CMS found no evidence of microscopic black holes, string balls, or electroweak sphalerons in 138 fb⁻¹ of 13 TeV proton proton data. The search used a phase space distance method and a support vector machine classifier to isolate unusually energetic, high multiplicity event patterns from Standard Model backgrounds.
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Create a landscape editorial hero image for this Studio Global article: How did a CMS analysis of 138 fb⁻¹ of 2016–2018 LHC proton-proton collision data use a novel phase-space-distance machine-learning method to. Article summary: CMS found no statistically significant evidence for microscopic black holes, string balls, or electroweak sphalerons in 138 fb⁻¹ of 13 TeV proton-proton data from 2016–2018. It therefore strengthened model-dependent cons. Topic tags: general, education, government, academic, general web. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermark
CMS examined the full 2016–2018 Run 2 dataset—138 fb⁻¹ of proton-proton collisions at 13 TeV—and found no statistically significant sign of microscopic black holes, string balls, or electroweak sphalerons. The result narrows the viable parameter space of specific TeV-scale gravity and large-extra-dimension models, but it does not rule out extra dimensions or resolve the hierarchy problem. 3
4
The targeted search focused on collision events with distinctive kinematics: many energetic particles distributed relatively isotropically. Such a topology could arise if a microscopic black hole formed and decayed essentially immediately into Standard Model particles; it is also relevant to benchmark electroweak-sphaleron signals.
CMS introduced a method based on phase-space distance—a measure of how close two events are when their reconstructed kinematic properties are represented in a multidimensional event space. This let the analysis organize events by kinematic similarity rather than relying only on a single variable. A support-vector-machine classifier then learned a boundary between simulated signal-like events and ordinary multijet background events. 1
4
The machine-learning search complemented a more inclusive, model-independent strategy based on the scalar sum of transverse momenta, (S_\mathrm{T}), in events containing jets, leptons, or photons. Together, the approaches targeted rare events at the extreme high-energy, high-multiplicity end of the dataset. 4
Neither the inclusive search nor the dedicated phase-space regions showed an excess inconsistent with Standard Model expectations. 3
4
That null result translates into 95% confidence-level exclusions within the models CMS tested:
The analysis extended the black-hole exclusion reach by about 1–1.6 TeV in favorable scenarios relative to earlier searches; the precise improvement is benchmark-dependent.
Large-extra-dimension models are motivated in part by the hierarchy problem: the striking difference between the apparent four-dimensional Planck scale and the electroweak scale. In some versions of these models, gravity becomes strong near the TeV scale, potentially allowing microscopic black holes or related objects to be produced in high-energy collisions.
CMS has not disproved that broader idea. Its result excludes the parts of particular models that predict black-hole or string-ball production often enough, and at low enough masses, to have appeared in this dataset. The limits depend on assumptions about the geometry and size of extra dimensions, the fundamental gravity scale, the threshold for black-hole formation, and the decay model. 3
Likewise, exclusions involving particular assumed numbers of extra dimensions should not be read as a direct measurement of how many spatial dimensions nature has. They apply only within the specified model framework. Extra-dimensional constructions can span very different characteristic sizes, from roughly inverse-TeV scales to millimetres. 1
The High-Luminosity LHC can improve this type of search chiefly by delivering a much larger sample of collisions. More data can increase sensitivity to rare, high-(S_\mathrm{T}), high-multiplicity signatures and to smaller production rates.
That statistical gain could extend tests of heavier black-hole and string-ball thresholds or lower sphaleron transition probabilities, especially alongside upgraded detectors, triggering, and analysis methods designed for high-pileup conditions. But luminosity is not a substitute for collision energy: if the relevant production threshold is beyond the partonic energies available at the LHC, a higher-energy collider would ultimately be required.
The new phase-space-distance approach gave CMS another way to search for unusual event structures in a huge collision dataset. It found no new phenomenon, but produced stronger exclusions for the tested models: black holes below up to 11.4 TeV, string balls below up to 10.7 TeV, and a stringent bound on the benchmark sphaleron transition rate. Those are meaningful constraints on specific routes to TeV-scale gravity—not a final verdict on extra dimensions, quantum gravity, or the hierarchy problem. 3
4
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CMS found no evidence of microscopic black holes, string balls, or electroweak sphalerons in 138 fb⁻¹ of 13 TeV proton proton data.
CMS found no evidence of microscopic black holes, string balls, or electroweak sphalerons in 138 fb⁻¹ of 13 TeV proton proton data. The search used a phase space distance method and a support vector machine classifier to isolate unusually energetic, high multiplicity event patterns from Standard Model backgrounds.
CMS also excluded string ball masses below 9.0–10.7 TeV and limited the tested sphaleron transition fraction above a nominal 9 TeV threshold to 0.0034 at 95% confidence level.
