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A neural network device for on-line particle identification in cosmic ray experiments
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Nuclear Instruments and Methods in Physics Research A 524 (2004) 152–161
A neural network device for on-line particle identification in cosmic ray experiments R. Scrimaglioa,*, N. Finettia, L. D’Altorioa, E. Rantuccia, M. Rasoa, E. Segretoa, A. Tassonia, G.C. Cardarillib b
a Dipartimento di Fisica dell’Universita" di L’Aquila, Via Vetoio, I-67010 Coppito-L’Aquila, Italy Dipartimento di Ingegneria Elettronica dell’Universita" di Roma ‘‘Tor Vergata’’, I-00133 Roma, Italy
Received 31 July 2003; received in revised form 6 January 2004; accepted 14 January 2004
Abstract On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification. r 2004 Elsevier B.V. All rights reserved. PACS: 84.35.+i; 07.05.Mh; 95.55.Vj; 98.70.Sa Keywords: Neural networks; Artificial intelligence; Elementary particle detectors; Cosmic rays
1. Introduction Artificial neural networks have been employed in High Energy Physics experiments for track reconstruction, event classification and for accomplishing complex trigger logics. Actually, artificial *Corresponding author. E-mail address: [email protected] (R. Scrimaglio).
neural networks turn out to be quick in data processing, robust and fault tolerant and they are able to learn and to generalize from known tasks to unknown ones. Real time particle identification is one of the main goals of many experiments in space. We refer to experiments such as NINA (operating aboard of the Russian satellite Resurs01 n. 4 till 1999), NINA-2 (which operated from 2000 upto 2001 aboard of the ASI satellite MITA) [1,2] and PAMELA (scheduled to fly aboard of the
0168-9002/$ - see front matter r 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.nima.2004.01.052
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Russian satellite Resurs-Artika in 2004) [3–5] for solar, galactic and maybe extra-galactic charged particle flux measurements and AGILE (which is one of the small ASI missions) [6] for cosmic gamma ray measurements. In cosmic ray experiments event selection is usually performed off-line (on the whole bulk of recorded data) while a neural device could allow on-line detector signal processing. We point out that, for example, in rare event studies (like those concerning positrons [7,8] and antiprotons [9] in cosmic rays) real time background rejection is crucial for measurement optimization. Moreover a second-level trigger logics, developed ad hoc for each specific orbital region and implemented by means of neural devices, could improve measurements (in normal operating mode of the apparatus and maybe also in presence of noise) allowing observations in those orbital regions, such as Radiation Belts and South Atlantic Anomaly, where the particle rate raises so much that it could not be otherwise possible to acquire the whole event data set for both mass memory and telemetry limits. Finally, in order to carry out dose equivalent rate estimations aboard of spacecrafts it is necessary to classify incident particles on-line and this could be possible by using neural systems [10,11]. We point out that software neural networks have already been employed off-line for analysing data of balloonborne experiments [7–10,12] achieving better or comparable results than those obtained by applying the conventional methods of analysis. In this document, we report on the performances of a programmable neural device which was developed in VLSI technology with analog inputs and discrete weights. This prototype was realized with eight input neurons and eight output neurons interconnected by a matrix of 64 programmable synapses. Neurons and synapses were realized with CMOS Operational Transconductance Amplifier (OTA) structures. In this paper the main features of the chip are described and the performances of the neural device in accomplishing the XOR boolean function are reported. Moreover, results concerning the hardware implementation of a Multi-Layer Perceptron (MLP) apt to identify nuclei by processing the signals of a simple apparatus, consisting in three double-sided
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silicon strip detectors, are discussed. Each layer of the detector, once it is crossed by the incident particle, produces a signal proportional to the energy released into the traversed strip and the nuclear species is usually classified with the DE E method [13]. From the analysis of a set of simulated data, we show how it could be possible to process the detector signals on-line by using MLPs achieving results comparable with those obtained with the standard data analysis method. 2. The neural device prototype The programmable analog/digital neural device described in this document was developed in VLSI technology [14,15] and it was based on CMOS OTA circuits for implementing both synapses and neurons [16–19]. Actually, an OTA is a voltagecontrolled current source with very high (infinite) input and output impedances. The OTA gain can be modified by adjusting the gate voltage of a MOSFET and thus varying the transconductance of the input pair which determines the OTA output current for a given differential input voltage. Therefore, the effective OTA transconductance is used as the synaptic weight and the multiplier is implemented by properly varying the reference current of the OTA. This fully differential architecture has the inherent advantages of being not only almost immune to noise but also able to perform the automatic inversion required in order to build a neural network with only one amplifier per neuron [17]. The test chip consisted of eight input neurons and eight output neurons fully connected with a total number of 64 synapses. In order to optimize the silicon area consumption, activation sigmoidal functions was implemented directly on synapses limiting neuron operations to the sum of the synaptic currents [15,20]. In what follows, we will refer to neurons and synapses in terms of the circuits which were used to implement them: the Programmable Current-to-Voltage Converter (PIVC) for the neuron and the chain Sigmoidal Function Generator–Distributed Sigmoid Synapses (SFG–DSS) for the synapsis. In Fig. 1 the neural device prototype scheme is shown. The nominal dimensions of the chip are 3731:5 2868:8 mm2 :
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We point out that net inputs (lines DinAin#) can be both current or voltage signals: in the first case, the current signal is converted in a voltage signal by the corresponding PIVC, while in the second case,
the PIVC is bypassed and the voltage signal is injected directly into the SFG. The neural network input voltage VIN is processed by the SFG circuit which furnishes, on the output line, a voltage signal
Fig. 1. Block diagram of the neural device prototype. The elementary cells (PIVC, SFG and DSS) are shown.
