Fragmentation of 1 GeV/nucleon iron ions in thick targets relevant for space exploration

Cary Zeitlin

Fragmentation of 1 GeV/nucleon iron ions in thick targets relevant for space exploration

Advances in Space Research, 2005

Fragmentation of 1 GeV/nucleon iron ions in thick targets relevant for space exploration C. La Tessa a, * , S. Guetersloh b , L. Heilbronn b , J. Miller b , L. Sihver a,c , C. Zeitlin b a Chalmers University of Technology, Nuclear Science and Engineering, 41269 Gothenburg, Sweden b Lawrence Berkeley National Laboratory, Berkeley, CA, USA c Roanoke College, Salem, VA, USA Received 21 December 2004; received in revised form 4 February 2005; accepted 4 February 2005 Abstract We have measured charged nuclear fragments produced by 1 GeV/nucleon 56 Fe ions interacting with aluminium, polyethylene and lead. These materials are relevant for assessment of radiation risk for manned space ﬂight. The data will be presented in a form suitable for comparison with models of nuclear fragmentation and transport, including linear energy transfer (LET) spectrum, ﬂu- ence for iron and fragments, event-tack- and event-dose-averaged LET, total dose and iron contribution to dose. Ó 2005 Published by Elsevier Ltd on behalf of COSPAR. Keywords: Nuclear fragmentation; Space environment; Radioprotection; Iron projectile 1. Introduction The space radiation environment is composed of par- ticles with a wide range of charges and energies which are produced by several sources, one of the major ones being the galactic cosmic rays (GCR). It is well known that exposure to the radiation will af- fect the health of humans, thus it is important for the safety of crewmembers to estimate the dose incurred dur- ing space missions, both inside and outside the protective eﬀects of the geomagnetosphere; radiation exposure is one of the principal risks for astronauts on extended space missions, such a possible mission to Mars. In order to investigate the biological eﬀects due to radi- ation exposure, the composition of the radiation ﬁeld both outside and inside the spacecraft needs to be well known. The external environment is fairly well known, but when the incident radiation interacts with the space- craft hull and internal materials, nuclear fragmentation modiﬁes the external radiation ﬁeld. Accurate knowledge of the physics of the fragmentation process is needed in or- der to improve the radiation protection for astronauts. Although comprising only 1% of the GCR, iron makes a major contribution to the equivalent dose (around 14%) and it has a broad peak in the kinetic en- ergy spectrum from 100 to 1000 MeV/nucleon (Simpson, 1983). In the present work, we studied the fragmentation of 1 GeV/nucleon 56 Fe ions in several diﬀerent thick tar- gets. The experiments were performed at the alternating gradient synchrotron (AGS) at Brookhaven National Laboratory in 2002. Linear energy transfer (LET) spec- tra, ﬂuences for primary beam and fragments, event- track- and event-dose-averaged LET, total dose and dose from iron alone will be presented in this paper. 2. Experimental setup Table 1 is a list of the target materials considered in this paper. 0273-1177/$30 Ó 2005 Published by Elsevier Ltd on behalf of COSPAR. doi:10.1016/j.asr.2005.02.007 * Corresponding author. Tel.: +46 317722911; fax: +46 317722931. E-mail address: chiara@chem.chalmers.se (C. La Tessa). www.elsevier.com/locate/asr Advances in Space Research 35 (2005) 223–229

In Fig. 1, a schematic diagram of the experimental set- up is shown; all detectors are aligned on the beam axis. In the experiments, we used fully depleted silicon detectors to measure deposited energy, providing parti- cle identiﬁcation. Detector thickness and radii are listed in Table 2. Detector d3mmu is placed before the target and is used as trigger detector. The position-sensitive detectors (PSDs), have two output signals from which it is possible to determine po- sition in one coordinate via charge division. The more- upstream detector is oriented to measure y and the more-downstream detector to measure x (z is the inci- dent beam direction). Each PSD also provides a DE sig- nal, proportional to the total charge collected. With this kind of setup it was only possible to detect charge changing and not neutron stripping. All silicon detectors were read out with standard elec- tronics (Zeitlin et al., 1994). 