---
title: 'RadMap Telescope: Cosmic-Ray Spectroscopy'
url: https://www.emergentmind.com/topics/radmap-telescope
type: topic
---

# RadMap Telescope: Cosmic-Ray Spectroscopy

Searching arXiv for RadMap-related papers to support the article.
RadMap Telescope is a compact, low-power radiation telescope developed for spectroscopic space-radiation monitoring, with the explicit aim of reconstructing cosmic-ray nuclei event-by-event in terms of charge $Z$, energy per nucleon, and arrival direction in the MeV–GeV/n regime [2508.12708]. The same name, “RadMAP,” also appears in an earlier and distinct terrestrial context: the Radiological Multi-sensor Analysis Platform, a mobile radiation-background characterization system built on a 20-ft box truck and used to measure fast neutron backgrounds in the San Francisco Bay Area [1611.04996]. The overlap in nomenclature has generated an obvious potential confusion. In the 2016 study, RadMAP is not an astronomical telescope but a mobile multi-sensor survey platform for neutron-background characterization; in the 2025 study, RadMap Telescope denotes a scintillating-fiber tracking calorimeter intended for operational space-radiation measurements. Taken together, these works show a transition from mobile environmental neutron-background sensing to compact event-resolved cosmic-ray spectroscopy, while preserving a common emphasis on radiation-field characterization under realistic operational constraints [1611.04996; 2508.12708].

## 1. Nomenclature and historical context

The term “RadMAP” originally referred to the Radiological Multi-sensor Analysis Platform, a mobile, multi-sensor radiation detection and background characterization system with onboard power, built on a 20-ft box truck [1611.04996]. It originated as the Naval Research Laboratory’s MISTI gamma-ray platform and was transferred to Lawrence Berkeley National Laboratory in late 2011, where it was expanded to include fast-neutron detection and environmental and geospatial sensing for mobile background studies [1611.04996]. In that setting, the platform’s role was to characterize fast neutron backgrounds and their dependence on atmospheric and urban structural conditions rather than to serve as a telescope in the astronomical sense [1611.04996].

The later RadMap Telescope is a different instrument class. It is described as a compact, low-power radiation telescope developed to bring spectroscopic capability into operational space-radiation monitoring, specifically event-by-event recovery of species, energy per nucleon, and arrival direction [2508.12708]. This instrument has already collected data inside the International Space Station from April 2023 to January 2024, although the cited feasibility study is based on Geant4-simulated data and neural-network reconstruction rather than on a full end-to-end analysis of flight measurements [2508.12708].

This naming continuity suggests a broader programmatic lineage centered on radiation-field mapping. A plausible implication is that “RadMap” evolved from denoting a mobile terrestrial mapping platform to denoting a compact spaceborne spectroscopic sensor, but the two instruments should not be conflated: their detector architectures, operational environments, and measurement objectives are substantially different [1611.04996; 2508.12708].

## 2. Instrument architectures

The terrestrial RadMAP platform employed 16 EJ-309 organic liquid scintillator cells supplied by Sandia National Laboratories for fast-neutron detection [1611.04996]. Each cell was a 5-inch diameter by 5-inch long aluminum cylinder, with a total active volume of approximately 25 L across the array [1611.04996]. The cells were oriented horizontally and stacked vertically in two columns of eight within the truck cargo space [1611.04996]. EJ-309 was selected for pulse-shape discrimination capability, enabling neutron/gamma separation, and the experiment used the tail-to-total method for all PSD calculations [1611.04996]. Seven detectors were coupled to 5-inch Hamamatsu photomultipliers and nine to 5-inch Photonis photomultipliers [1611.04996]. Data acquisition used two Struck SIS3320 digitizers, nominally 250 MHz and 12-bit, operated at 200 MHz, corresponding to 5 ns per sample [1611.04996].