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dependent from a quantity which varies in a sigmoidal way with the differential input voltage ðVIN VCM Þ: The SFG output voltage signal is sent to the input of the DSS cell which consists in four current mirrors and one inverter. Each DSS cell repeats the reference current In according with a programmed coefficient which establishes the corresponding synaptic weight value. Each SFG is connected with a row of eight DSS which correspond to eight different synapses leaving from the same neuron. The resolution of each synaptic weight was limited to five programming bits to reduce both complexity and power consumption of the device [14,15,20], but the gain of each PIVC can be settled within a set of eight different values adding a further degree of freedom in the weight value choice. 3. Experimental characteristics of the basic cells Measurements were performed on the neural device prototype in order to verify the agreement of the experimental characteristics of each elementary cell (PIVC included) with the corresponding SPECTRE simulations. For this reason the input voltage Vin was converted in a current signal through a 24-kO resistance before being injected into the PIVC. The experimental characteristics of a PIVC cell for the eight possible values of gain (from 0 to 7) are shown in Fig. 2: the input voltage Vin (a ramp with amplitude between 0 and 5 V) and the corresponding output voltage Vout are reported on the abscissa and the ordinate axis, respectively. In Fig. 3, the response of a SFG– DSS–PIVC chain is shown for all possible values of the DSS weight (from 15 to þ15) while the output PIVC gain was fixed to 7. The experimental characteristic of the SFG–DSS–PIVC chain resulted to be affected by an offset ðC20 mVÞ; due to the parasitic capacitance of the PAD, which was not taken into account in the circuit simulations. The signal fluctuations observed in Fig. 3 were due to quantization errors of the Digital-to-Analog Converter employed for signal acquisition. Measurements were performed over each DSS cell of the 8 8 matrix, varying the weight value from 15 to þ15; and they showed the uniform behavior and the almost constant extension of the sigmoidal function linear region (within 750 mV).
Fig. 2. Characteristics of the PIVC cell for the eight possible values of gain.
Fig. 3. Response of the SFG–DSS–PIVC chain for all possible values of the DSS weight. Vin and Vout are the voltage signals at the input of the SFG cell and at the output of the PIVC cell, respectively.
4. Hardware implementation of the XOR architecture In order to test the neural device performances, we chose a simple structure implementing the exclusive OR (XOR) boolean function. Although a boolean function did not represent the best
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DSS00
INPUT A DS S0
DS
S
PIVC0
40
DS
1
2
DSS42
THRESHOLD
SS
10
DS
S4
D
INPUT B
A B Q S2
DSS11
PIVC2
DS
OUTPUT Q
0 1 0 1
0 0 1 1
0 1 1 0
2 S3
1
PIVC1
Fig. 4. Multi-Layer Perceptron (MLP) implementing the XOR boolean function (the truth table is reported on the right). In this scheme neurons and synapses are quoted with the names of the elementary cells of the neural device which were employed. Table 1 Performances of the neural network in reproducing the XOR boolean function Path No.
Input A (V)
Input B (V)
Output Q (V)
Target (V)
1 2 3 4
0.000 0.000 5.000 5.000
0.000 5.000 0.000 5.000
0.082 4.970 4.926 0.078
0.000 5.000 5.000 0.000
choice for testing an analog device, the XOR implementation allowed us to verify the collective behavior of the elementary cells by using a low number of neurons and synapses. In Fig. 4, the MLP employed to implement the XOR boolean function is shown together with the corresponding truth table. In this figure, neurons and synapses are quoted with the names of the elementary cells of the neural chip (PIVC and DSS, respectively) employed for the hardware implementation of the XOR. The input voltage (A or B) can assume both the values of 5 V (logic state 1) or 0 V (logic state 0) according with one of the four configurations reported in the truth table of Fig. 4, while the threshold value was fixed to 5 V (logic state 1). In order to train the neural network, the Weight Perturbation technique was used since the standard Back-Propagation requires over 12 bits of weight resolution for successful training [21]. The net performances, obtained after a training over 3000 epochs (four train patterns per epoch) with chip in loop (i.e. the chip response is used in the weight optimization process), are reported in Table 1. It results that the XOR boolean function was implemented with good
approximation by means of this neural device since the target values were reached within a few cents of volt.
5. Hardware implementation of a MLP apt to identify nuclei In this work, the possible implementation of a feed-forward neural network (MLP) trained to identify particles on-line by means of two-dimensional position-sensitive Si detectors is taken into account. The radiation monitoring device consisted of three double-sided silicon strip detectors (which constitute the tracker) and two plastic scintillator planes (Fig. 5). The trigger was provided by the coincidence of the two signals coming from the scintillator planes when these were traversed by the incident particle. By using this kind of detectors, particle trajectory is reconstructed by means of the distribution of the hits (i.e. the fired silicon strips) in the tracking system and the nuclear species of the incident particle is usually assigned by the DE E method on the basis of the energy released by particle in the tracker silicon strips. In our case, the tracking system covered a sensitive area of 8 8 cm2 and its total height was about 4:5 cm (the spacing between two contiguous silicon planes was 2 cm). A double silicon plane consisted of two detectors formed by 32 strips each. A silicon strip was 2:4-mm wide, 8-cm long and 380-mm thick. In a double silicon plane, detectors were arranged with the strips of one detector perpendicular to those of the other detector allowing the particle track impact point (x and y coordinate) reconstruction.
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Fig. 5. Radiation monitoring device consisting of three doublesided silicon strip detectors and two plastic scintillator planes.