CMS found no evidence of microscopic black holes, string balls, or electroweak sphalerons in 138 fb⁻¹ of 13 TeV proton proton data. The search used a phase space distance method and a support vector machine classifier to isolate unusually energetic, high multiplicity event patterns from Standard Model backgrounds.
Published byEdited with GPT-5.6 TerraImages generated with GPT Image 2
Research answer

Create a landscape editorial hero image for this Studio Global article: How did a CMS analysis of 138 fb⁻¹ of 2016–2018 LHC proton-proton collision data use a novel phase-space-distance machine-learning method to. Article summary: CMS found no statistically significant evidence for microscopic black holes, string balls, or electroweak sphalerons in 138 fb⁻¹ of 13 TeV proton-proton data from 2016–2018. It therefore strengthened model-dependent cons. Topic tags: general, education, government, academic, general web. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermark
CMS examined the full 2016–2018 Run 2 dataset—138 fb⁻¹ of proton-proton collisions at 13 TeV—and found no statistically significant sign of microscopic black holes, string balls, or electroweak sphalerons. The result narrows the viable parameter space of specific TeV-scale gravity and large-extra-dimension models, but it does not rule out extra dimensions or resolve the hierarchy problem. 3
4
The targeted search focused on collision events with distinctive kinematics: many energetic particles distributed relatively isotropically. Such a topology could arise if a microscopic black hole formed and decayed essentially immediately into Standard Model particles; it is also relevant to benchmark electroweak-sphaleron signals.
CMS introduced a method based on phase-space distance—a measure of how close two events are when their reconstructed kinematic properties are represented in a multidimensional event space. This let the analysis organize events by kinematic similarity rather than relying only on a single variable. A support-vector-machine classifier then learned a boundary between simulated signal-like events and ordinary multijet background events. 1
4
The machine-learning search complemented a more inclusive, model-independent strategy based on the scalar sum of transverse momenta, (S_\mathrm{T}), in events containing jets, leptons, or photons. Together, the approaches targeted rare events at the extreme high-energy, high-multiplicity end of the dataset. 4
Neither the inclusive search nor the dedicated phase-space regions showed an excess inconsistent with Standard Model expectations. 3
4
That null result translates into 95% confidence-level exclusions within the models CMS tested:
The analysis extended the black-hole exclusion reach by about 1–1.6 TeV in favorable scenarios relative to earlier searches; the precise improvement is benchmark-dependent.
Large-extra-dimension models are motivated in part by the hierarchy problem: the striking difference between the apparent four-dimensional Planck scale and the electroweak scale. In some versions of these models, gravity becomes strong near the TeV scale, potentially allowing microscopic black holes or related objects to be produced in high-energy collisions.
CMS has not disproved that broader idea. Its result excludes the parts of particular models that predict black-hole or string-ball production often enough, and at low enough masses, to have appeared in this dataset. The limits depend on assumptions about the geometry and size of extra dimensions, the fundamental gravity scale, the threshold for black-hole formation, and the decay model. 3
Likewise, exclusions involving particular assumed numbers of extra dimensions should not be read as a direct measurement of how many spatial dimensions nature has. They apply only within the specified model framework. Extra-dimensional constructions can span very different characteristic sizes, from roughly inverse-TeV scales to millimetres. 1
The High-Luminosity LHC can improve this type of search chiefly by delivering a much larger sample of collisions. More data can increase sensitivity to rare, high-(S_\mathrm{T}), high-multiplicity signatures and to smaller production rates.
That statistical gain could extend tests of heavier black-hole and string-ball thresholds or lower sphaleron transition probabilities, especially alongside upgraded detectors, triggering, and analysis methods designed for high-pileup conditions. But luminosity is not a substitute for collision energy: if the relevant production threshold is beyond the partonic energies available at the LHC, a higher-energy collider would ultimately be required.
The new phase-space-distance approach gave CMS another way to search for unusual event structures in a huge collision dataset. It found no new phenomenon, but produced stronger exclusions for the tested models: black holes below up to 11.4 TeV, string balls below up to 10.7 TeV, and a stringent bound on the benchmark sphaleron transition rate. Those are meaningful constraints on specific routes to TeV-scale gravity—not a final verdict on extra dimensions, quantum gravity, or the hierarchy problem. 3
4
Studio Global AI
This page includes a source-backed answer you can continue inside Studio Global.
CMS found no evidence of microscopic black holes, string balls, or electroweak sphalerons in 138 fb⁻¹ of 13 TeV proton proton data.
CMS found no evidence of microscopic black holes, string balls, or electroweak sphalerons in 138 fb⁻¹ of 13 TeV proton proton data. The search used a phase space distance method and a support vector machine classifier to isolate unusually energetic, high multiplicity event patterns from Standard Model backgrounds.
CMS also excluded string ball masses below 9.0–10.7 TeV and limited the tested sphaleron transition fraction above a nominal 9 TeV threshold to 0.0034 at 95% confidence level.