Comparison of the $m_{miss}$ shapes for the simulated signal events within the fiducial region and those outside it, after including the effect of PU protons as describe in the text, for a generated $m_{X}$ mass of 1000 GeV. The distributions are shown for single(+z)-single(−z) proton reconstruction categories.

Product of the acceptance times the combined reconstruction and identification efficiency, as a function of $m_{X}$ , for events generated inside the fiducial volume defined in Table 1. The curves shown panel display the different final states. The definition of the fiducial region and signal model used to estimate the acceptance is provided in the text.

Product of the acceptance times the combined reconstruction and identification efficiency, as a function of $m_{X}$ , for different proton reconstruction categories in the $Z \rightarrow \mu\mu $ analysis, and for events generated inside the fiducial volume defined in Table 1. The curves shown panel display the different final states. The definition of the fiducial region and signal model used to estimate the acceptance is provided in the text.

Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The ee final states are shown.The ee events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The $\mu\mu$ final states are shown.The $\mu\mu$ events are displayed without the Z boson $p_T$ requirement.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the negative arm from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed proton $\xi$ in the positive arm from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Distributions of the reconstructed di-proton rapidity from the proton mixing procedure with simulated MC events, are compared to data. The $\gamma$ final states are shown.For illustration, the simulated signal distributions are superimposed for various choices of $m_{X}$ , normalised to a generated fiducial cross section of 100 pb.

Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The proton $\xi$ distribution for the positive CT-PPS arm is shown.

Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The proton $\xi$ distribution for the negative CT-PPS arm is shown.

Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The di-proton invariant mass is shown.

Validation of the background modelling method, using the $e\mu$ control sample. Selected $e\mu$ events are mixed with protons from $Z\rightarrow \mu \mu$ events with $p_{T}(Z)<10$ GeV to simulate the combinatorial background shape, while the data points are unaltered $e\mu$ events. The $m_{miss}$ is shown.

$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZeeX$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow ppZ\mu\mu X$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 1 pb.

$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the multi(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.

$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the multi(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.

$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the single(+z)-multi(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.

$m_{miss}$ distributions in the $pp\rightarrow pp\gamma X$, with the protons reconstructed with the single(+z)-single(-z)method. The background distributions are shown after the fit. The expectations for a signal with $m_{X} = 1000$ GeV are superimposed, where the fiducial production cross section is normalised to 10 pb.

Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z\rightarrow ee$ analysis.

Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z\rightarrow \mu\mu$ analysis.

Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $Z$ analysis.

Upper limits on the $pp\rightarrow ppZ/\gamma X$ cross section at 95% CL, as a function of $m_X$. The 68 and 95% central intervals of the expected limits are represented by the green and yellow bands, respectively, while the observed limit is superimposed as a curve. The plot corresponds to the $\gamma$ analysis.

The observed 90% C.L upper limit on neutrino effective millicharge (\delta_{Q}[e_{0}]) in SR1. The data contains observed upper limit, median sensitivity and 1\sigma and 2\sigma sensitivity range.

The observerd 90% C.L upper limit on solar axion g_{ae} coupling constant in valid across the mass range for SR1. The data contains observed upper limit, median sensitivity and 1\sigma and 2\sigma sensitivity range as a function of Solar Axion Mass .

The observed 90% C.L. upper limit on axion like particle (ALP) coupling constant (g_{ae}) in SR1. ALP mass spanning from 1 keV to 17 keV is considered. The data contains observed upper limit, median sensitivity and 1\sigma and 2\sigma sensitivity range as a function of ALP Mass.

The observed 90% C.L. upper limit on hidden photon (HP) coupling constant squared (\kappa^2) in SR1. HP mass spanning from 1 keV to 17 keV is considered. The data contains observed upper limit, median sensitivity and 1\sigma and 2\sigma sensitivity range as a function of HP Mass.

The observed 90% C.L. upper limit for the spin-independent (cm^2) WIMP cross-section vs. WIMP mass (GeV/c^2), using the Migdal signal pathway. The result is obtained for WIMPs undergoing the Migdal effect from 0.5 to 9 GeV/c2. The data contains observed upper limit, median sensitivity and 1\sigma and 2\sigma sensitivity range as a function of WIMP Mass.

The observed 90% C.L. upper limit for the spin-dependent WIMP cross-section (cm^2) vs. WIMP mass (GeV/c^2) for coupling on neutrons. The result is obtained for WIMPs undergoing the Migdal effect from 0.5 to 9 GeV/c2. The data contains observed upper limit, median sensitivity and 1\sigma and 2\sigma sensitivity range as a function of WIMP Mass.