We point out that the DE E method did not provide, in this case, a measurement of the total energy of the incident particle because the nucleus was not stopped in the active medium of the radiation monitoring device in exam and this meant that the kinetic energy of the incident particle was not known. In order to train and to test the neural networks, we took into account particles with straight tracks which impinged on the detector planes in the perpendicular direction (vertical tracks). Moreover, we analyzed the detector response to light nuclei (from H to B) by examining the distribution of the energy lost by vertical single tracks which gave a signal in both the scintillator planes, while particles which had hadronic interaction in the detector with generation of secondaries were removed from the sample of data by rejecting multiple-tracks (trigger condition). We simulated events with kinetic energy upto 200 MeV=n and with flat energy spectra. In such a way we had about the same number of events in each energy bin for all the nuclear species in exam and this assured to design a neural network which was able to identify particles in the whole energy range in exam without being specialized in any specific energy bin. For simulations we used the toolkit Geant 3.21 of CERN [22] and we simulated a set
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of 3000 events for each nuclear species (‘‘test data set’’). Moreover, in order to train the MLPs, an independent set of 2000 events for nuclear species was simulated (‘‘training data set’’). We point out that we simulated the same number of events for each kind of nuclei in order to construct a neural device which was able to identify nuclei from H to B and which was not specialized in the identification of any specific nuclear species. The distribution of the total energy Etot released into the tracking system versus the difference DE ¼ ðE1 E3 ), between the energy lost in the first and in the last of the three double-sided silicon planes, is shown in Fig. 6 for the set of 15 000 simulated events (‘‘test data set’’). We point out that the difference E1 E3 is greater, apart from statistical fluctuations, than the difference E1 E2 (between the energy lost in the first and in the second plane) or E2 E3 (between the energy lost in the second and in the third plane), therefore the use of E1 E3 in the DE E method allows a more suitable separation among the different nuclear species than the use of E1 E2 or E2 E3 because of the lower percentage fluctuations. As shown in Fig. 6, discrimination among the different species of nuclei was possible with high efficiency and very low contamination only when the difference DE ¼ ðE1 E3 ) resulted to be DEp 3 MeV while for events with DE > 3 MeV identification was possible (with lower efficiency and higher contamination) on the basis of the total energy Etot released into the tracking system (Fig. 7). We point out that DEp 3 MeV corresponded to low-energy particles, for example for 4 He the kinetic energy of the incident nucleus resulted to be less than 45 MeV=n for DEp 3 MeV: In order to identify each particle which crossed the apparatus, we applied simple cuts on the total energy lost in the detector. These cuts was optimized by means of a Landau fit on the event energy loss distribution. The results obtained on the set of simulated events (‘‘test data set’’) by employing the standard DE E method of analysis are reported in Table 2, where they are summarized in the form of ‘‘confusion matrix’’. Each row of the confusion matrix corresponds to events of a given nuclear species which were
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Fig. 6. Distribution of the total energy Etot released into the tracking system versus the difference DE ¼ ðE1 E3 ), between the energy lost in the first and in the last of the three double-sided silicon planes, for a set of events consisting of different species of nuclei (from H to B) with kinetic energy upto 200 MeV=n: On the bottom, only events with DEp 3 MeV are shown.
classified as H, He, Li, Be or B so that the diagonal elements of this matrix give the percentages of events which were correctly identified by the classifying procedure. From the analysis of the response of the net on the ‘‘test data set’’, it was found that the neural device could process the detector signals on-line giving results comparable with those obtained with the ‘‘classical’’ classifying procedure when a multidimensional analysis of events based on an MLP with two layers connection was considered. At first, an MLP (6-6-5) was implemented by means of the software package JETNET [23] (Fig. 8). In
this configuration each MLP input signal corresponded to the energy lost by particle in a given Si plane of the detector (i.e. the pulse height response of a specific silicon detector at a single-particle track detected) while the output neuron with the highest value defined the event class (ion atomic number Z). The input/output signals were normalized in the ½0; 1 interval and the activation function (transfer function) of neurons was the sigmoid function gðx; yÞ ¼
1 1 þ expðbðx yÞÞ
ð1Þ
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E 1x
W ij
(1)
W jk
159
(2)
O1 E 1y O2 E 2x O3 E 2y
O4
E 3x O5 E 3y Fig. 8. Multi-Layer Perceptron with 6 input neurons, 6 neurons in the hidden layer and 5 output neurons (MLP 6-6-5).