3. Data analysis The analysis was performed using the CERN library package PAW (Brun et al., 1989). Our analysis method is based on the use of scatter plots of the energy deposi- tion (DE) values in adjacent detectors in order to make diﬀerent graphical cuts (Zeitlin et al., 1997 and Zeitlin et al., 2001). Since the analysis depends on these cuts, the results are prone to variations arising from the small degree of subjectivity involved in drawing cut contours. Eﬀorts were made to perform analysis as consistently as possible on all data sets. 1. The ﬁrst cut is made on the trigger detector d3mmu. The beam at exit from the evacuated part of the beam line has some small inhomogeneity due to interac- tions of the ions with elements of the beam line. In addition some fragments are produced by the interac- tion of the beam with the detectors upstream of the target. In order to eliminate these particles we selected the events within about two standard devia- tions of the iron peak in the trigger detector. 2. In a plot of DE values in dex2 vs. dey2, a cut is made selecting the events lying along the densely populated 45° line, as shown in Fig. 2. The eliminated events Fig. 1. Experimental setup. Table 2 Detector thicknesses and radii Detector type Thickness (mm) Active radius (cm) dey1 1 1.95 dex1 1 1.95 d3mmu 3 1.15 dey2 1 1.95 dex2 1 1.95 d3mm1 3 1.15 d3mm2 3 1.15 Table 1 Material used in this work Target Thickness (g/cm 2 ) Density (g/cm 3 ) Lead 30 11.35 Aluminium 26 2.7 PMMA (Lucite) 23 1.16 224 C. La Tessa et al. / Advances in Space Research 35 (2005) 223–229

Advances in Space Research 35 (2005) 223–229 www.elsevier.com/locate/asr Fragmentation of 1 GeV/nucleon iron ions in thick targets relevant for space exploration C. La Tessa a,* , S. Guetersloh b, L. Heilbronn b, J. Miller b, L. Sihver a a,c , C. Zeitlin b Chalmers University of Technology, Nuclear Science and Engineering, 41269 Gothenburg, Sweden b Lawrence Berkeley National Laboratory, Berkeley, CA, USA c Roanoke College, Salem, VA, USA Received 21 December 2004; received in revised form 4 February 2005; accepted 4 February 2005 Abstract We have measured charged nuclear fragments produced by 1 GeV/nucleon 56Fe ions interacting with aluminium, polyethylene and lead. These materials are relevant for assessment of radiation risk for manned space ﬂight. The data will be presented in a form suitable for comparison with models of nuclear fragmentation and transport, including linear energy transfer (LET) spectrum, ﬂuence for iron and fragments, event-tack- and event-dose-averaged LET, total dose and iron contribution to dose. Ó 2005 Published by Elsevier Ltd on behalf of COSPAR. Keywords: Nuclear fragmentation; Space environment; Radioprotection; Iron projectile 1. Introduction The space radiation environment is composed of particles with a wide range of charges and energies which are produced by several sources, one of the major ones being the galactic cosmic rays (GCR). It is well known that exposure to the radiation will affect the health of humans, thus it is important for the safety of crewmembers to estimate the dose incurred during space missions, both inside and outside the protective eﬀects of the geomagnetosphere; radiation exposure is one of the principal risks for astronauts on extended space missions, such a possible mission to Mars. In order to investigate the biological eﬀects due to radiation exposure, the composition of the radiation ﬁeld both outside and inside the spacecraft needs to be well known. The external environment is fairly well known, but when the incident radiation interacts with the space- craft hull and internal materials, nuclear fragmentation modiﬁes the external radiation ﬁeld. Accurate knowledge of the physics of the fragmentation process is needed in order to improve the radiation