The observed 90% C.L. upper limit for the spin-dependent WIMP cross-section (cm^2) vs. WIMP mass (GeV/c^2) for coupling on protons. The result is obtained for WIMPs undergoing the Migdal effect from 0.5 to 9 GeV/c2. The data contains observed upper limit, median sensitivity and 1\sigma and 2\sigma sensitivity range as a function of WIMP Mass.

Results of applying the BH algorithm to the mass spectra in the leading photon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading electron category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading muon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading small-R jet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading bjet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading large-R jet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading photon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading electron category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading muon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the inclusive category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the inclusive category, using the fitted background estimations from the initial step

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading small-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading bjet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading photon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading electron category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading muon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading small-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading bjet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading photon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading electron category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading muon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the inclusive category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the inclusive category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading small-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading bjet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading large-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading photon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading electron category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading muon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading small-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading bjet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading large-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading photon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading electron category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading muon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the inclusive category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the inclusive category

Acceptance times efficiency in an HVT model as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times the branching fraction($W^{\prime} \to ZW$) for the HVT signal as functions of mass($m_{ZX}$) in the leading large-R jet category.The full(dashed) red curves correspond to the theoretical predictions of HVT model A(B)

Comparison of the identification efficiencies using standard and merged-ee reconstruction as a function of true PT(Z)

Background rejection factor as a function of signal efficiency. The red curve shows the BDT performance whereas the blue curve corresponds to that of a cut-based analysis relying only on the jet energyfraction deposited in the EM calorimeter

BH p-values of the 100 pseudo-experiments as a function of in the leading small-R jet category for an injected Gaussian-shaped signal with a relative width value of 3%. The fractions of the pseudo-experiments that have the correctly identified interval and BH p-values below the threshold of 0.01 are indicated. The background is derived by the background-only fit in the full fitting range.

BH p-values of the 100 pseudo-experiments as a function of in the leading small-R jet category for an injected Gaussian-shaped signal with a relative width value of 3%. The fractions of the pseudo-experiments that have the correctly identified interval and BH p-values below the threshold of 0.01 are indicated. The background is derived by the background-only fit in the range excluding the BH interval.

Fractions of pseudo-experiments in which the detected BH interval agrees with the injected mass point and the BH p-value is below 0.01 as a function of mass in the leading small-R jet category for Gaussian-shaped signal with a relative width of 3%.

Fractions of pseudo-experiments in which the detected BH interval agrees with the injected mass point and the BH p-value is below 0.01 as a function of mass in the leading small-R jet category for Gaussian-shaped signal with a relative width of 3%.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 3% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 3% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 5% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 5% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 10% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 10% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Data yields of the six event categories in the $Z\to e^+e^-$ and $\mu^+\mu^-$ decay channels. The merged-$e^+e^-$ events are included in the $e^+e^-$ channel, increasing the event yield, mainly in the leading large-$R$-jet category, by 0.6 %.

A list of mass spectra, event categories and their corresponding fit ranges, functional forms, numbers of free parameters and global $\chi^2$ $p$-values from background-only fits. The fit range values are rounded to the nearest 5 GeV. Here $f_1(x)=p_0\left(\mathrm{e}^{-p_1x}+p_2\mathrm{e}^{-(p_1+p_3)x}+p_4\mathrm{e}^{-(p_1+p_3+p_5)x}+\cdots\right)$ and $f_2(x)=p_0(1-x)^{p_1}x^{p_2+p_3\ln\!x+p_4\ln^2\!x+\cdots}$

A list of mass spectra, event categories and their corresponding signal-sensitive mass ranges. The initial BH $p$-value is obtained by using the background derived from the background-only fit in the full fit range, whereas the new BH $p$-value uses the background derived from the background-only fit in the range excluding the initial BH interval.

A list of mass spectra, event categories, relative width values of Gaussian-shaped signals and limit-sensitive mass ranges and fractions, corresponding to the mass values in the previous column, of pseudo-experiments having exclusion upper limits higher than the nominal exclusion limit at 95% CL

Cutflow of HVT model $A$ signals ($W^\prime \to ZW \to \ell\ell qq$) with $m_{W^\prime} = 1$ TeV and $m_{W^\prime} = 4$ TeV based on MC simulations.

Acceptance times efficiency ($\mathcal{A} \times \epsilon$) values in % in the dominant event category for $p^Z_{\mathrm{T}} > 100$ GeV in the $Z \to \ell^{+}\ell^{-}$ decay channel.