Fig. 7. Distribution of the total energy Etot released into the tracking system for events (nuclei from H to B) with kinetic energy upto 200 MeV=n: Events were selected by applying the cut DE > 3 MeV: Table 2 Confusion matrix for different species of nuclei (from H to B) with kinetic energy upto 200 MeV=n identified by means of the standard DE E method of analysis (a set of 3000 events was used for each different species of nuclei) Confusion matrix Nucl. species
id. p (%)
id. He (%)
id. Li (%)
id. Be (%)
id. B (%)
p 4 He 7 Li 9 Be 11 B
93.67 o0.03 o0.03 o0.03 o0.03
6.30 77.30 o0.03 o0.03 o0.03
0.03 19.87 69.63 0.77 o0.03
o0.03 2.83 23.10 68.13 2.07
o0.03 o0.03 7.27 31.10 97.93
where y and b were the neuron threshold and gain factor, respectively. The synaptic matrix W consisted of the weights wð1Þ ij ; related to the synapses connecting the input neurons to the neurons of the hidden layer, and the weights wð2Þ jk of synapses connecting the hidden neurons to the output neurons of the MLP. The neural network was trained by applying the standard Back-Propagation method and by exposing the network to a data set of examples (‘‘training data set’’). The weights were updated according with the ‘‘Delta Rule’’ in order to minimize the mean-squared
error function of the classification system [24,25]. After each training epoch, the MLP classification capability was tested by exposing the network to an independent data set (‘‘test data set’’) and the learning session was stopped when the error function, evaluated on the ‘‘test data set’’, reached its lowest value. We point out that the standard Back-Propagation results to be an efficient learning algorithm for software implemented neural networks because in this case the MLP weights are almost continuous. The results obtained on the ‘‘test data set’’ after 20 000 training epochs of the neural classifier are summarized in the confusion matrix of Table 3. In order to implement on the VLSI chip a neural network apt to identify nuclei an MLP with two layers connection was considered. From simulations an MLP (1-7-5) architecture resulted to be appropriate for this purpose (Fig. 9). In this configuration, the MLP input signal corresponded to the common logarithm of the total energy lost by the incident particle in the radiation monitoring device (i.e. of the sum of the pulse height responses of the silicon detectors at a single-particle track detected), while the output neuron with the highest value defined the event class (ion atomic number Z). The input/output signals were normalized in the [2,3] V interval and the activation function (transfer function) of neurons was, in first approximation, a sigmoid function. We point out that the standard Back-Propagation did not result to be an efficient learning algorithm for this hardware implemented neural network because in this case
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Table 3 Confusion matrix for different species of nuclei (from H to B) with kinetic energy upto 200 MeV=n identified by means of an MLP (6-6-5)
Table 4 Confusion matrix for different species of nuclei (from H to B) with kinetic energy upto 200 MeV=n identified by means of an MLP (1-7-5) implemented on the VLSI chip
Confusion matrix
Confusion matrix
Nucl. species
id. p (%)
id. He (%)
id. Li (%)
id. Be (%)
id. B (%)
Nucl. species
id. p (%)
id. He (%)
id. Li (%)
id. Be (%)
id. B (%)
p 4 He 7 Li 9 Be 11 B
94.28 o0.03 o0.03 o0.03 o0.03
5.69 80.14 0.53 o0.03 o0.03
0.03 16.25 74.20 4.95 o0.03
o0.03 3.60 23.65 60.97 2.63
o0.03 o0.03 1.62 34.08 97.37
p 4 He 7 Li 9 Be 11 B
97.59 o0.03 o0.03 o0.03 o0.03
2.41 78.46 o0.03 o0.03 o0.03
o0.03 17.43 66.70 o0.03 o0.03
o0.03 3.66 22.65 60.92 15.45
o0.03 0.45 10.65 39.08 84.55
The same ‘‘test data set’’ employed to estimate the performances of the ‘‘standard’’ classifying procedure (Table 2) was exposed to the neural classifier after 20 000 training epochs.
W ij
(1)
W jk
(2)
O1 O2 E tot
O3 O4 O5
Fig. 9. Multi-Layer Perceptron with 1 input neuron, 7 neurons in the hidden layer and 5 output neurons (MLP 1-7-5).
the MLP weights were discrete (5 bits) and the algorithm of ‘‘Perturbation of Weights’’ [21] was employed for this purpose. This algorithm implies the initial assignation of random weight values on which the good conclusion of the network training depends. For this reason it was appropriate to minimize the number of connections in the network and we chose to implement an MLP with only one input line which used the information on the total energy lost by particles in the radiation monitoring device. The synaptic matrix of the MLP was trained by exposing the network to the ‘‘training data set’’ and updating the weights in order to minimize the mean-squared error function. The learning session was stopped when the
The same ‘‘test data set’’ employed to estimate the performances of the ‘‘standard’’ classifying procedure (Table 2) was exposed to the neural classifier.
error function, evaluated on the ‘‘test data set’’, reached its lowest value. The results obtained on the ‘‘test data set’’, after the train of the neural classifier, are summarized in the confusion matrix of Table 4. We point out that the results presented in Tables 2–4 do not depend on the energy spectra and on the relative abundances of incident particles because we simulated a set of 3000 events per nuclear species with flat energy spectra. Moreover, the training and stopping procedures employed in this work are unbiased general criteria to build up a classification system by means of neural networks and by following these procedures it was not possible to control the identification capability of the neural network for each nuclear species in exam since the global mean-squared error function was taken into account. Finally, the differences found in particle discrimination with the two MLPs (Tables 3 and 4) can be ascribed to differences in the structures of the two networks and to the different learning algorithms employed.
6. Conclusions The results of the analysis performed on simulated samples of nuclei impinging on a radiation monitoring device (consisting of three double-sided silicon strip detectors and two plastic scintillator planes) by using neural network
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architectures employing Multi-Layer Perceptrons were in agreement with those of the ‘‘standard’’ classifying procedure (DE E method). A MultiLayer Perceptron with two layers connection MLP (1-7-5), implemented on the VLSI chip, resulted to be apt to identify nuclei by using as input signal the common logarithm of the total energy lost by particle in the radiation monitoring device, while the output neuron with the highest value defined the event class (ion atomic number Z). This configuration allowed particle identification with efficiency and contamination comparable with those obtained by using an MLP (6-6-5), implemented by means of the software package JETNET, which used as input signals the particle energy losses in the six Si planes of the detector. The differences found in particle discrimination with the two MLPs can be ascribed to differences in the structures of the two networks and to the different learning algorithms employed. From this work it is found that cosmic-ray discrimination capabilities of silicon nuclear telescopes could be improved by using an MLP implemented on the VLSI chip in order to perform real time particle classification.
Acknowledgements The authors would like to thank the WiZard Group and INFN, involved in the development of the radiation monitoring device, for offering support in the analysis of the particle detector response. Moreover, the authors would like to acknowledge the support of ASI (‘‘Agenzia Spaziale Italiana’’) for funding this research.