That system also integrated non-radiological sensors. Positioning used a NovAtel SPAN GNSS/INS installed in January 2012, with centimeter-level accuracy and a 100 Hz data rate, designed to remain robust under intermittent satellite reception [1611.04996]. Weather measurements were obtained from a Davis Vantage Vue Wireless Weather Station recording atmospheric pressure, temperature, and absolute humidity [1611.04996]. Each identified neutron event was associated with GPS coordinates and the contemporaneous weather metrics [1611.04996].

By contrast, the RadMap Telescope’s Active Detection Unit is a scintillating-fiber tracking calorimeter comprising 1024 plastic scintillating fibers [2508.12708]. Each fiber has a square cross-section of $2\times2$ mm and a length of 80 mm [2508.12708]. The fibers are arranged in 32 layers of alternating orientation, forming an imaging tracking calorimeter whose signals are projected into two 2D views, $yx$ and $yz$, each represented as a $16\times32$ pixel image [2508.12708]. The stack depth in the $y$ direction is $32\times2$ mm, giving 64 mm of active plastic scintillator [2508.12708]. Each fiber is read out by a SiPM on one end, and the instrument records per-fiber light intensities that, to leading order, scale with energy lost in the fiber [2508.12708].

The two architectures reflect different physical measurement problems. The truck-based system optimized neutron-background surveying with environmental co-registration and PSD-capable bulk scintillators [1611.04996]. The space instrument optimized event imaging and spectroscopic reconstruction of charged cosmic-ray nuclei with fine segmentation and per-fiber readout [2508.12708].

## 3. Measurement principles and observables

In the 2016 RadMAP platform study, the measured quantity of interest was the fast-neutron background at ground level in the 500 keV to 8 MeV range [1611.04996]. Event selection relied on tail-to-total PSD in EJ-309 to distinguish neutron interactions from gamma-ray events, and neutron events passing PSD classification within the analysis window were retained [1611.04996]. The physical interpretation of the measured neutrons was explicitly tied to cosmic-ray secondaries: below the Pfotzer maximum, fast neutrons at ground level are largely products of cosmic-ray showers whose flux attenuates exponentially with atmospheric depth [1611.04996].

In the spaceborne RadMap Telescope, the principal observables are track images and longitudinal energy-deposition patterns across the fiber stack [2508.12708]. Tracks are parameterized by spherical angles $\phi\in[-180^\circ,180^\circ)$ and $\theta\in[0^\circ,180^\circ]$ referenced to detector axes [2508.12708]. For stopping particles, the measured Bragg curve encodes $Z$, $\beta$, and hence $E_{\mathrm{kin}}$; for through-going particles, the longitudinal energy-loss profile still carries information on $Z$ and $\beta$ [2508.12708]. The visible response departs from proportionality to $\langle dE/dx\rangle$ at high ionization density because of quenching, which is modeled in Geant4 and described as a saturation-like compression of high-$dE/dx$ signals [2508.12708].

Three complicating effects are emphasized for the telescope: energy-loss straggling, nuclear fragmentation, and ionization quenching [2508.12708]. Straggling introduces substantial event-by-event fluctuations relative to mean Bethe–Bloch behavior. Fragmentation generates lower-$Z$ secondaries and hadronic debris and becomes more probable with increasing $Z$ and traversed material. Quenching reduces separation power at high $Z$ because stopping power scales approximately as $Z^2$ at fixed $\beta$, while the visible signal is compressed [2508.12708]. These are intrinsic obstacles to species and energy reconstruction in thin segmented scintillator systems.

The contrast is therefore between environmental background metrology and per-particle spectroscopy. The ground system measured aggregate neutron count-rate modulation by environmental variables [1611.04996]. The space instrument reconstructs individual charged-particle properties from detector images and energy-loss topology [2508.12708].

