parameters of interest. The NSBI team addressed this head-on by designing calibration procedures and performing validation exercises that probed the method’s performance under diverse conditions. These checks included tests with synthetic data designed to stress the model, as well as cross-validation against independent simulation samples. The goal was to demonstrate that the NSBI approach remains faithful to the underlying physics even when confronted with uncertainties in the modeling, detector response, or the simulation pipeline.
Another layer of validation centered on uncertainty decomposition. In traditional analyses, many systematic uncertainties are propagated through the estimation of the final result via analytical or semi-empirical methods. The NSBI framework requires a careful account of how various sources of uncertainty—such as modeling of the detector, the physics processes, or the numerical approximations in the simulation—propagate through the neural network’s inference. The team thus invested effort into mapping out the sensitivity of the likelihood predictions to these inputs, quantifying how mis-modeling might affect the inference, and ensuring that the reported uncertainties faithfully reflect the potential variation in the data. This kind of sensitivity analysis is crucial for building the trust that the broader ATLAS community needs to adopt the method in routine analyses.
Communication within a collaboration of thousands matters as much as technical validity. The process of engaging stakeholders across different groups, from hardware-focused computing teams to physics analysts, required clear, reproducible documentation of the NSBI workflow. The team developed transparent benchmarks, code repositories, and validation reports that allowed other ATLAS members to review, reproduce, and challenge the results. This transparency is essential not only for internal confidence but also for the external science community that relies on the credibility of high-energy physics measurements. The NSBI program stood as an example of how advanced statistical techniques can be responsibly integrated into a field that demands high standards of reproducibility and openness.
The governance process also highlighted a broader cultural dimension. For many scientists, embracing a new method means stepping beyond the familiar territory of well-worn analysis pipelines. The NSBI story demonstrated that progress is possible when a collaboration fosters an environment where risk-taking is balanced with disciplined validation and a readiness to learn from both successes and failures. The attitude reflected in this journey—curiosity tempered by rigorous testing, readiness to revise established practices, and openness to new computational paradigms—signals a potential shift in how scientific methodologies evolve within large collaborations.
Finally, the human element in this narrative cannot be overstated. The collaboration’s willingness to invest in a multi-year, cross-disciplinary project rested on a clear recognition that the payoff could be substantial: not only a sharper measurement for Higgs physics but also a new blueprint for how the field approaches data interpretation in the era of machine learning. The NSBI project therefore stands as a testament to the value of patient, collaborative engineering in science—where the core physics questions drive technical innovation, and the new tools, in turn, illuminate the physics with greater clarity.
Demonstrations: two papers, improved precision, and a shift in planning
The output of the NSBI initiative materialized in the form of two pivotal papers that captured both the method and its empirical impact. The first paper served as a foundational blueprint, detailing the Neural Simulation-Based Inference approach, the architecture of the neural networks, the simulation strategy, the construction of the likelihood ratio, and the calibration and validation procedures necessary to establish trust within the collaboration. The second paper turned the method into a tangible result by applying it to reanalyze existing ATLAS data and comparing the outcome to the collaboration’s published analyses obtained through conventional methods. The comparison was not a mere check of numerical agreement; it demonstrated the practical advantages of NSBI in a real-world setting, including a demonstrably improved precision. The strengthened constraints on the parameter under study offered concrete evidence that NSBI could extract more information from the same data, leveraging interference rather than being impeded by it.
A crucial factor in the acceptance of NSBI within ATLAS was the demonstration’s credibility across both statistical logic and scientific skepticism. The team’s approach to uncertainty quantification and calibration aligned with the community’s expectations for a credible measurement, helping to reassure skeptics who might worry about the reliability of a neural-based likelihood approach. The demonstration’s success did not only lie in producing a better estimate; it lay in proving that a fundamentally different way of thinking about inference could be trusted, reproducible, and compatible with the collaboration’s rigorous standards.
The practical implications for ATLAS’s future plans were immediate and tangible. The improved precision achieved with NSBI translated into more stringent tests of the Standard Model predictions concerning the Higgs sector and its couplings. It also opened the door to a more sensitive exploration of possible new particles that could interact with the Higgs mechanism or affect its decay pathways. In other words, NSBI didn’t just improve the measurement of a single quantity; it altered the experimental calculus for long-term planning. When ATLAS projected how precisely they would be able to test various Higgs properties in the next generation of data collection and analysis, those priors had to be revisited in light of the NSBI results. The collaboration found itself rethinking its long-range projections in some cases, because the new method had already achieved levels of precision that were ahead of where their then-current planning had anticipated for a number of years into the future.