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Nuclear Instruments and Methods in Physics Research A 524 (2004) 152–161
A neural network device for on-line particle identification in cosmic ray experiments R. Scrimaglioa,*, N. Finettia, L. D’Altorioa, E. Rantuccia, M. Rasoa, E. Segretoa, A. Tassonia, G.C. Cardarillib b
a Dipartimento di Fisica dell’Universita" di L’Aquila, Via Vetoio, I-67010 Coppito-L’Aquila, Italy Dipartimento di Ingegneria Elettronica dell’Universita" di Roma ‘‘Tor Vergata’’, I-00133 Roma, Italy
Received 31 July 2003; received in revised form 6 January 2004; accepted 14 January 2004
Abstract On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification. r 2004 Elsevier B.V. All rights reserved. PACS: 84.35.+i; 07.05.Mh; 95.55.Vj; 98.70.Sa Keywords: Neural networks; Artificial intelligence; Elementary particle detectors; Cosmic rays
1. Introduction Artificial neural networks have been employed in High Energy Physics experiments for track reconstruction, event classification and for accomplishing complex trigger logics. Actually, artificial *Corresponding author. E-mail address: [email protected] (R. Scrimaglio).
neural networks turn out to be quick in data processing, robust and fault tolerant and they are able to learn and to generalize from known tasks to unknown ones. Real time particle identification is one of the main goals of many experiments in space. We refer to experiments such as NINA (operating aboard of the Russian satellite Resurs01 n. 4 till 1999), NINA-2 (which operated from 2000 upto 2001 aboard of the ASI satellite MITA) [1,2] and PAMELA (scheduled to fly aboard of the
0168-9002/$ - see front matter r 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.nima.2004.01.052
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Russian satellite Resurs-Artika in 2004) [3–5] for solar, galactic and maybe extra-galactic charged particle flux measurements and AGILE (which is one of the small ASI missions) [6] for cosmic gamma ray measurements. In cosmic ray experiments event selection is usually performed off-line (on the whole bulk of recorded data) while a neural device could allow on-line detector signal processing. We point out that, for example, in rare event studies (like those concerning positrons [7,8] and antiprotons [9] in cosmic rays) real time background rejection is crucial for measurement optimization. Moreover a second-level trigger logics, developed ad hoc for each specific orbital region and implemented by means of neural devices, could improve measurements (in normal operating mode of the apparatus and maybe also in presence of noise) allowing observations in those orbital regions, such as Radiation Belts and South Atlantic Anomaly, where the particle rate raises so much that it could not be otherwise possible to acquire the whole event data set for both mass memory and telemetry limits. Finally, in order to carry out dose equivalent rate estimations aboard of spacecrafts it is necessary to classify incident particles on-line and this could be possible by using neural systems [10,11]. We point out that software neural networks have already been employed off-line for analysing data of balloonborne experiments [7–10,12] achieving better or comparable results than those obtained by applying the conventional methods of analysis. In this document, we report on the performances of a programmable neural device which was developed in VLSI technology with analog inputs and discrete weights. This prototype was realized with eight input neurons and eight output neurons interconnected by a matrix of 64 programmable synapses. Neurons and synapses were realized with CMOS Operational Transconductance Amplifier (OTA) structures. In this paper the main features of the chip are described and the performances of the neural device in accomplishing the XOR boolean function are reported. Moreover, results concerning the hardware implementation of a Multi-Layer Perceptron (MLP) apt to identify nuclei by processing the signals of a simple apparatus, consisting in three double-sided
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silicon strip detectors, are discussed. Each layer of the detector, once it is crossed by the incident particle, produces a signal proportional to the energy released into the traversed strip and the nuclear species is usually classified with the DE E method [13]. From the analysis of a set of simulated data, we show how it could be possible to process the detector signals on-line by using MLPs achieving results comparable with those obtained with the standard data analysis method. 2. The neural device prototype The programmable analog/digital neural device described in this document was developed in VLSI technology [14,15] and it was based on CMOS OTA circuits for implementing both synapses and neurons [16–19]. Actually, an OTA is a voltagecontrolled current source with very high (infinite) input and output impedances. The OTA gain can be modified by adjusting the gate voltage of a MOSFET and thus varying the transconductance of the input pair which determines the OTA output current for a given differential input voltage. Therefore, the effective OTA transconductance is used as the synaptic weight and the multiplier is implemented by properly varying the reference current of the OTA. This fully differential architecture has the inherent advantages of being not only almost immune to noise but also able to perform the automatic inversion required in order to build a neural network with only one amplifier per neuron [17]. The test chip consisted of eight input neurons and eight output neurons fully connected with a total number of 64 synapses. In order to optimize the silicon area consumption, activation sigmoidal functions was implemented directly on synapses limiting neuron operations to the sum of the synaptic currents [15,20]. In what follows, we will refer to neurons and synapses in terms of the circuits which were used to implement them: the Programmable Current-to-Voltage Converter (PIVC) for the neuron and the chain Sigmoidal Function Generator–Distributed Sigmoid Synapses (SFG–DSS) for the synapsis. In Fig. 1 the neural device prototype scheme is shown. The nominal dimensions of the chip are 3731:5 2868:8 mm2 :
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We point out that net inputs (lines DinAin#) can be both current or voltage signals: in the first case, the current signal is converted in a voltage signal by the corresponding PIVC, while in the second case,
the PIVC is bypassed and the voltage signal is injected directly into the SFG. The neural network input voltage VIN is processed by the SFG circuit which furnishes, on the output line, a voltage signal
Fig. 1. Block diagram of the neural device prototype. The elementary cells (PIVC, SFG and DSS) are shown.