## 4. Environmental neutron-background characterization in the mobile RadMAP platform

The mobile RadMAP study assembled 37 usable runs with the scintillators between May 2012 and December 2013 across the San Francisco Bay Area, including urban cores, bridges, tunnels, and rural or sparsely built areas spanning sea level to above 3800 ft elevation [1611.04996]. In 2014, while the truck was stationary for maintenance, more than 100 long stationary datasets of 12–15 hours each were collected at LBNL Building 88, providing high-statistics data for weather and geomagnetic analyses [1611.04996].

The principal empirical result was the expected exponential dependence of fast-neutron count rate on atmospheric pressure [1611.04996]. Combining mobile and stationary data, the count rate $C$ as a function of pressure $P$ in mbar was fit as
$$
C(P)=\exp\!\big(7.58-0.00670\,P\,[\mathrm{mbar}]\big),
$$
with fit parameter uncertainties $(7.58\pm0.08)$ and $(0.00670\pm0.00008)\,\mathrm{mbar}^{-1}$ [1611.04996]. Over the measured pressure range, the rate decreased by about 32%; explicitly, the paper notes about 32% suppression from 970 mbar to 1030 mbar, with rates ranging from roughly 3 CPS at low pressure to about 2.1 CPS at high pressure [1611.04996].

This dependence was then used for event-level normalization to standard atmospheric pressure, 1013.25 mbar [1611.04996]. Each detected neutron at pressure $x$ was assigned the weight
$$
w(x)=\frac{C(1013.25)}{C(x)}.
$$
After this correction, the rate-versus-pressure distribution became flat, with a best-fit near the sea-level mean, $f(1013.25)=2.206$ CPS [1611.04996]. The linear fit to the pressure-adjusted histogram was
$$
y=(0.00001\pm0.00014)x+(2.2\pm0.1),
$$
which was statistically consistent with no residual pressure dependence [1611.04996].

The operational value of this normalization was quantified through 60 s count-rate distributions across all runs [1611.04996]. The systematic width, defined as the RMS after subtracting Poisson statistics in quadrature, decreased from 0.113 CPS in unadjusted data to 0.078 CPS after pressure adjustment, corresponding to a 31% reduction in systematic uncertainty [1611.04996]. The paper explicitly connects this reduction to improved background predictability and improved source detectability [1611.04996].

Residual environmental dependencies were weaker. After pressure normalization, no significant residual correlation was found with temperature in the 500 keV–8 MeV fast-neutron band; the reported linear fit was
$$
y=(0.0004\pm0.0001)x+(2.181\pm0.006)
$$
[1611.04996]. Absolute humidity showed a weak residual positive correlation,
$$
y=(0.0064\pm0.0003)x+(2.136\pm0.004),
$$
although the authors cautioned that more data and analysis were needed and noted that any slight increase could reflect hydrogen-rich air downscattering high-energy neutrons into the detectable band [1611.04996]. Increased geomagnetic activity, parameterized by Kp, produced a small suppression after pressure adjustment, with the linear fit implying about 2.3% suppression at $Kp=5$; this was judged insufficient for routine correction but relevant during major Forbush decreases or ground level enhancements [1611.04996].

A further consistency check related altitude-dependent rates to Pfotzer’s atmospheric depth model using an absorption length of $148\ \mathrm{g/cm^2}$ and found good agreement when pinning the predicted curve to the measured sea-level mean of about 2.2 CPS [1611.04996]. This supports the interpretation that the dominant modulation is the changing atmospheric overburden experienced by cosmic-ray secondaries.

## 5. Urban shielding, sky-view factor, and structural suppression

A distinctive feature of the mobile RadMAP analysis was the use of sky-view factor (SVF) to quantify the degree of urban structural shielding [1611.04996]. SVF was defined as the fraction of unobstructed sky visible from a point out of $2\pi$ steradians [1611.04996]. In urban environments, lower SVF corresponds to increased shielding by surrounding buildings and overhead structures, which suppresses the background of cosmic-ray secondaries, including neutrons [1611.04996].