Cutflow table for a pair produced coloron of 1500 GeV decaying into two quarks.

The observed number of data, background and top squark signal events in each of the signal regions of the inclusive selection

The observed number of data, background and coloron signal events in each of the signal regions of the inclusive selection

The observed number of data, background and top squark signal events in each of the signal regions of the b-tagged selection

Signal acceptance and efficiency (in %) as a function of M(STOP), before mass windows

Signal acceptance (in %) and efficiency as a function of M(STOP), after mass windows

Signal acceptance and efficiency (in %) as a function of M(RHO), before mass windows

Signal acceptance and efficiency (in %) as a function of M(RHO), after mass windows

Signal acceptance (in %) and efficiency as a function of M(STOP), before mass windows

Signal acceptance (in %) and efficiency as a function of M(STOP), after mass windows

Cross section excluded at 95% CL as a function of the top squark mass, for a pair produced top squark with decays into a pair of light-quarks.

Cross section excluded at 95% CL as a function of the cooron mass, for a pair produced coloron with decays into a pair of light-quarks.

Cross section excluded at 95% CL as a function of the top squark mass, for a pair produced top squark with decays into a b- and an s-quark.

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Inclusive SIGMA(1660) production cross sections, times the < LAMBDA PI> branching ratio, in the XL range 0.3 to 1.0 for the SIGMA- beam on the Copper target.

Inclusive SIGMA(1660) production cross sections, times the < LAMBDA PI> branching ratio, in the XL range 0.3 to 1.0 for the SIGMA- beam on the Carbon target.

Inclusive SIGMA(1660) production cross sections per nucleon, times the < LAMBDA PI> branching ratio, in the XL range 0.3 to 1.0 for the SIGMA- beam on the Carbon target, and the exponent in the cross section parametrization of the formA**POWER.

Inclusive SIGMA(1385) production cross sections for the PI- beam with a Copper target, times the < LAMBDA PI> branching ratio., RE = PI- CU --> SIGMA(1385)- X. PI- CU --> SIGMA(1385)+ X.

Inclusive SIGMA(1385) production cross sections for the PI- beam with a Carbon target, times the < LAMBDA PI> branching ratio., RE = PI- C --> SIGMA(1385)-X. PI- C --> SIGMA(1385)+ X.

Inclusive SIGMA(1385) production cross sections per nucleon, times the < LAMBDA PI> branching ratio, for the PI- beam, and the exponent in the cross section parametrization of the form A**POWER.

Inclusive SIGMA(1385) production cross sections for the Neutron beam with aCopper target, times the < LAMBDA PI> branching ratio.

Inclusive SIGMA(1385) production cross sections for the Neutron beam with aCarbon target, times the < LAMBDA PI> branching ratio.

Inclusive SIGMA(1385) production cross sections per nucleon, times the < LAMBDA PI> branching ratio, for the Neutron beam, and the exponent in the cross section parametrization of the form A**POWER.

Differential cross sections as a function of XL for SIGMA+- production in CU and C with the SIGMA- beam.

Differential cross sections as a function of PT**2 for SIGMA+- production in CU and C with the SIGMA- beam.

Differential cross sections as a function of XL for SIGMA(1385)- productionin CU and C with the PI- and SIGMA- beams.

Differential cross sections as a function of XL for SIGMA(1385)- productionin CU and C with the Neutron beam.

Differential cross sections as a function of PT**2 for SIGMA(1385)- production in CU and C with the PI- and SIGMA- beams.

Differential cross sections as a function of PT**2 for SIGMA(1385)- production in CU and C with the Neutron beam.

Differential cross sections as a function of XL for SIGMA(1385)+ productionin CU and C with the PI- and SIGMA- beams.

Differential cross sections as a function of XL for SIGMA(1385)+ productionin CU and C with the Neutron beam.

Differential cross sections as a function of PT**2 for SIGMA(1385)+ production in CU and C with the PI- and SIGMA- beams.

Differential cross sections as a function of PT**2 for SIGMA(1385)+ production in CU and C with the Neutron beam.

Differential cross sections as a function of XL for SIGMABAR(1385)- production in CU and C with the SIGMA- beam.

Differential cross sections as a function of PT**2 for SIGMABAR(1385)- production in CU and C with the SIGMA- beam.

Differential cross sections as a function of XL for SIGMA(1660)+- production in CU and C with the SIGMA- beam.

Differential cross sections as a function of PT**2 for SIGMA(1660)+- production in CU and C with the SIGMA- beam.