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dependent from a quantity which varies in a sigmoidal way with the differential input voltage ðVIN VCM Þ: The SFG output voltage signal is sent to the input of the DSS cell which consists in four current mirrors and one inverter. Each DSS cell repeats the reference current In according with a programmed coefficient which establishes the corresponding synaptic weight value. Each SFG is connected with a row of eight DSS which correspond to eight different synapses leaving from the same neuron. The resolution of each synaptic weight was limited to five programming bits to reduce both complexity and power consumption of the device [14,15,20], but the gain of each PIVC can be settled within a set of eight different values adding a further degree of freedom in the weight value choice. 3. Experimental characteristics of the basic cells Measurements were performed on the neural device prototype in order to verify the agreement of the experimental characteristics of each elementary cell (PIVC included) with the corresponding SPECTRE simulations. For this reason the input voltage Vin was converted in a current signal through a 24-kO resistance before being injected into the PIVC. The experimental characteristics of a PIVC cell for the eight possible values of gain (from 0 to 7) are shown in Fig. 2: the input voltage Vin (a ramp with amplitude between 0 and 5 V) and the corresponding output voltage Vout are reported on the abscissa and the ordinate axis, respectively. In Fig. 3, the response of a SFG– DSS–PIVC chain is shown for all possible values of the DSS weight (from 15 to þ15) while the output PIVC gain was fixed to 7. The experimental characteristic of the SFG–DSS–PIVC chain resulted to be affected by an offset ðC20 mVÞ; due to the parasitic capacitance of the PAD, which was not taken into account in the circuit simulations. The signal fluctuations observed in Fig. 3 were due to quantization errors of the Digital-to-Analog Converter employed for signal acquisition. Measurements were performed over each DSS cell of the 8 8 matrix, varying the weight value from 15 to þ15; and they showed the uniform behavior and the almost constant extension of the sigmoidal function linear region (within 750 mV).
Fig. 2. Characteristics of the PIVC cell for the eight possible values of gain.
Fig. 3. Response of the SFG–DSS–PIVC chain for all possible values of the DSS weight. Vin and Vout are the voltage signals at the input of the SFG cell and at the output of the PIVC cell, respectively.
4. Hardware implementation of the XOR architecture In order to test the neural device performances, we chose a simple structure implementing the exclusive OR (XOR) boolean function. Although a boolean function did not represent the best
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DSS00
INPUT A DS S0
DS
S
PIVC0
40
DS
1
2
DSS42
THRESHOLD
SS
10
DS
S4
D
INPUT B
A B Q S2
DSS11
PIVC2
DS
OUTPUT Q
0 1 0 1
0 0 1 1
0 1 1 0
2 S3
1
PIVC1
Fig. 4. Multi-Layer Perceptron (MLP) implementing the XOR boolean function (the truth table is reported on the right). In this scheme neurons and synapses are quoted with the names of the elementary cells of the neural device which were employed. Table 1 Performances of the neural network in reproducing the XOR boolean function Path No.
Input A (V)
Input B (V)
Output Q (V)
Target (V)
1 2 3 4
0.000 0.000 5.000 5.000
0.000 5.000 0.000 5.000
0.082 4.970 4.926 0.078
0.000 5.000 5.000 0.000
choice for testing an analog device, the XOR implementation allowed us to verify the collective behavior of the elementary cells by using a low number of neurons and synapses. In Fig. 4, the MLP employed to implement the XOR boolean function is shown together with the corresponding truth table. In this figure, neurons and synapses are quoted with the names of the elementary cells of the neural chip (PIVC and DSS, respectively) employed for the hardware implementation of the XOR. The input voltage (A or B) can assume both the values of 5 V (logic state 1) or 0 V (logic state 0) according with one of the four configurations reported in the truth table of Fig. 4, while the threshold value was fixed to 5 V (logic state 1). In order to train the neural network, the Weight Perturbation technique was used since the standard Back-Propagation requires over 12 bits of weight resolution for successful training [21]. The net performances, obtained after a training over 3000 epochs (four train patterns per epoch) with chip in loop (i.e. the chip response is used in the weight optimization process), are reported in Table 1. It results that the XOR boolean function was implemented with good
approximation by means of this neural device since the target values were reached within a few cents of volt.
5. Hardware implementation of a MLP apt to identify nuclei In this work, the possible implementation of a feed-forward neural network (MLP) trained to identify particles on-line by means of two-dimensional position-sensitive Si detectors is taken into account. The radiation monitoring device consisted of three double-sided silicon strip detectors (which constitute the tracker) and two plastic scintillator planes (Fig. 5). The trigger was provided by the coincidence of the two signals coming from the scintillator planes when these were traversed by the incident particle. By using this kind of detectors, particle trajectory is reconstructed by means of the distribution of the hits (i.e. the fired silicon strips) in the tracking system and the nuclear species of the incident particle is usually assigned by the DE E method on the basis of the energy released by particle in the tracker silicon strips. In our case, the tracking system covered a sensitive area of 8 8 cm2 and its total height was about 4:5 cm (the spacing between two contiguous silicon planes was 2 cm). A double silicon plane consisted of two detectors formed by 32 strips each. A silicon strip was 2:4-mm wide, 8-cm long and 380-mm thick. In a double silicon plane, detectors were arranged with the strips of one detector perpendicular to those of the other detector allowing the particle track impact point (x and y coordinate) reconstruction.
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Fig. 5. Radiation monitoring device consisting of three doublesided silicon strip detectors and two plastic scintillator planes.