protection for astronauts. Although comprising only 1% of the GCR, iron makes a major contribution to the equivalent dose (around 14%) and it has a broad peak in the kinetic energy spectrum from 100 to 1000 MeV/nucleon (Simpson, 1983). In the present work, we studied the fragmentation of 1 GeV/nucleon 56Fe ions in several diﬀerent thick targets. The experiments were performed at the alternating gradient synchrotron (AGS) at Brookhaven National Laboratory in 2002. Linear energy transfer (LET) spectra, ﬂuences for primary beam and fragments, eventtrack- and event-dose-averaged LET, total dose and dose from iron alone will be presented in this paper. 2. Experimental setup * Corresponding author. Tel.: +46 317722911; fax: +46 317722931. E-mail address: chiara@chem.chalmers.se (C. La Tessa). 0273-1177/$30 Ó 2005 Published by Elsevier Ltd on behalf of COSPAR. doi:10.1016/j.asr.2005.02.007 Table 1 is a list of the target materials considered in this paper. 224 C. La Tessa et al. / Advances in Space Research 35 (2005) 223–229 Table 1 Material used in this work Table 2 Detector thicknesses and radii Target Thickness (g/cm2) Density (g/cm3) Detector type Thickness (mm) Active radius (cm) Lead Aluminium PMMA (Lucite) 30 26 23 11.35 2.7 1.16 dey1 dex1 d3mmu dey2 dex2 d3mm1 d3mm2 1 1 3 1 1 3 3 1.95 1.95 1.15 1.95 1.95 1.15 1.15 In Fig. 1, a schematic diagram of the experimental setup is shown; all detectors are aligned on the beam axis. In the experiments, we used fully depleted silicon detectors to measure deposited energy, providing particle identiﬁcation. Detector thickness and radii are listed in Table 2. Detector d3mmu is placed before the target and is used as trigger detector. The position-sensitive detectors (PSDs), have two output signals from which it is possible to determine position in one coordinate via charge division. The moreupstream detector is oriented to measure y and the more-downstream detector to measure x (z is the incident beam direction). Each PSD also provides a DE signal, proportional to the total charge collected. With this kind of setup it was only possible to detect charge changing and not neutron stripping. All silicon detectors were read out with standard electronics (Zeitlin et al., 1994). 3. Data analysis The analysis was performed using the CERN library package PAW (Brun et al., 1989). Our analysis method is based on the use of scatter plots of the energy deposition (DE) values in adjacent detectors in order to make diﬀerent graphical cuts (Zeitlin et al., 1997 and Zeitlin et al., 2001). Since the analysis depends on these cuts, the results are prone to variations arising from the small degree of subjectivity involved in drawing cut contours. Eﬀorts were made to perform analysis as consistently as possible on all data sets. 1. The ﬁrst cut is made on the trigger detector d3mmu. The beam at exit from the evacuated part of the beam line has some small inhomogeneity due to interactions of the ions with elements of the beam line. In addition some fragments are produced by the interaction of the beam with the detectors upstream of the target. In order to eliminate these particles we selected the events within about two standard deviations of the iron peak in the trigger detector. 2. In a plot of DE values in dex2 vs. dey2, a cut is made selecting the events lying along the densely populated 45° line, as shown in Fig. 2. The eliminated events Fig. 1. Experimental setup. 225 C. La Tessa et al. / Advances in Space Research 35 (2005) 223–229 straight-ahead approximation is not longer valid the charge spectrum doesnÕt shows a clear particles distribution. LET spectrum instead of charge spectrum was therefore used for the analysis. Assuming that LET in water and energy deposition in silicon are related by a proportionality constant (for our energy range this approximation is good within 2–3%), we could write the following relation for iron surviving after the target: LETt ðFeÞ ¼ Fig. 2. Scatter plot of DE in dex2 vs. DE in dey2, showing a typical cut used to select events that have a well-correlated signal in both detectors. could be particles that had an incomplete registration of DE in one of the detectors (due to e.g., edge hits) or fragments produced in the silicon. We could not distinguish between these two kinds of tracks, so we took into account only events that had a well-correlated signal in both detectors. The cut contour was drawn near the iron cluster in order to exclude charge 25 (Mn) fragments produced in the silicon; such particles could release in the detector an amount of energy comparable to the iron ions because of their similar charge. 