To estimate SVF along the truck route, the analysis used NOAA/USGS coastal lidar data providing latitude, longitude, and elevation per point [1611.04996]. RadMAP positions were discretized in 3 m bins along the route [1611.04996]. A simple 2D open-sky angle in the transverse plane was found inadequate in dense urban intersections because it could remain $180^\circ$ in 2D even when overhead structures substantially reduced the true visible sky [1611.04996]. The adopted method followed Zakšek et al.: for $n$ angular slices around the point, one finds the highest obstructed elevation angle $\gamma_i$ above the local horizon within a fixed radius $R$, and computes
$$
\mathrm{SVF}=1-\frac{\sum_{i=1}^{n}\sin\gamma_i}{n}.
$$
The implementation used $n=12$ slices at $30^\circ$ intervals and a search radius $R=35$ m around the truck [1611.04996]. Elevation angles were obtained by subtracting each slice’s open-sky angle from $90^\circ$, with slice orientations set by truck bearing and all lidar points at or above truck elevation within each slice included [1611.04996].

The method had explicit limitations. Structures beyond 35 m could still occlude the sky, producing SVF overestimation in dense high-rise environments; the paper notes likely overestimation in San Francisco because of tall buildings outside the 35 m radius [1611.04996]. There was also temporal mismatch between lidar acquisition and neutron acquisition. At one San Francisco location, lidar indicated low SVF from dense buildings that had actually been demolished before the RadMAP run, yielding an anomalously high neutron rate in the lowest SVF bin, 0.10–0.15 [1611.04996]. For that reason, the authors excluded $\mathrm{SVF}<0.15$ from fits and recommended onboard upward-looking lidar to remove the mismatch [1611.04996].

Despite these limitations, the observed suppression was strong. Pressure-adjusted neutron rates versus SVF in Berkeley, Downtown Oakland, and Downtown San Francisco all showed consistent suppression at low SVF, with differences among areas plausibly linked to building density [1611.04996]. A combined quadratic fit, excluding $\mathrm{SVF}<0.15$, gave
$$
y=(-2.2\pm0.2)x^2+(4.3\pm0.2)x+(0.19\pm0.08),
$$
where $y$ is count rate in CPS and $x$ is SVF [1611.04996]. Between $\mathrm{SVF}=1$ and $\mathrm{SVF}=0.2$, the pressure-adjusted rate dropped from 2.25 CPS to 0.95 CPS, corresponding to 58% suppression [1611.04996]. Each of the three urban areas exhibited more than 50% suppression across the measured SVF range [1611.04996].

This result is physically significant because it indicates that structural shielding dominates over any additional neutron production in building materials via spallation [1611.04996]. It also provides an operational lesson: in urban neutron-background modeling, structural context can modulate rates at least as strongly as meteorological variables, and in the measured Bay Area datasets the SVF effect exceeded the pressure effect, which alone produced about 32% suppression across 970–1030 mbar [1611.04996].

## 6. Neural-network reconstruction in the RadMap Telescope

The 2025 feasibility study formulated the RadMap Telescope reconstruction problem as direct inference from detector images [2508.12708]. The two raw grayscale projections, each $16\times32$, were used as network inputs without explicit hit clustering, segment finding, or handcrafted $dE/dx$ features [2508.12708]. The approach therefore relied on CNNs to learn latent representations of track geometry and ionization signatures from the images themselves.

Track-angle reconstruction used one CNN with inception-style feature extraction to learn $\phi$ from the $yx$ view and $\theta_{\mathrm{proj}}$ from the $yz$ view, with the true polar angle recovered through
$$
\theta=\arctan\!\Big(\frac{\tan\theta_{\mathrm{proj}}}{\sin\phi}\Big).
$$
The task was treated as dual classification with 0.2° bins, giving 1800 classes for $\phi$ and 900 for $\theta_{\mathrm{proj}}$ [2508.12708]. Outputs were pseudo-probability distributions over angle classes, and the highest-probability class was selected [2508.12708]. Training used AdamW and early stopping, and the track network had approximately 2.8 million trainable parameters [2508.12708]. Training employed 0.7 million events with 0.2 million validation events per epoch [2508.12708].