We point out that the DE E method did not provide, in this case, a measurement of the total energy of the incident particle because the nucleus was not stopped in the active medium of the radiation monitoring device in exam and this meant that the kinetic energy of the incident particle was not known. In order to train and to test the neural networks, we took into account particles with straight tracks which impinged on the detector planes in the perpendicular direction (vertical tracks). Moreover, we analyzed the detector response to light nuclei (from H to B) by examining the distribution of the energy lost by vertical single tracks which gave a signal in both the scintillator planes, while particles which had hadronic interaction in the detector with generation of secondaries were removed from the sample of data by rejecting multiple-tracks (trigger condition). We simulated events with kinetic energy upto 200 MeV=n and with flat energy spectra. In such a way we had about the same number of events in each energy bin for all the nuclear species in exam and this assured to design a neural network which was able to identify particles in the whole energy range in exam without being specialized in any specific energy bin. For simulations we used the toolkit Geant 3.21 of CERN [22] and we simulated a set
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of 3000 events for each nuclear species (‘‘test data set’’). Moreover, in order to train the MLPs, an independent set of 2000 events for nuclear species was simulated (‘‘training data set’’). We point out that we simulated the same number of events for each kind of nuclei in order to construct a neural device which was able to identify nuclei from H to B and which was not specialized in the identification of any specific nuclear species. The distribution of the total energy Etot released into the tracking system versus the difference DE ¼ ðE1 E3 ), between the energy lost in the first and in the last of the three double-sided silicon planes, is shown in Fig. 6 for the set of 15 000 simulated events (‘‘test data set’’). We point out that the difference E1 E3 is greater, apart from statistical fluctuations, than the difference E1 E2 (between the energy lost in the first and in the second plane) or E2 E3 (between the energy lost in the second and in the third plane), therefore the use of E1 E3 in the DE E method allows a more suitable separation among the different nuclear species than the use of E1 E2 or E2 E3 because of the lower percentage fluctuations. As shown in Fig. 6, discrimination among the different species of nuclei was possible with high efficiency and very low contamination only when the difference DE ¼ ðE1 E3 ) resulted to be DEp 3 MeV while for events with DE > 3 MeV identification was possible (with lower efficiency and higher contamination) on the basis of the total energy Etot released into the tracking system (Fig. 7). We point out that DEp 3 MeV corresponded to low-energy particles, for example for 4 He the kinetic energy of the incident nucleus resulted to be less than 45 MeV=n for DEp 3 MeV: In order to identify each particle which crossed the apparatus, we applied simple cuts on the total energy lost in the detector. These cuts was optimized by means of a Landau fit on the event energy loss distribution. The results obtained on the set of simulated events (‘‘test data set’’) by employing the standard DE E method of analysis are reported in Table 2, where they are summarized in the form of ‘‘confusion matrix’’. Each row of the confusion matrix corresponds to events of a given nuclear species which were
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Fig. 6. Distribution of the total energy Etot released into the tracking system versus the difference DE ¼ ðE1 E3 ), between the energy lost in the first and in the last of the three double-sided silicon planes, for a set of events consisting of different species of nuclei (from H to B) with kinetic energy upto 200 MeV=n: On the bottom, only events with DEp 3 MeV are shown.
classified as H, He, Li, Be or B so that the diagonal elements of this matrix give the percentages of events which were correctly identified by the classifying procedure. From the analysis of the response of the net on the ‘‘test data set’’, it was found that the neural device could process the detector signals on-line giving results comparable with those obtained with the ‘‘classical’’ classifying procedure when a multidimensional analysis of events based on an MLP with two layers connection was considered. At first, an MLP (6-6-5) was implemented by means of the software package JETNET [23] (Fig. 8). In
this configuration each MLP input signal corresponded to the energy lost by particle in a given Si plane of the detector (i.e. the pulse height response of a specific silicon detector at a single-particle track detected) while the output neuron with the highest value defined the event class (ion atomic number Z). The input/output signals were normalized in the ½0; 1 interval and the activation function (transfer function) of neurons was the sigmoid function gðx; yÞ ¼
1 1 þ expðbðx yÞÞ
ð1Þ
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E 1x
W ij
(1)
W jk
159
(2)
O1 E 1y O2 E 2x O3 E 2y
O4
E 3x O5 E 3y Fig. 8. Multi-Layer Perceptron with 6 input neurons, 6 neurons in the hidden layer and 5 output neurons (MLP 6-6-5).