3.1. LET spectrum and fluence After applying the aforementioned cuts we could plot the spectrum used for the analysis. Detectors dey2 and dex2 (PSD2) were chosen for this purpose. In the case of thin targets, we could assume that the fragments produced have approximately the same velocity as the primary ions: the proportionality factor between Z2 and DE takes the same value for all the fragments, and thus it is possible to rescale the spectrum as number of events vs. particles charge pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1Þ Z i ¼ Z Fe DEi =DEFe : However, this approximation breaks down for thick targets: the fragments show a velocity distribution with values that could be quite diﬀerent from the primary beam velocity (Giacomelli et al., 2004). This diﬀerence becomes more relevant for light fragments. When the DEt ðFeÞ LET0 ðFeÞ; DE0 ðFeÞ ð2Þ where the subscript t refers to the presence of the target and the subscript 0 to its absence. The DEt(Fe) and DE0(Fe) values were measured in the energy spectra, looking at the iron peak position. The value of LET0(Fe) was obtained by an energy loss program developed by our group. In this case the initial energy was 980 MeV/nucleon and the calculated LET0(Fe) value was 151.4 keV/lm. The energy spectrum was rescaled as LET spectrum using the following equation: LETt ðDEÞ ¼ LETt ðFeÞ DEt : DEt ðFeÞ ð3Þ A LET spectrum for the lead target is reported in Fig. 3. With the spectra so obtained we were able to identify the peak of the primary beam and those of a few fragments. The peak of the lightest fragment we could distinguish was diﬀerent for each target. For the distinguishable peaks, the bottom of each ‘‘valley’’ was used to delimit the range of events corresponding to a particular Z. The number of events for each Z, N(Z), was then determined by counting events between the delimiting range. All the remaining events, contained in the indistinguishable peaks, were clustered in one group and counted together as ‘‘grouped particles’’. To take into account the attenuation of the beam in the PSD2 detectors the quantity N(Z) must be divided by the survival fraction (s.f.) in 2 mm of silicon: N 0 ðZÞ ¼ N ðZÞ=s:f:ðZÞ: ð4Þ The survival fraction is calculated as s:f:ðZÞ ¼ expðx rðZÞ N a =AÞ; ð5Þ where x = 2 mm, q and A are for silicon, Na is AvogadroÕs number and r is the geometrical cross-section (Townsend and Wilson, 1986). The survival fraction for the grouped particles was assumed to be the average of the survival fractions of each particle. TownsendÕs model was chosen according to the analysis performed by Zeitlin et al. (2001), future studies will 226 C. La Tessa et al. / Advances in Space Research 35 (2005) 223–229 1 G e V / n5 6 Fe + 3 0 g / c m 2 Pb Number of events 10 10 3 2 10 1 0 25 50 75 10 0 12 5 150 175 200 LET (keV/micron) Fig. 3. Histogram of DE summed over dey2 and dex2 and scaled to the LET according to Eq. (3). These data were obtained using a 30 g/cm2 lead target. be performed to test its validity and to compare it with other models. Fluence for events with charge Z was deﬁned to be the ratio between the number N 0 (Z) and the total number of events , Fe X 0 UðZÞ ¼ N ðZÞ N 0 ðZÞ: ð6Þ Z¼1 A further correction was necessary to eliminate the fragments produced in the beamline other than in the silicon (detector dead layers, air, etc.). Thus the experiment was run taking out the target and the ﬂuences were measured; we denote the target-out data as U0(Z); the subscript 0 refers to the absence of the target. The equations we used for the correction are ð7Þ UðZÞcorrect ¼ UðZÞ U0 ðZÞUðFeÞcorrect for the fragments: ð8Þ 3.2. Event-track- and event-dose-averaged LET Event-track- and event-dose-average LET values were also measured for all targets. The LET value for a particle with charge Z was obtained as:1 Z2 ZðFeÞ2 : LETt ðg:p:Þ ¼ LET0 ðFeÞ DE : DE0 ðFeÞ ð10Þ With the experimental setup we used we can not measure the number of particles emerging from the target because the downstream detectors register the energy deposition of the incoming fragments as a single event. Thus, we are not able to determine the track-averaged LET and we have to turn to another quantity to characterize the beam quality. We deﬁne the event-track-averaged LET as X hLETievent ¼ Ucorrect ðZÞ LETðZÞ ð11Þ Z UðFeÞcorrect ¼ UðFeÞ=U0 ðFeÞ for the primary beam; LETt ðZÞ ¼ LETt ðFeÞ For the grouped particles a mean energy deposition was measured from the energy spectrum and the LET was calculated as ð9Þ the diﬀerence between the latter and the track-averaged LET is that in this case the ﬂuences Ucorrect(Z) are normalized to the total number of incident particles on the target. The relation between ÆLETætrack and ÆLETæevent is hLETitrack ; hLETievent ¼ m ð12Þ is the average multiplicity, i.e., the mean numwhere m ber of particles emerging from the target per incident particle. We also deﬁned a quantity analogous to the ÆLETædose P 2 Ucorrect ðZÞ LETðZÞ : ð13Þ hLETievent-dose ¼ PZ Z Ucorrect ðZÞ LETðZÞ There is no simple relation between this quantity and the ÆLETædose because of the presence of the LET2. 3.3. Dose 1 In this case the straight-ahead approximation is valid because we are applying it to heavy fragments that show distinguishable peaks in the charge spectrum. The relation to obtain the total dose is Dosetot ¼ N out hLETitrack ; A ð14Þ 227 C. La Tessa et al. / Advances in Space Research 35 (2005) 223–229 where Nout is the total number of particle after the target and A is the detector area. To calculate the total dose per incident particle we used the following equation: 1 N out hLETievent Dosetot =inc: part: ¼ hLETitrack ¼ A N in A ð15Þ the last relation is obtained using the Eq. (12). The iron dose value is DoseFe ¼ N out ðFeÞ LETt ðFeÞ; A where Nout(Fe) is the number of iron particles after the target. We were interested to measure the iron contribution to the dose DoseFe =Dosetot LETt ðFeÞ ¼ UðFeÞcorrect : hLETievent ð16Þ 4. Results 4.1. Fluence results The ﬂuence values (Eqs. (7) and (8)) measured for all targets are reported in Tables 3 and 4. Using the PMMA we were able to identify only the iron peak: the considerable thickness of the target, in fact, caused a high fragment production. Overlapping Table 4 Fluence values for the primary beam and grouped particles with Z 6 25, after 23 g/cm2 of PMMA PMMA 23 g/cm2 Z Fluence 104 26 625 950 ± 14 905 ± 50 The detector pair dey2/dex2 was used for the analysis. of the fragment peak prevented resolution of individual species. Fluence as a function of the charge is plotted in Fig. 4 for all targets. The highest iron ﬂuence is obtained using the lead target; there is a factor 2 between this value and the one obtained using the aluminium target and a factor 7 using the PMMA target. 4.2. Event-track- and event-dose averaged LET results Iron LET value after the target, event-track- and event-dose-averaged LET were obtained using Eqs. (6), (7) and (9); the results are reported in Table 5. To understand the ÆLETæ results we have to take into account that two kinds of processes are involved: nuclear fragmentation and energy loss due to Coulomb interactions. The former reduces the number of high-LET particles whereas the latter increases the LET of all ions. The highest ÆLETæevent is measured for the unshielded beam. With the presence of the target, nuclear fragmentation becomes important and causes a reduction of this value. The ÆLETædose values, on the other hand, exhibit diﬀerent behaviours for the metal targets and the plastic target. In the PMMA target the low-LET particles Table 3 Fluence values for the primary beam, for 13 < Z < 26 charges and for grouped particles with Z 6 13 combined, after 