The reflections of key project participants underscored the sense of momentum. Zach Marshall, the computing coordinator, emphasized the importance of engaging the right slice of the community—those who could articulate both the technical details and the physics implications—to ensure that the NSBI work would be understood, vetted, and made useful for the wider ATLAS audience. His perspective highlighted the reality that the success of such a method depends not only on its statistical merits but also on effective communication and inclusive collaboration. The view from within the program was that NSBI represented not just a technical advancement but a strategic opportunity: to recalibrate how the collaboration frames its capabilities and its expectations for precision in a future where machine learning is integrated more deeply into the analytic workflow.
The net effect on ATLAS’s strategic direction was clear. The NSBI method offered a path to achieve higher precision earlier than previously anticipated, given sufficient computational resources and validation effort. This acceleration in expected performance has implications for proposals, data-taking strategies, and the prioritization of analyses that benefit most from improved inference when interference is a dominant factor. The collaboration’s leadership recognized the potential that NSBI brings to not only Higgs physics but also to a broader spectrum of measurements across the detector’s capabilities. In short, the NSBI demonstration catalyzed a rethinking of future work, by providing a concrete mechanism to squeeze more information from existing data and to push the boundaries of what precision is realistically achievable in the next decade of collider experiments.
The broader physics community also took note of ATLAS’s progress with NSBI. The achievement provided a compelling case study for how modern inference techniques can be deployed responsibly in high-stakes experimental physics, balancing ambition with rigorous validation. It showcased a model of collaboration in which a relatively small, tightly connected team could develop a transformative approach and then scale its impact to the entire experiment through careful cross-checks, transparent documentation, and sustained engagement with the broader community. The narrative resonated beyond ATLAS, offering a blueprint for how other experiments confronting interference or similar statistical challenges might adopt NSBI-inspired strategies to improve their measurements and to unlock new physics possibilities that previously seemed out of reach.
Looking ahead: how NSBI reshapes future plans and the search for new physics
The practical success of NSBI for ATLAS has immediate implications for how the collaboration envisions its future research agenda. First, the improved precision in key measurements translates into tighter constraints on Higgs boson properties, including its couplings and potential interactions with new particles. This sharpening of the experimental handle enables more stringent tests of the Standard Model’s predictions in the Higgs sector and provides a more sensitive probe for deviations that could signal new physics. The ability to test these ideas with greater fidelity strengthens the case for dedicating computational and methodological resources to NSBI-capable analyses, as the payoff in scientific insight is tangible.
Second, the demonstrated ability to handle interference with high fidelity broadens the set of processes that ATLAS can examine with confidence. Interference is not an exotic corner case; it is a pervasive feature of particle interactions, especially in the Higgs and electroweak sectors. NSBI’s general approach can be adapted to other channels where interference plays a crucial role, enabling a whole family of analyses to adopt a similar strategy for inference. The methodological flexibility means that physicists can tackle a wider array of questions with the same underlying framework, potentially increasing the breadth and depth of ATLAS’s physics program.
Third, the NSBI program has implications for how uncertainties are perceived and communicated within the collaboration. The emphasis on calibration, cross-checks, and transparent validation sets a high standard for quantifying and reporting uncertainties in neural-network-based inferences. This posture aligns with the broader scientific goal of ensuring that reported results faithfully reflect what is known, as well as what remains uncertain, about the physics and the measurement process. As future analyses increasingly rely on sophisticated statistical tools, the experience gained through NSBI’s validation efforts will inform best practices for credible uncertainty quantification and robust inference in collider experiments.
Fourth, the NSBI experience is likely to influence how ATLAS and similar collaborations approach the integration of machine learning into the physics workflow. The success story demonstrates that carefully governed, well-validated ML methods can offer meaningful gains without compromising the scientific standards of the field. It suggests a future in which ML-based inference tools become core components of the analysis toolkit, with standardized pathways for validation, documentation, and governance. This cultural shift could accelerate the adoption of advanced computational techniques across the spectrum of collider physics analyses, enabling researchers to push the boundaries of precision and discovery.