Fig. 7. Distribution of the total energy Etot released into the tracking system for events (nuclei from H to B) with kinetic energy upto 200 MeV=n: Events were selected by applying the cut DE > 3 MeV: Table 2 Confusion matrix for different species of nuclei (from H to B) with kinetic energy upto 200 MeV=n identified by means of the standard DE E method of analysis (a set of 3000 events was used for each different species of nuclei) Confusion matrix Nucl. species
id. p (%)
id. He (%)
id. Li (%)
id. Be (%)
id. B (%)
p 4 He 7 Li 9 Be 11 B
93.67 o0.03 o0.03 o0.03 o0.03
6.30 77.30 o0.03 o0.03 o0.03
0.03 19.87 69.63 0.77 o0.03
o0.03 2.83 23.10 68.13 2.07
o0.03 o0.03 7.27 31.10 97.93
where y and b were the neuron threshold and gain factor, respectively. The synaptic matrix W consisted of the weights wð1Þ ij ; related to the synapses connecting the input neurons to the neurons of the hidden layer, and the weights wð2Þ jk of synapses connecting the hidden neurons to the output neurons of the MLP. The neural network was trained by applying the standard Back-Propagation method and by exposing the network to a data set of examples (‘‘training data set’’). The weights were updated according with the ‘‘Delta Rule’’ in order to minimize the mean-squared
error function of the classification system [24,25]. After each training epoch, the MLP classification capability was tested by exposing the network to an independent data set (‘‘test data set’’) and the learning session was stopped when the error function, evaluated on the ‘‘test data set’’, reached its lowest value. We point out that the standard Back-Propagation results to be an efficient learning algorithm for software implemented neural networks because in this case the MLP weights are almost continuous. The results obtained on the ‘‘test data set’’ after 20 000 training epochs of the neural classifier are summarized in the confusion matrix of Table 3. In order to implement on the VLSI chip a neural network apt to identify nuclei an MLP with two layers connection was considered. From simulations an MLP (1-7-5) architecture resulted to be appropriate for this purpose (Fig. 9). In this configuration, the MLP input signal corresponded to the common logarithm of the total energy lost by the incident particle in the radiation monitoring device (i.e. of the sum of the pulse height responses of the silicon detectors at a single-particle track detected), while the output neuron with the highest value defined the event class (ion atomic number Z). The input/output signals were normalized in the [2,3] V interval and the activation function (transfer function) of neurons was, in first approximation, a sigmoid function. We point out that the standard Back-Propagation did not result to be an efficient learning algorithm for this hardware implemented neural network because in this case
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Table 3 Confusion matrix for different species of nuclei (from H to B) with kinetic energy upto 200 MeV=n identified by means of an MLP (6-6-5)
Table 4 Confusion matrix for different species of nuclei (from H to B) with kinetic energy upto 200 MeV=n identified by means of an MLP (1-7-5) implemented on the VLSI chip
Confusion matrix
Confusion matrix
Nucl. species
id. p (%)
id. He (%)
id. Li (%)
id. Be (%)
id. B (%)
Nucl. species
id. p (%)
id. He (%)
id. Li (%)
id. Be (%)
id. B (%)
p 4 He 7 Li 9 Be 11 B
94.28 o0.03 o0.03 o0.03 o0.03
5.69 80.14 0.53 o0.03 o0.03
0.03 16.25 74.20 4.95 o0.03
o0.03 3.60 23.65 60.97 2.63
o0.03 o0.03 1.62 34.08 97.37
p 4 He 7 Li 9 Be 11 B
97.59 o0.03 o0.03 o0.03 o0.03
2.41 78.46 o0.03 o0.03 o0.03
o0.03 17.43 66.70 o0.03 o0.03
o0.03 3.66 22.65 60.92 15.45
o0.03 0.45 10.65 39.08 84.55
The same ‘‘test data set’’ employed to estimate the performances of the ‘‘standard’’ classifying procedure (Table 2) was exposed to the neural classifier after 20 000 training epochs.
W ij
(1)
W jk
(2)
O1 O2 E tot
O3 O4 O5
Fig. 9. Multi-Layer Perceptron with 1 input neuron, 7 neurons in the hidden layer and 5 output neurons (MLP 1-7-5).
the MLP weights were discrete (5 bits) and the algorithm of ‘‘Perturbation of Weights’’ [21] was employed for this purpose. This algorithm implies the initial assignation of random weight values on which the good conclusion of the network training depends. For this reason it was appropriate to minimize the number of connections in the network and we chose to implement an MLP with only one input line which used the information on the total energy lost by particles in the radiation monitoring device. The synaptic matrix of the MLP was trained by exposing the network to the ‘‘training data set’’ and updating the weights in order to minimize the mean-squared error function. The learning session was stopped when the
The same ‘‘test data set’’ employed to estimate the performances of the ‘‘standard’’ classifying procedure (Table 2) was exposed to the neural classifier.
error function, evaluated on the ‘‘test data set’’, reached its lowest value. The results obtained on the ‘‘test data set’’, after the train of the neural classifier, are summarized in the confusion matrix of Table 4. We point out that the results presented in Tables 2–4 do not depend on the energy spectra and on the relative abundances of incident particles because we simulated a set of 3000 events per nuclear species with flat energy spectra. Moreover, the training and stopping procedures employed in this work are unbiased general criteria to build up a classification system by means of neural networks and by following these procedures it was not possible to control the identification capability of the neural network for each nuclear species in exam since the global mean-squared error function was taken into account. Finally, the differences found in particle discrimination with the two MLPs (Tables 3 and 4) can be ascribed to differences in the structures of the two networks and to the different learning algorithms employed.
6. Conclusions The results of the analysis performed on simulated samples of nuclei impinging on a radiation monitoring device (consisting of three double-sided silicon strip detectors and two plastic scintillator planes) by using neural network
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architectures employing Multi-Layer Perceptrons were in agreement with those of the ‘‘standard’’ classifying procedure (DE E method). A MultiLayer Perceptron with two layers connection MLP (1-7-5), implemented on the VLSI chip, resulted to be apt to identify nuclei by using as input signal the common logarithm of the total energy lost by particle in the radiation monitoring device, while the output neuron with the highest value defined the event class (ion atomic number Z). This configuration allowed particle identification with efficiency and contamination comparable with those obtained by using an MLP (6-6-5), implemented by means of the software package JETNET, which used as input signals the particle energy losses in the six Si planes of the detector. The differences found in particle discrimination with the two MLPs can be ascribed to differences in the structures of the two networks and to the different learning algorithms employed. From this work it is found that cosmic-ray discrimination capabilities of silicon nuclear telescopes could be improved by using an MLP implemented on the VLSI chip in order to perform real time particle classification.
Acknowledgements The authors would like to thank the WiZard Group and INFN, involved in the development of the radiation monitoring device, for offering support in the analysis of the particle detector response. Moreover, the authors would like to acknowledge the support of ASI (‘‘Agenzia Spaziale Italiana’’) for funding this research.
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