30 g/cm2 of lead and 26 g/cm2 of aluminium Z 26 25 24 23 22 21 20 19 18 17 16 15 14 613 Lead 30 g/cm2 Aluminium 26 g/cm2 Fluence 104 Fluence 104 6620 ± 8 253 ± 8 142 ± 6 100 ± 5 113 ± 5 85 ± 5 63 ± 4 52 ± 4 50 ± 4 63 ± 4 75 ± 4 57 ± 4 70 ± 4 226 ± 20 3227 ± 9 343 ± 10 262 ± 9 227 ± 8 150 ± 6 176 ± 7 189 ± 7 172 ± 7 171 ± 7 156 ± 7 163 ± 7 177 ± 7 200 ± 7 439 ± 30 The ﬂuence for events with charge Z was deﬁned to be the ratio between the number of events for that Z and the total number of events.The detector pair dey2/dex2 was used for the analysis. Fig. 4. Fluence values as a function of the charge for all targets. We omitted the error bars because they have same dimension as the used symbols. 228 C. La Tessa et al. / Advances in Space Research 35 (2005) 223–229 Table 5 Iron LET, event-track- and event-dose-averaged LET values for all targets Target (thickness g/cm2) LET(Fe)t (keV/lm) ÆLETæevent (keV/lm) ÆLETæevent-dose (keV/lm) – Lead (30) Aluminium (26) PMMA (23) 151.4 190 226 291 151.4 141.9 ± 0.9 111.3 ± 0.8 85.5 ± 0.5 151.4 182.8 ± 1.2 195.4 ± 1.6 137.1 ± 1.5 The results were obtained using Eqs. (9), (11) and (13). The values for the unshielded beam were obtained with an energy loss program developed by our group. production, as in the case of ÆLETæevent, is larger than the increase of the LETt(Fe) values, thus yielding a lower ÆLETæevent-dose compared to the unshielded beam. On the contrary, in the lead and aluminium targets the fragmentation doesnÕt compensate the increase of the primary beam LET and the ÆLETæevent-dose is substantially increased with respect to the unshielded beam. One should also remark that ÆLETæevent-dose and LETt(Fe) are comparable for both metal targets. 4.3. Dose results Total dose values after the target were measured too (Eq. (14)). Moreover the iron dose was measured in order to estimate its contribution to the total dose (Eq. (15)). The results are reported in Table 6. The unshielded beam produces the highest value, showing that the presence of the target always decreases the dose. What one expects from simple consideration is that fragmentation should reduce the iron ﬂuence and thus its contribution to the dose. On the other hand, iron LET should be increased by Coulomb interactions to a greater extent than for lighter fragments. Such considerations lead us to expect that PMMA, which is the most eﬀective of the three materials for fragmenting the primary ions and has the lowest stopping power, should produce the lowest iron dose and ﬂuence; lead, which has opposite properties, should produce the highest iron dose and ﬂuence. This is exactly what we observe in Fig. 5, where we compare the iron contribution to the dose with its ﬂuence. Table 6 Total dose and iron contribution to the dose for the shielded and the unshielded beam Target (thickness g/cm2) Total dose/inc. particle (nGy) Fe dose/total dose (%) – Lead (30) Aluminium (26) PMMA (23) 20.30 19.03 ± 0.12 14.93 ± 0.11 11.46 ± 0.07 100 88.6 ± 0.8 65.5 ± 0.8 32.2 ± 0.6 The values were obtained using Eqs. (14) and (15). Fig. 5. Iron contribution to the ﬂuence and to the total dose for all targets. 5. Conclusions In this work, we studied nuclear fragmentation of 1 GeV/nucleon iron beam interacting with diﬀerent thick targets. Fluence for primary beam and fragments, total dose and iron contribution to the dose were measured. For metallic targets it was possible to measure the ﬂuences for primary beam and fragments with 13 < Z < 26, while for the PMMA target we could only measure the iron ﬂuence. Two new quantities were deﬁned in this paper: eventtrack- and event-dose averaged LET. They can be used to characterize the beam quality in a leading-particle analysis. The highest value of total dose is measured for the unshielded beam and it is reduced by the presence of the targets. Evaluating iron contribution to the total dose we could conclude that the highest fragmentation is obtained using the PMMA target. The lowest one is obtained with the lead target and, in this case, the iron contribution to the total dose was more than 90%. References Brun, R., Couet, O., Vandoni, C.E., et al. PAW, a general-purpose portable software tool for data analysis and presentation. Comput. Phys. Commun. 