Charge reconstruction used two consecutive CNNs, each with multiple flat inception layers and about 2.1 million parameters [2508.12708]. The first, or low-$Z$ network, classified $Z=1$–8 with an overflow bin for charges above 8. The second, or high-$Z$ network, classified $Z=9$–26 and was trained with $Z=27$ included to mitigate boundary effects [2508.12708]. Training used 9 million events and testing used 1 million events, with energies spanning 20 MeV to 5 TeV [2508.12708].

Energy-per-nucleon reconstruction used 26 element-specific CNN regressors returning continuous $E/n$ outputs [2508.12708]. For $Z\ge2$, each network was trained on mixed charges within either $[Z-1,Z+1]$ or $[Z-2,Z+2]$ to improve robustness against charge confusion [2508.12708]. Typical energy ranges were 20 MeV/n to 1 GeV/n, extended to 10 GeV/n for the heaviest elements [2508.12708]. Each element-specific network was trained on about 1.8 million events, and evaluation used a combined 8 million-event dataset [2508.12708]. A branched variant added a filter network routing events to stopping versus through-going energy regressors [2508.12708].

The simulation and dataset generation were deliberately simplified to expose intrinsic detector capability [2508.12708]. The detector model contained only the 1024-fiber stack and omitted surrounding support, housing, electronics, and ISS structure, thus removing external scattering and fragmentation [2508.12708]. Ionization quenching was included, but optical and electrical cross-talk, SiPM saturation, channel-to-channel gain variations, and fiber misalignment were not [2508.12708]. The source model used the most abundant isotopes from hydrogen to iron, excluded electrons and gamma rays, imposed equal elemental abundances to avoid severe class imbalance, and sampled energies from log-uniform distributions over task-specific ranges [2508.12708]. Unless otherwise stated, events had to contain at least three hit fibers in each projection, while stricter criteria were used for angle training and benchmarking [2508.12708].

## 7. Performance, dosimetric significance, and unresolved limitations

The feasibility study reported angular resolutions defined through the Gaussian $\sigma$ of the direction-independent residual distributions, using the central 68% interval and a correction for the intrinsic direction ambiguity of near-MIP cases [2508.12708]. For protons, the results were $\sigma_{\Delta\phi}=0.6^\circ$ and $\sigma_{\Delta\theta}=0.7^\circ$ for MIP 3 GeV events; approximately $1.2^\circ$ for 120 MeV monoenergetic events in both angles; and $1.4^\circ$ and $1.3^\circ$ respectively for stopping events [2508.12708]. Iron showed weak energy dependence with $\sigma_{\Delta\phi}\approx0.8^\circ$ and $\sigma_{\Delta\theta}\le1.1^\circ$ [2508.12708]. Biases were negligible, with $|\mu|$ smaller than the 0.2° bin width [2508.12708]. The reported resolutions were described as close to the geometric limit set by the effective $2\times2$ mm pixel size [2508.12708].

Charge separation performance was highly species dependent [2508.12708]. Exact-charge assignment across all $Z$ was 59% overall [2508.12708]. For hydrogen, the classifier achieved 99.8% accuracy and 99.6% purity; for helium, 99.3% accuracy and 98.8% purity [2508.12708]. Light nuclei with $Z\le8$ were reconstructed with accuracy well over 95% and purity at least 84% [2508.12708]. Exact-charge accuracy for the largest $Z$ values fell to 30–40%, but misassignments typically went to neighboring charges. When one allowed $|\Delta Z|\le1$, heavy-element accuracy remained at least 70%; allowing $|\Delta Z|\le2$ increased it to at least 83%, with mean purity rising to 83% and 91% respectively [2508.12708]. The performance discontinuity at the $Z=8\rightarrow9$ handover was attributed to the intrinsic difficulty of high-$Z$ separation under quenching, straggling, and fragmentation, as well as inter-network boundary effects [2508.12708].