Finally, the NSBI narrative contributes to a broader dialogue about the relationship between theory, computation, and experiment in modern physics. It underscores the importance of close collaboration among theorists, experimentalists, and data scientists to design methods that faithfully reflect underlying physics while exploiting the full power of modern computation. The result is not merely a more precise measurement; it is a demonstration of how interdisciplinary teamwork and methodological innovation can expand the scientific frontier. As ATLAS and other experiments continue to collect data and refine their analyses, NSBI stands as a promising cornerstone of the methodological evolution that will shape high-energy physics for years to come.
A broader impact on particle physics and scientific culture
Beyond the immediate gains in a single measurement, the NSBI story signals a broader transformation in how large-scale experiments approach data analysis in the age of artificial intelligence. By embracing a framework that directly targets likelihoods and leverages neural networks to navigate the complexities of interference, the physics community gains a tool that is both powerful and principled. This is a meaningful departure from conventional workflows that emphasize classification-based training and template fitting, particularly in contexts where interference can blur the lines between signal and background in subtle, difficult-to-predict ways.
One consequence of this shift is a potential ripple effect across other experiments and disciplines within particle physics. If NSBI can be successfully generalized and validated across a range of processes and channels, it may become a standard option for analyses where the final states are ambiguous or where interference effects play a dominant role in shaping the observed distributions. The method’s generality and its emphasis on likelihood-based inference make it attractive to a broader community of researchers who grapple with similar statistical challenges in the interpretation of complex datasets.
The NSBI story also highlights the importance of cultivating a research culture that supports ambitious, long-term projects in a field where many researchers move on to short-term postdoc appointments or rapid publication cycles. The willingness to invest in a multi-year effort, to recruit a multidisciplinary team, and to build the validation and governance infrastructure necessary for broad adoption reflects a commitment to scientific depth and reliability. It demonstrates that big science benefits from a willingness to experiment with new ideas, provided there is a robust scaffolding of checks, documentation, and collaborative oversight.
Moreover, the narrative underscores the value of inclusive collaboration in advancing scientific knowledge. The NSBI project thrived because individuals with complementary expertise—experimentalists, theorists, computational scientists, and engineers—came together with a shared goal to improve inference in the presence of quantum interference. The governance model that allowed such cross-cutting collaboration to flourish—while maintaining the integrity of the broader experimental program—offers a blueprint for how other large scientific endeavors might structure themselves to welcome innovative approaches that enhance discovery potential.
From a communications perspective, the NSBI case presents a compelling story of how modern science negotiates risk, uncertainty, and novelty with the public and with the scientific community. The ability to explain a dense statistical methodology to a broad audience—while still conveying the core physics and the significance of the results—becomes an increasingly valuable skill. The narrative surrounding NSBI demonstrates that the future of scientific progress rests not only on technical breakthroughs but also on how effectively researchers can articulate their ideas, validate them, and integrate them into the shared enterprise of scientific knowledge.
The broader significance thus extends beyond the confines of a single collaboration. It captures a trend in which the fusion of physics, statistics, and machine learning is redefining what is possible in experimental science. The NSBI experience is a case study in how to harness the strengths of diverse disciplines to tackle complex problems, all while preserving the rigor and transparency that are the hallmarks of reliable science. As the field moves forward, the questions will increasingly revolve around how to scale, generalize, and govern these powerful methods so that their benefits can be realized across experiments, channels, and institutions.
Conclusion
A young researcher’s innovative use of Neural Simulation-Based Inference has unlocked a more complete and trustworthy way to extract information from LHC data in the presence of quantum interference. By reframing the problem from signal-versus-background classification to likelihood-based inference, NSBI preserves the underlying physics and leverages the full richness of collider data. The ATLAS collaboration’s two foundational papers demonstrated that the method could be trained, validated, and applied to real data, resulting in substantially improved precision and a tangible impact on future planning. The success of this program has already begun to reshape how ATLAS thinks about uncertainty, planning, and the broader adoption of machine learning in high-energy physics.
The NSBI program stands as a testament to the power of patient collaboration, rigorous validation, and bold methodological innovation. It illustrates how a focused, multi-year effort can alter the trajectory of a major experiment, enabling more precise tests of fundamental theories and expanding the horizon for discovering new physics. As ATLAS and the wider physics community continue to explore the Higgs sector, search for new particles, and push the boundaries of our understanding of quantum interference, NSBI provides a robust, scalable framework that will likely play a central role in shaping the science of the coming era.