57, 432–437, 1989. Giacomelli, M., Sihver, L., Skvarč, J., et al. Projectile like fragment emission angles in fragmentation reactions of light heavy ions in the energy region <200 MeV/nucleon: modeling and simulation. Phys. Rev. C 69, 1–11, 2004. Simpson, J.A. Elemental and isotopic composition of the galactic cosmic rays. Ann. Rev. Nucl. Part. Sci. 33, 323–381, 1983. C. La Tessa et al. / Advances in Space Research 35 (2005) 223–229 Townsend, L.W., Wilson, J.W. Comparison of abrasion model diﬀerences in heavy ion fragmentation: optical versus geometric models. Phys. Rev. C 34, 1491–1494, 1986. Zeitlin, C.J., Frankel, K.A., Gong, W., et al. A modular solid state detector for measuring high energy heavy ion fragmentation near the beam axis. Radiat. Meas. 23, 65–81, 1994. 229 Zeitlin, C.J., Heilbronn, L., Miller, J., et al. Heavy fragment production cross section from 1.05 GeV/nucleon 56Fe in C, Al, Cu, Pb, and CH2 targets. Phys. Rev. C 56, 388–397, 1997. Zeitlin, C.J., Fukumura, A., Heilbronn, L., et al. Fragmentation cross section of 600 MeV/nucleon 20Ne on elemental target. Phys. Rev. C 64, 1–16, 2001.

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

In the course of the last decades, we have assisted a slow and steady modification of the energy balance in urban environments, causing an alteration of the energy partitioning in cities that emerges from the complex nature of urban environments. Such changes include increases in heat and greenhouse gas emissions and fluxes, which are directly dependent on the growing world population and the constant increase of energy demand and consumption. The research takes place within the EU Horizon 2020 programme, funding PAUL and ICOS Cities projects. To accurately estimate and close the urban energy balance, it is essential to study the residual terms, with a focus on the anthropogenic contribution. The Eddy Covariance approach for environmental studies is a powerful technique that is used in many applications in the study of urban ecosystems and fluxes. For the present work the urban EC tower instrumentation located in the centre of Sassari was employed. The post-processing steps of flux data included flux footprint climatology and energy and carbon budgets calculations, which were realized using tools such as ‘Flux Footprint Prediction’ and ‘Tovi’ from LI-COR. The energy balance regression model reveals a state of non-closure with a poor correlation. The application of the correction method of Mauder et al. (2013) helps achieve a near-closure balance, with few residuals left. By comparing the daily variation of budget components, the correction reveals the action of CO2 and H2O that delay heat fluxes, enhanced probably by advection effects and winds. Energy residuals seem to correlate well with energy availability and storage, hinting at inputs from anthropogenic heat fluxes, like fossil fuel consumption and urban metabolism. The range of variations remains uniform, with only little deviations falling along main wind directions (e.g. NW winds). The CO2 balance analysis corroborates the effective contribution of CO2 from fossil fuels due to enrichment by urban traffic peaks at rush hours. The flux footprint processing demonstrates the urban nature of the flux data since the major contributions (about 60%) to footprint climatology come from urban areas, while a minor input is from vegetated surfaces. The footprint profiles seem to roughly reflect in shape the prevailing wind directions, i.e. SW and SE, in atmospherically stable and neutral conditions that best characterize the tower site. Finally, the k-means land cover classification responds to the patchy typology of the terrain around the site by partitioning flux contributions with little differences among the fractions of fluxes themselves, either by class or cumulative.

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Fragmentation of 1 GeV/nucleon iron ions in thick targets relevant for space exploration