Energy resolution was reported as $\sigma_E/E_{\mathrm{kin}}$ in logarithmic bins [2508.12708]. Hydrogen achieved $\sigma_E/E\le5\%$ below 100 MeV, $\le10\%$ below 300 MeV, and $\le16\%$ below 1 GeV [2508.12708]. Helium achieved $\le14\%$ below 300 MeV/n and $\le24\%$ below 800 MeV/n [2508.12708]. Carbon showed realistic energy resolution in the 10–25% range, though low-purity subsets affected by charge confusion could show a local maximum of about 34% near 100 MeV/n, while the ideal pure-charge limit approached about 10% [2508.12708]. Iron achieved $\le7\%$ near 150 MeV/n, $\le10\%$ below 400 MeV/n, and $\le20\%$ below 2 GeV/n [2508.12708]. The global claim was that the framework achieves less than 20% energy resolution for energies below 1 GeV/n up to iron, with substantially better performance for H and He across the most dosimetrically relevant energies [2508.12708].

These performance levels matter because the telescope is intended to supply the spectroscopic inputs needed for absorbed dose and dose-equivalent estimation [2508.12708]. The paper gives two standard formalisms. In an energy-deposition view,
$$
D=\frac{1}{m}\sum_{\text{events}} E_{\mathrm{dep}},
\qquad
\dot{D}=\frac{1}{m}\int E_{\mathrm{dep}}(Z,E,\Omega)\,\phi_Z(E,\Omega)\,dE\,d\Omega.
$$
In an LET-based view,
$$
D=\frac{1}{\rho}\int \mathrm{LET}(L)\,\phi(L)\,dL,
\qquad
H=\int Q(L)\,dD(L),
$$
with one example quality factor relation given as the ICRP-60 piecewise form [2508.12708]. The study does not implement a full dosimetric pipeline, but it argues that the demonstrated reconstruction of $(Z,E/n,\Omega)$ is sufficient to feed standard NASA and ICRP workflows [2508.12708].

Several limitations are explicit and materially important. The simulated geometry excluded housing, electronics, and ISS structure, so shielding-dependent fragmentation, scattering, and anisotropic acceptance were not represented [2508.12708]. Detector nonidealities such as cross-talk, SiPM saturation and gain variations, fiber-placement tolerances, and nonuniform light yield were also omitted and are expected to degrade the quoted best-case resolutions [2508.12708]. High-$Z$ separation remains intrinsically difficult because quenching compresses high $dE/dx$, while straggling and fragmentation broaden class overlap [2508.12708]. Events with few hit fibers are also intrinsically harder, so selection cuts improve performance at the cost of sensitivity to low-energy or corner-crossing tracks [2508.12708]. Finally, the training distributions used equal elemental abundances and log-uniform spectra to avoid class imbalance, whereas real GCR abundances and energy spectra differ markedly; future work is therefore directed toward realistic priors, domain adaptation, physics-informed training, and potentially more expressive architectures such as transformers or GNNs [2508.12708].

The resulting picture is technically coherent. The older RadMAP platform established a methodology for high-fidelity environmental radiation characterization through event-level contextualization, normalization, and structural modeling [1611.04996]. The newer RadMap Telescope extends the name into space-radiation instrumentation, where the emphasis shifts from environmental modulation of aggregate count rates to event-level reconstruction of particle identity, kinematics, and dosimetric relevance [2508.12708]. The shared conceptual core is radiation mapping under operational conditions, but the instruments themselves belong to different detector lineages and should be distinguished accordingly.

Source: https://www.emergentmind.com/topics/radmap-telescope